WO2018028443A1 - Data processing method, device and system - Google Patents

Abstract

Provided are a data processing method, device and system, the method comprising: a query terminal receiving a query request of a user, wherein the query request comprises search keywords; acquiring dimension keywords, index keywords and time granularity keywords among the search keywords, and first data corresponding to dimension features matching the dimension keywords, second data corresponding to index features matching the index keywords, and third data corresponding to time granularity features matching the time granularity keywords; and the query terminal determining target data fed back to the user according to the first data, the second data and the third data. In the present embodiment, the user only need to input the search keywords once without traversing each data outlet to search data, and a search engine database can then find data related to the search keywords in all the data outlets, thereby improving the efficiency of searching data.

Description

Data processing method, device and system

The present application claims the priority of the Chinese Patent Application Serial No. PCT Application No.

Technical field

The present disclosure relates to Internet technologies, and in particular, to a data processing method, device, and system.

Background technique

With the rapid development of the Internet, data has exploded. Currently, all companies with big data assets have a large amount of data stored. The above-mentioned companies generally use four kinds of data export, which refers to a storage space in which data is stored or a software application capable of generating data, and the storage space or software application can provide a data source for the database and present the massive data stored therein. For all employees of the company, the four data exports are data application exports (such as Alibaba's Taobao business and Baidu's Baidu index, etc.), report exports (such as the company's salary report), and knowledge base platform exports (such as Baidu's Baidu Encyclopedia and cluster physical table exports (such as corporate users' personal information).

For the non-technical employees of the above companies, it is generally necessary to find the above four data exports in order to obtain the required data. For example, a non-technical employee of the company has the need to obtain the “amount of transaction for a home-made product”, so the non-technical personnel need to find the company's data application export, report export, knowledge base platform export, and cluster physical table. Export until you find the company's "the amount of the day's home improvement products."

Since in the actual application, the data amount of the data presented by each of the above data outlets is large, the non-technical staff searches for each data export in turn, which inevitably results in inefficient query data.

Summary of the invention

The present disclosure provides a data processing method, device and system to improve the efficiency of finding data.

In one aspect, the present disclosure provides a data processing system including: a query terminal and a search engine database;

The query terminal is configured to receive a query request of a user, where the query request includes a search keyword; the query terminal acquires a dimension keyword, an index keyword, and a time granularity keyword in the search keyword, and Transmitting the dimension keyword, the index keyword, and the time granularity keyword to the search engine database;

The search engine database pre-stores data in the data exit, and characteristic information of the data, The feature information includes at least one of the following: a dimensional feature, an index feature, and a time granularity feature;

The search engine database is configured to acquire first data corresponding to the dimension feature matching the dimension keyword, second data corresponding to the index feature matched by the index keyword, and match the time granularity keyword Corresponding third data corresponding to the time granularity feature, and sending the first data, the second data, and the third data to the query terminal;

The query terminal is further configured to determine, according to the first data, the second data, and the third data, target data that is fed back to the user, and display the target data to the user.

In another aspect, the present disclosure provides a data processing method, including:

The querying terminal receives a query request of the user, where the query request includes a search keyword;

The query terminal acquires a dimension keyword, an index keyword, and a time granularity keyword in the search keyword;

Transmitting, by the query terminal, the dimension keyword, the index keyword, and the time granularity keyword to a search engine database, so that the search engine database obtains a dimension feature that matches the dimension keyword. The first data, the second data corresponding to the indicator feature matched by the indicator keyword, and the third data corresponding to the time granularity feature matched by the time granularity keyword, the search engine database is pre-stored in the data exit Data, and characteristic information of the data, the feature information includes at least one of the following: a dimensional feature, an index feature, and a time granularity feature;

Receiving, by the query terminal, the first data, the second data, and the third data that are sent by the search engine database;

The querying terminal determines target data fed back to the user according to the first data, the second data, and the third data.

In another aspect, the present disclosure provides a data processing method, including:

The querying terminal receives a query request of the user, where the query request includes a search keyword;

The query terminal acquires at least two types of keywords in the search keyword;

The querying terminal sends at least two types of keywords to the search engine database, so that the search engine database obtains source data corresponding to the at least two types of keywords respectively;

The query terminal receives the source data sent by the search engine database;

The query terminal determines target data fed back to the user according to the source data.

In another aspect, the present disclosure provides a data processing method, including:

The search engine database receives the dimension keywords, index keywords, and time granularity key sent by the query terminal. a word, the dimension keyword, the index keyword, and the time granularity keyword are: the query terminal receives a query request of a user, and is obtained from a search keyword included in the query request;

The search engine database pre-stores data in the data exit and feature information of the data, and the feature information includes at least one of the following: a dimension feature, an index feature, and a time granularity feature;

The search engine data acquires first data corresponding to the dimensional feature matched by the dimension keyword, second data corresponding to the index feature matched by the index keyword, and time granularity matched with the time granularity keyword Third data corresponding to the feature;

Transmitting, by the search engine data, the first data, the second data, and the third data to the query terminal, so that the query terminal is configured according to the first data, the second data, and the The third data is determined to determine target data that is fed back to the user.

In still another aspect, the present disclosure provides a data processing method, including:

The search engine database acquires first data in the data application, and dimension features, indicator features, and time granularity features of the first data;

The search engine database respectively acquires a second data in the report, the knowledge base platform, the cluster physical table, and the dimensional characteristics of the second data;

The search engine database stores the first data, and dimension features, index features, and time granularity features of the first data;

The search engine database stores the second data and dimensional features of the second data.

In another aspect, the disclosure provides a query terminal, including: a receiving unit, a processing unit, and a sending unit;

The receiving unit is configured to receive a query request of a user, where the query request includes a search keyword;

The processing unit is coupled to the receiving unit, configured to acquire a dimension keyword, an index keyword, and a time granularity keyword in the search keyword;

The sending unit is coupled to the processing unit, configured to send the dimension keyword, the index keyword, and the time granularity keyword to a search engine database, so that the search engine database acquires The first data corresponding to the dimension feature matched by the dimension keyword, the second data corresponding to the indicator feature matched by the index keyword, and the third data corresponding to the time granularity feature matched by the time granularity keyword, The search engine database pre-stores data in the data exit and characteristic information of the data, and the data export includes at least one of the following: a data application, a report, a knowledge base platform, and a cluster physical table, and the feature information Including at least one of the following: dimensional features, indicator features, and time granularity features;

The receiving unit is further configured to receive the first data and the second data sent by the search engine database And the third data;

The processing unit is further configured to determine target data that is fed back to the user according to the first data, the second data, and the third data.

In still another aspect, the present disclosure provides a search engine database, including: a receiver, a memory, a processor, and a transmitter;

The receiver is configured to receive a dimension keyword, an index keyword, and a time granularity keyword sent by the query terminal, where the dimension keyword, the index keyword, and the time granularity keyword are the query terminal Receiving a query request of the user, and obtaining the search keyword included in the query request;

The memory is configured to store data in a data exit, and characteristic information of the data, where the data export includes at least one of the following: a data application, a report, a knowledge base platform, and a cluster physical table, and the feature information Including at least one of the following: dimensional features, indicator features, and time granularity features;

The processor is coupled to the receiver and the memory, and is configured to acquire first data corresponding to the dimensional feature matched by the dimension keyword, and second data corresponding to the index feature matched by the index keyword And third data corresponding to the time granularity feature matching the time granularity keyword;

The transmitter is coupled to the processor, configured to send the first data, the second data, and the third data to the query terminal, so that the query terminal is configured according to the first The data, the second data, and the third data determine target data that is fed back to the user.

In the present disclosure, data in a data application, a report, a knowledge base platform, and a cluster physical table are collected in advance into a search engine database, and dimension attributes, index features, and time granularity features are added to each piece of data collected. At least one; when the search engine receives the search keyword input by the user, firstly splits the search keyword to obtain a dimension keyword, an index keyword, and a time granularity keyword; and then, in a pre-established search engine database, Find the data matching the dimension keyword, the index keyword and the time granularity keyword respectively, and display the matched data to the user; the user does not need to traverse each data export to perform data search, only need to input the search keyword once, the search engine The database can find out the data related to the search keyword in all data exits, thereby improving the efficiency of finding data.

DRAWINGS

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, a brief description of the drawings used in the embodiments or the prior art description will be briefly described below. Obviously, the drawings in the following description It is a certain embodiment of the present invention, and for those skilled in the art, without creative labor, Other drawings can be obtained from these figures.

FIG. 1 is a schematic diagram of an optional application scenario of the present disclosure;

2 is a schematic structural diagram of a data processing system according to an embodiment of the present disclosure;

FIG. 3 is a flowchart of a data processing method according to Embodiment 1 of the present disclosure;

4 is a flowchart of a data processing method according to Embodiment 2 of the present disclosure;

FIG. 5 is a flowchart of a data processing method according to Embodiment 3 of the present disclosure;

6 is a flowchart of a data processing method according to Embodiment 4 of the present disclosure;

FIG. 7 is a flowchart of a data processing method according to Embodiment 5 of the present disclosure;

8 is a flowchart of a data processing method according to Embodiment 6 of the present disclosure;

9 is a flowchart of a data processing method according to Embodiment 7 of the present disclosure;

FIG. 10 is a flowchart of a data processing method according to Embodiment 8 of the present disclosure;

11 is a flowchart of a data processing method according to Embodiment 9 of the present disclosure;

FIG. 12 is a schematic structural diagram of a query terminal according to Embodiment 1 of the present disclosure;

FIG. 13 is a schematic structural diagram of a query terminal according to Embodiment 2 of the present disclosure;

FIG. 14 is a schematic structural diagram of a query terminal according to Embodiment 3 of the present disclosure;

FIG. 15 is a schematic structural diagram of a search engine database according to an embodiment of the present disclosure.

detailed description

Exemplary embodiments will be described in detail herein, examples of which are illustrated in the accompanying drawings. The following description refers to the same or similar elements in the different figures unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Instead, they are merely examples of devices and methods consistent with aspects of the invention as detailed in the appended claims.

In the prior art, when a non-technical employee of the company needs to obtain the "amount of sales of a home-made product" of the company, it is necessary to sequentially search for the company's data application export, report export, knowledge base platform export, and cluster physical table export. Until the company's "the amount of the home improvement product" is found, this will lead to a decline in the efficiency of data search. In response to this problem, the present invention proposes a data processing method, and the specific process of the data processing method provided by the present invention will now be described in conjunction with FIG.

As shown in FIG. 1 , the user 10 queries the terminal 11 for querying data. The user 10 may be a non-technical person in the company or a consumer. The query terminal 11 may be a terminal device in the company to which the user 10 belongs, or may be User 10's personal computer, laptop, etc. The query terminal 11 is installed with a search engine, and the user 10 can The search keyword is input in the search box of the search engine by querying the keyboard of the terminal 11, for example, the search keyword is "the most recent home improvement category transaction amount", and the semantic recognition module 12 splits the search keyword into the big data field. Dimensional keywords, index keywords, and time granularity keywords. Specifically, the dimension keywords are “home improvement category”, the indicator keyword is “transaction amount”, and the time granularity keyword is “the most recent day”. The method by which the semantic recognition module 12 splits the search keyword into a dimensional keyword, an index keyword, and a time granularity keyword will be described in detail in the following embodiments.

The semantic recognition module 12 transmits the split dimension keyword "home improvement category", the index keyword "transaction amount", and the time granularity keyword "last day" to the search engine database 13, and the data source of the search engine database 13 includes The data application program 15, the report 16, the knowledge base platform 17, and the cluster physical table 18, wherein the data application 15 may specifically be a data product, such as Alibaba's Taobao business and Baidu's Baidu index, etc., the data product is a web page. In the form of web products, the biggest difference between data products and ordinary web products is that the data products carry a large amount of data and need to frequently interact with the background data source, and the background data source is specifically stored with data operable by the data application 15. Device. In the present embodiment, the data in the data application 15 and the report 16 can be stored in the search engine database 13 by the syntax parser 19, taking the data application 15 as an example, since the data application 15 is through a software development kit (Software). The Development Kit (SDK) is developed so that the data in the data application 15 can be collected into the parser 19 via the SDK. The syntax parser 19 can parse a dimensionality feature, an index feature, a time granular feature, and a read table name of a Structured Query Language (SQL). For example, a piece of SQL is as follows:

SELECT stat_date AS date

, user_type AS user type

,se_lpv_pc_1d_001 AS Pv

,se_uv_pc_1d_001 AS Uv

FROM tbbi.ads_tb_log_1d

WHERE ds='20151026'

The syntax parser 19 can parse that the dimension feature of the segment of SQL is "user type", the indicator feature is "Pv, Uv", the time granularity feature is "the last day", and the read table name is "tbbi.ads_tb_log_1d". Through the foregoing method, the parser parser 19 can parse the dimensional features, index features, and time granularity features of each of the data in the data application 15 and the report 16.

The parser parser 19 transmits the parsed data to the search engine database 13, which stores not only the data itself but also dimensional features, index features, and time granularity features of the data. In addition, The search engine database 13 can also store the data in the knowledge base platform 17 and the cluster physical table 18. The storage process is specifically: splitting each data in the knowledge base platform 17 and the cluster physical table 18, and extracting the split after extracting The dimensional characteristics of each of the data, and each of the data in the knowledge base platform 17 and the cluster physical table 18, and the dimensional characteristics of each data are stored in the search engine database 13. As such, each data stored in the search engine database 13 has at least a dimensional feature.

When the search engine database 13 receives the dimension keyword "home improvement category", the index keyword "transaction amount", and the time granularity keyword "last day" sent by the semantic recognition module 12, respectively, the dimension keyword "home improvement" is found. The category "matched data, the data matching the index keyword "the transaction amount", and the data matching the time granularity keyword "the last day", the search engine database 13 sends the found matching data to the sequencer 14, if The search engine database 13 finds only one matching data, and the sequencer 14 sends the matching data to the query terminal 11, and the query terminal 11 displays the matching data; if the search engine database 13 finds more than one matching data, the sorting is performed. The device 14 sorts the plurality of matching data according to a preset algorithm, and sends the sorted plurality of matching data to the query terminal 11, and the query terminal 11 displays the plurality of matching data in the order of sorting. In this embodiment, the preset algorithm for sorting the plurality of matching data by the sequencer 14 includes at least one of the following: a Pagerank algorithm, a CUS-distance algorithm, a Latent Dirichlet Allocation (LDA) algorithm, and a width. Priority Search (BFS) algorithm, etc.

In this embodiment, the data in the data application, the report, the knowledge base platform, and the cluster physical table are collected in advance into the search engine database, and the dimension feature, the index feature, and the time granularity feature are added to each collected data. At least one; when the search engine receives the search keyword input by the user, firstly splits the search keyword to obtain a dimension keyword, an index keyword, and a time granularity keyword; and then, in a pre-established search engine database, Find the data matching the dimension keyword, the index keyword and the time granularity keyword respectively, and display the matched data to the user; the user does not need to traverse each data export to perform data search, only need to input the search keyword once, the search engine The database can find out the data related to the search keyword in all data exits, thereby improving the efficiency of finding data.

2 is a schematic structural diagram of a data processing system according to an embodiment of the present disclosure. As shown in FIG. 2, the data processing system includes a query terminal 1 and a search engine database 2, wherein the query terminal 1 is configured to receive a query request of a user, The query request includes a search keyword; the query terminal acquires a dimension keyword, an index keyword, and a time granularity keyword in the search keyword, and the dimension keyword, the index keyword, and the time The granularity keyword is sent to the search engine database.

As shown in FIG. 1 , the query terminal 11 receives the query request of the user 10 , and the query request may be performed in various manners. For example, the user 10 inputs text and voice on the search engine of the query terminal 11 , and the text or voice includes the user 10 Search for keywords. As shown in FIG. 1, the semantic recognition module 12 and the sequencer 14 may be modules belonging to the query terminal 11, and the semantic recognition module 12 splits the search keywords into dimension keywords, index keywords, and time granularity in the big data domain. Keywords, specifically, the dimension keyword is “home improvement category”, the indicator keyword is “transaction amount”, and the time granularity keyword is “last day”. The semantic recognition module 12 also transmits the dimension keyword "home improvement category", the index keyword "transaction amount", and the time granularity keyword "last day" to the search engine database 2.

The search engine database 2 pre-stores data in the data exit, and feature information of the data, the feature information including at least one of the following: a dimensional feature, an index feature, and a time granular feature.

Optionally, in this embodiment, the data export includes: a data application, a report, a knowledge base platform, and a cluster physical table. The search engine database 13 stores data in the data application, the report, the knowledge base platform, and the cluster physical table. And the characteristic information of each data, each data in the data application has dimensional characteristics, index characteristics and time granularity characteristics, and the data in the report, the knowledge base platform and the cluster physical table all have dimensional characteristics.

The search engine database 2 is configured to acquire first data corresponding to the dimensional feature matched by the dimension keyword, second data corresponding to the index feature matched by the index keyword, and time matched with the time granularity keyword The third data corresponding to the granularity feature, and the first data, the second data, and the third data are sent to the query terminal.

When the search engine database 13 receives the dimension keyword "home improvement category", the index keyword "transaction amount", and the time granularity keyword "last day" sent by the semantic recognition module 12, the search for the dimension keyword can be separately found. The matching data of the home improvement category, the data matching the index keyword "transaction amount", and the data matching the time granularity keyword "last day". The search engine database 13 can match the dimension keyword "home improvement category" recognized by the semantic recognition module 12 with the dimensional features of the stored data, and obtain first data corresponding to the dimensional feature matching the dimensional keyword, the first A data may be a plurality of data, and the first data may be data derived from the data application 15, the report 16, the knowledge base platform 17, or the cluster physical table 18.

In addition, the search engine database 13 may further match the index keyword “transaction amount” recognized by the semantic recognition module 12 with the index feature of the stored data, and obtain second data corresponding to the indicator feature that matches the index keyword. The second data may be a plurality of data originating from the data application 15.

In addition, the search engine database 13 may also match the time granularity keyword "last day" recognized by the semantic recognition module 12 with the time granularity feature of the stored data to obtain a time granularity feature corresponding to the time granularity keyword. The third data, which may be a plurality of data originating from the data application 15.

The first data, the second data, and the third data obtained by the search engine database 13 are sent to the query terminal 11, and may be sent to the sequencer 14 in the query terminal 11.

The query terminal 1 is further configured to determine target data fed back to the user according to the first data, the second data, and the third data, and display the target data to the user.

If the search engine database 13 finds only one matching data, that is, the first data, the second data, and the third data are the same data, the sequencer 14 sends the matching data to the display of the query terminal 11. The display of the inquiry terminal 11 displays the matching data.

If there are multiple matching data found by the search engine database 13, that is, the first data, the second data, and the third data are not the same data, the sequencer 14 matches the plurality of matches according to a preset algorithm. The data is sorted, and the sorted plurality of matching data are sent to the display of the query terminal 11, and the display of the query terminal 11 displays the plurality of matching data in a sorted order.

In this embodiment, the data in the data application, the report, the knowledge base platform, and the cluster physical table are collected in advance into the search engine database, and the dimension feature, the index feature, and the time granularity feature are added to each collected data. At least one; when the search engine receives the search keyword input by the user, firstly splits the search keyword to obtain a dimension keyword, an index keyword, and a time granularity keyword; and then, in a pre-established search engine database, Find the data matching the dimension keyword, the index keyword and the time granularity keyword respectively, and display the matched data to the user; the user does not need to traverse each data export to perform data search, only need to input the search keyword once, the search engine The database can find out the data related to the search keyword in all data exits, thereby improving the efficiency of finding data.

FIG. 3 is a flowchart of a data processing method according to Embodiment 1 of the present disclosure. As shown in FIG. 3, the method includes the following steps:

Step S201: The query terminal receives a query request of the user, where the query request includes a search keyword.

As shown in FIG. 1 , the query terminal 11 receives the query request of the user 10 , and the query request may be performed in various manners. For example, the user 10 inputs text and voice on the search engine of the query terminal 11 , and the text or voice includes the user 10 Searching for keywords; or, the search engine of the query terminal 11 is provided with a drop-down list in which keywords are pre-stored, and the user can input the pre-retrieved keywords by selecting keywords in the list and clicking; The user 10 previews the text information on the query terminal 11, and the user 10 selects a keyword from the text information of the preview, and searches for the keyword by dragging, sliding, and clicking the function key.

The user 10 queries the data through the query terminal 11, and the user 10 can be a non-technical person in the company, and can also It is a consumer, and the inquiry terminal 11 may be a terminal device in a company to which the user 10 belongs, or may be a device such as a personal computer or a notebook computer of the user 10. The query terminal 11 is installed with a search engine, and the user 10 can input a search keyword in the search box of the search engine by querying the keyboard of the terminal 11, for example, the search keyword is "the latest day home improvement category transaction amount".

Step S202: The query terminal acquires a dimension keyword, an index keyword, and a time granularity keyword in the search keyword.

As shown in FIG. 1 , the semantic recognition module 12 and the sequencer 14 may be modules belonging to the query terminal 11 or modules belonging to the search engine database 13 , and the query terminal 11 and the search engine database 13 may be directly connected or may be connected. Connect indirectly through other devices. In this embodiment, the semantic identification module 12 and the sequencer 14 are directly connected to the query terminal 11, the query terminal 11, and the search engine database 13 as an example.

The semantic recognition module 12 splits the search keyword into a dimension keyword, an index keyword, and a time granularity keyword in the big data domain. Specifically, the dimension keyword is “home improvement category”, and the index keyword is “transaction amount”. The time granularity keyword is "the most recent day."

Step S203: The query terminal sends the dimension keyword, the index keyword, and the time granularity keyword to a search engine database, so that the search engine database obtains a dimension matching the dimension keyword. The first data corresponding to the feature, the second data corresponding to the indicator feature matched by the index keyword, and the third data corresponding to the time granularity feature matched by the time granularity keyword.

In an embodiment, the search engine database pre-stores data in the data exit and feature information of the data, and the feature information includes at least one of the following: a dimensional feature, an indicator feature, and a time granularity feature.

Transmitting, by the query terminal, the dimension keyword, the index keyword, and the time granularity keyword to a search engine database, where the search engine database pre-stores data in a data exit, and characteristics of the data The information includes at least one of the following: a dimensional feature, an indicator feature, and a time granularity feature, and the data outlet includes at least one of the following: a data application, a report, a knowledge base platform, and a cluster physical table. Optionally, in this embodiment, the data export includes: a data application, a report, a knowledge base platform, and a cluster physical table. The search engine database 13 stores data in the data application, the report, the knowledge base platform, and the cluster physical table. .

As shown in FIG. 1, the data source of the search engine database 13 includes a data application 15, a report 16, a knowledge base platform 17, and a cluster physical table 18. The data application 15 may specifically be a data product, such as Alibaba's Taobao. Business and Baidu's Baidu Index, etc., data products are web products in the form of web pages. The biggest difference between data products and ordinary web products is that data products carry a large amount of data and need to be frequently and later. The data source is interactive, and the background data source is specifically a device that stores data operable by the data application 15. In the present embodiment, the data in the data application 15 can be stored in the search engine database 13 by the syntax parser 19, and specifically, the data in the data application 15 is collected into the syntax parser 19 by the SDK. The syntax parser 19 can parse a dimensionality feature, an index feature, a time granular feature, and a read table name of a Structured Query Language (SQL). For example, a piece of SQL is as follows:

SELECT stat_date AS date

, user_type AS user type

,se_lpv_pc_1d_001 AS Pv

,se_uv_pc_1d_001 AS Uv

FROM tbbi.ads_tb_log_1d

WHERE ds='20151026'

The syntax parser 19 can parse that the dimension feature of the segment of SQL is "user type", the indicator feature is "Pv, Uv", the time granularity feature is "the last day", and the read table name is "tbbi.ads_tb_log_1d". Through the foregoing method, the syntax parser 19 can parse the dimensional features, index features, and time granularity features of each data in the data application 15. The parser parser 19 transmits the parsed data to the search engine database 13, which stores not only the data itself but also dimensional features, index features, and time granularity features of the data.

In addition, the search engine database 13 can also store the data in the report 16, the knowledge base platform 17, and the cluster physical table 18. The storage process is specifically: performing each data in the report 16, the knowledge base platform 17, and the cluster physical table 18. Splitting, extracting the dimensional features of each of the split data, and storing each of the data in the report 16, the knowledge base platform 17, and the cluster physical table 18, and the dimensional features of each data in the search engine database 13. As such, each data stored in the search engine database 13 has at least a dimensional feature.

When the search engine database 13 receives the dimension keyword "home improvement category", the index keyword "transaction amount", and the time granularity keyword "last day" sent by the semantic recognition module 12, the search for the dimension keyword can be separately found. The matching data of the home improvement category, the data matching the index keyword "transaction amount", and the data matching the time granularity keyword "last day".

In the present embodiment, the search engine database 13 stores data in the data application 15 and dimensional features, index features, and time granularity features of each of the data in the data application 15. In addition, the search engine database 13 also stores data in the report 16, the knowledge base platform 17, and the cluster physical table 18, as well as the dimensional characteristics of each of the data in the report 16, the knowledge base platform 17, and the cluster physical table 18. In addition, the number in the search engine database 13 The dimensional characteristics of the data may be different and may be the same; the index characteristics of each data may be different and may be the same; the time granularity characteristics of each data may be different and may be the same.

The search engine database 13 in this embodiment may match the dimension keyword "home improvement category" recognized by the semantic recognition module 12 with the dimensional features of the data stored therein, and obtain the first corresponding to the dimensional feature matching the dimensional keyword. A data, the first data may be a plurality of data, and the first data may be data derived from the data application 15, the report 16, the knowledge base platform 17, or the cluster physical table 18.

In addition, the search engine database 13 may further match the index keyword “transaction amount” recognized by the semantic recognition module 12 with the index feature of the stored data, and obtain second data corresponding to the indicator feature that matches the index keyword. The second data may be a plurality of data originating from the data application 15.

In addition, the search engine database 13 may also match the time granularity keyword "last day" recognized by the semantic recognition module 12 with the time granularity feature of the stored data to obtain a time granularity feature corresponding to the time granularity keyword. The third data, which may be a plurality of data originating from the data application 15.

Step S204: The query terminal receives the first data, the second data, and the third data that are sent by the search engine database.

The first data, the second data, and the third data obtained by the search engine database 13 are sent to the query terminal 11, and may be sent to the sequencer 14 in the query terminal 11.

Step S205: The querying terminal determines, according to the first data, the second data, and the third data, target data that is fed back to the user.

If the search engine database 13 finds only one matching data, that is, the first data, the second data, and the third data are the same data, the sequencer 14 sends the matching data to the display of the query terminal 11. The display of the inquiry terminal 11 displays the matching data.

If there are multiple matching data found by the search engine database 13, that is, the first data, the second data, and the third data are not the same data, the sequencer 14 matches the plurality of matches according to a preset algorithm. The data is sorted, and the sorted plurality of matching data are sent to the display of the query terminal 11, and the display of the query terminal 11 displays the plurality of matching data in a sorted order.

In this embodiment, the data in the data application, the report, the knowledge base platform, and the cluster physical table are collected in advance into the search engine database, and the dimension feature, the index feature, and the time granularity feature are added to each collected data. At least one; when the search engine receives the search keyword input by the user, firstly splits the search keyword to obtain a dimension keyword, an index keyword, and a time granularity keyword; and then, in a pre-established search engine database, Find numbers that match the dimension keyword, indicator keyword, and time granularity keyword According to the data, the matching data is displayed to the user; the user does not need to traverse each data outlet to perform data search, and only needs to input the search keyword once, and the search engine database can find the data related to the search keyword in all data outlets. This improves the efficiency of finding data.

4 is a flowchart of a data processing method according to Embodiment 2 of the present disclosure. As shown in FIG. 4, on the basis of the embodiment shown in FIG. 3, the query terminal acquires a dimension keyword in the search keyword, The method for index keywords and time granularity keywords may specifically include the following steps:

Step S301: The query terminal performs word segmentation on the search keyword to obtain a plurality of target word segments.

For example, as described in step S201, the search keyword input by the user is "the most recent home improvement category transaction amount". The query terminal 11 can also split the search keywords input by the user by using the TF-idf algorithm to obtain a plurality of target word segments, and the plurality of target word segments are “the latest day”, “home improvement category”, and “transaction amount”.

Step S302: The querying terminal queries a preset mapping table according to each target word segment, where the mapping table includes a dimension word segmentation, an index word segmentation, and a time granularity word segmentation.

In this embodiment, the query terminal 11 is pre-established with a mapping table, which includes a dimension word segmentation, an index word segmentation, and a time granularity word segmentation, the dimension word segmentation may be a plurality of segmentation words having dimensional features, and the index segmentation words may be multiple index features. The word segmentation, time granularity word segmentation can be a plurality of word segmentation with time granularity characteristics. According to the plurality of target word segments split in step S301, the query terminal 11 respectively queries the mapping table, and for each target word segment, it is determined whether there is a word segment matching the target word segment in the mapping table.

Step S303: The query terminal determines, as the dimension keyword, a target word segment that matches the dimension word segment among the plurality of target word segments.

For example, if the “home improvement category” in the plurality of target word segments matches the dimension word segment in the mapping table, the “home improvement category” is used as the dimension keyword in the search keyword.

Step S304: The query terminal determines, as the index keyword, a target word segment that matches the indicator word segment among the plurality of target word segments.

For example, if the “transaction amount” in the plurality of target particials matches the indicator participle in the mapping table, the “transaction amount” is used as the index keyword in the search keyword.

Step S305: The query terminal determines, as the time granularity keyword, a target word segment that matches the time granularity word segment among the plurality of target word segments.

For example, if the “last day” in the plurality of target word segments matches the time granularity word segmentation in the mapping table, the “last day” is used as the time granularity keyword in the search keyword.

In this embodiment, a plurality of target word segments are obtained by performing word segmentation processing on the search keywords, and the dimension keywords, index keywords, and time granularity keywords in the plurality of target word segments are queried according to the pre-established mapping table, thereby improving the determined search. The efficiency of dimensional keywords, index keywords and time-granulated keywords in keywords.

FIG. 5 is a flowchart of a data processing method according to Embodiment 3 of the present disclosure. As shown in FIG. 5, based on any of the foregoing embodiments, based on the second embodiment, the data processing method provided by this embodiment is specific. Proceed as follows:

Step S401: The querying terminal receives a query request of the user, where the query request includes a search keyword.

Step S402: The query terminal acquires a dimension keyword, an index keyword, and a time granularity keyword in the search keyword.

Step S403: The query terminal sends the dimension keyword, the index keyword, and the time granularity keyword to a search engine database, so that the search engine database obtains a dimension matching the dimension keyword. The first data corresponding to the feature, the second data corresponding to the indicator feature matched by the index keyword, and the third data corresponding to the time granularity feature matched by the time granularity keyword.

Step S404: The query terminal receives the first data, the second data, and the third data that are sent by the search engine database.

Steps S401 to S404 are consistent with steps S201-S204, respectively, and the specific method is not described herein again.

Step S405: The querying terminal determines whether the first data, the second data, and the third data are the same data. If yes, step S406 is performed; otherwise, step S407 is performed.

Step S406: The querying terminal determines the same data as target data fed back to the user.

As shown in FIG. 1, if the search engine database 13 finds only one matching data, that is, the first data, the second data, and the third data are the same data, the sequencer 14 sends the matching data. To the display of the inquiry terminal 11, the display of the inquiry terminal 11 displays the matching data.

Step S407: The query terminal sorts the first data, the second data, and the third data, and determines the sorted data as target data fed back to the user.

If there are multiple matching data found by the search engine database 13, that is, the first data, the second data, and the third data are not the same data, the sequencer 14 matches the plurality of matches according to a preset algorithm. The data is sorted, and the sorted plurality of matching data are sent to the display of the query terminal 11, and the display of the query terminal 11 displays the plurality of matching data in a sorted order.

In the step S407, the method for the query terminal to sort the first data, the second data, and the third data may specifically include the following steps:

Step S51: The query terminal calculates a weight value of each of the first data, the second data, and the third data.

Specifically, the weight value of each data can be calculated by the Pagerank algorithm.

Step S52: The query terminal calculates a similarity between each of the first data, the second data, and the third data and the search keyword.

Specifically, the CUS-distance algorithm can be used to calculate the similarity between each data and the search keyword input by the user.

Step S53: The query terminal calculates a sort value of each data according to the weight value and the similarity of each data.

Specifically, a value obtained by adding a weight value and a similarity of each data may be used as a sort value of the data.

Step S54: The querying terminal sorts each of the first data, the second data, and the third data according to the sorting value of each data.

Specifically, each of the first data, the second data, and the third data may be sorted in descending order according to a sort value of each data.

Optionally, the querying terminal determines, according to the sorting value of each data, data that the ranking value in the first data, the second data, and the third data is greater than a first threshold; the query terminal The data whose sort value is greater than the first threshold is sorted according to the size of the sort value.

In addition, after calculating the ranking values of each of the first data, the second data, and the third data, the first data, the second data, and the third data may be determined. The data in the sorted value is greater than the first threshold, and the data whose sorted value is greater than the first threshold is sorted according to the size of the sorted value.

In this embodiment, a plurality of data matching the search keywords searched by the search engine database are sorted, and the sorting is based on the sort value of each data, the sort value and the weight value of each data, and the data and the search The similarity of the keyword is related, the larger the sorting value is, the stronger the correlation between the data and the search keyword is, and the plurality of sorted data are fed back to the user, and the user can conveniently view the most relevant to the search keyword. Strong data improves the user experience.

FIG. 6 is a flowchart of a data processing method according to Embodiment 4 of the present disclosure. As shown in FIG. 6, based on any of the foregoing embodiments, based on the second embodiment, the data processing method provided by this embodiment is specific. Proceed as follows:

Step S601: The querying terminal receives a query request of the user, where the query request includes a search keyword.

Step S602: The query terminal acquires a dimension keyword, an index keyword, and a time granularity keyword in the search keyword.

Step S603: The query terminal sends the dimension keyword, the index keyword, and the time granularity keyword to a search engine database, so that the search engine database obtains a dimension matching the dimension keyword. The first data corresponding to the feature, the second data corresponding to the indicator feature matched by the index keyword, and the third data corresponding to the time granularity feature matched by the time granularity keyword.

Step S604: The query terminal receives the first data, the second data, and the third data that are sent by the search engine database.

Step S605: The querying terminal determines, according to the first data, the second data, and the third data, target data that is fed back to the user.

Steps S601 to S605 are respectively consistent with steps S201 to S205, and the specific method is not described herein again.

Step S606: The query terminal receives a click operation of the user on the target data.

After the step S407, the sorted target data may be displayed on the query terminal, and the user may click to view the plurality of target data by querying the terminal. When the user clicks on a certain target data, the query terminal can receive the click operation of the target data by the user.

Step S607: The querying terminal establishes an association relationship between the user and the target data according to the click operation.

The association relationship includes a degree of association, the degree of association identifying a degree of association of the user with the target data.

In this embodiment, the query terminal establishes an association relationship between the user and the target data according to a click operation generated by the user clicking a certain target data, and may also calculate target data of the user and the click according to the association rule and the collaborative filtering rule. The degree of association, the number of target data clicked by the user may be multiple.

Step S608: When the user does not input the search keyword, the query terminal displays the target data according to the association relationship.

When the user does not input the search keyword in the query terminal 11, the query terminal 11 can display the target data according to the association relationship between the user and the target data that the user clicks on, that is, the query terminal 11 can display the target data that the user clicked to the user. .

Specifically, the association relationship includes an association degree, where the association degree identifies a degree of association between the user and the target data. The querying terminal displays the target data according to the association relationship, including: the querying terminal displays the target data whose association degree is greater than a second threshold.

Optionally, the query terminal displays the target data whose association degree is greater than a second threshold. The association relationship between the user and the target data that the user clicks on includes the degree of association between the user and the target data, and the query terminal 11 can also display the target data that the user clicked has a degree of relevance greater than the second threshold.

In this embodiment, by establishing an association relationship between the user and the target data that the user clicked on, when the user does not input the search keyword, the target data that the user clicked may be displayed according to the relationship between the user and the target data, thereby improving The convenience of users to query data.

FIG. 7 is a flowchart of a data processing method according to Embodiment 5 of the present disclosure. As shown in FIG. 7, the specific steps of the data processing method provided in this embodiment are as follows:

Step S501: The query terminal receives a query request of the user, where the query request includes a search keyword.

As shown in FIG. 1 , the query terminal 11 receives the query request of the user 10 , and the query request may be performed in various manners. For example, the user 10 inputs text and voice on the search engine of the query terminal 11 , and the text or voice includes the user 10 Search for keywords.

Step S502: The query terminal acquires at least two types of keywords in the search keyword.

In this embodiment, when the query terminal classifies the search keywords that the user requests for the query, it may not be limited to the three types of keywords: the dimension keyword, the index keyword, and the time granularity keyword, because the user does not request the query. Each of the search keywords includes three types of keywords: a dimension keyword, an index keyword, and a time granularity keyword. Therefore, the semantic recognition module 12 corresponding to the query terminal 11 shown in FIG. 1 can also retrieve the key of the user requesting the query. The word is divided into at least two types of keywords. For example, the user is a seller. The search keyword requested by the seller is “Is there a customer to evaluate my product?”, and the verb “evaluation” and the noun “commodity” can be separated.

Step S503: The query terminal sends at least two types of keywords to the search engine database, so that the search engine database obtains source data corresponding to the at least two types of keywords respectively.

The query terminal sends the verb "evaluation" and the noun "item" to the search engine database, and the search engine database stores the commodity information of all the seller's products, and the evaluation information of each commodity. The search engine database obtains product information of all the products of the seller according to the "product", and the product information specifically includes a name, a place of origin, a material, and the like, and obtains evaluation information of all the products according to the "evaluation".

Step S504: The query terminal receives the source data sent by the search engine database.

The search engine database transmits the product information and the evaluation information to the query terminal. Since the product information herein may be plural, the evaluation information may be plural.

Step S505: The querying terminal determines, according to the source data, target data that is fed back to the user.

The query terminal may determine, according to the number of pieces of evaluation information of each product, product information that is fed back to the product with the most user evaluation information, and may also feed back the first pieces of evaluation information of each product to the user. The specific implementation manner in which the query terminal determines the target data fed back to the user is not limited.

In this embodiment, by classifying the search keywords, the results of the classification are not limited to the dimension keywords, the index keywords, and the time granularity keywords, thereby improving the flexibility of the search keyword classification and increasing the search keywords. The flexibility of the search and the scope of the search are also expanded.

FIG. 8 is a flowchart of a data processing method according to Embodiment 6 of the present disclosure. As shown in FIG. 8, the specific steps of the data processing method provided in this embodiment are as follows:

Step S701: The search engine database receives the dimension keyword, the index keyword, and the time granularity keyword sent by the query terminal.

The dimension keyword, the index keyword, and the time granularity keyword are the query requests received by the query terminal by the user, and are obtained from the search keywords included in the query request.

In this embodiment, the search engine database pre-stores data in the data exit and feature information of the data, and the feature information includes at least one of the following: a dimension feature, an index feature, and a time granular feature.

The data exit includes at least one of the following: a data application, a report, a knowledge base platform, and a cluster physical table.

Step S702: The search engine data acquires first data corresponding to the dimensional feature matched by the dimension keyword, second data corresponding to the index feature matched by the index keyword, and matching with the time granularity keyword. The time granularity feature corresponds to the third data.

Step S703, the search engine data sends the first data, the second data, and the third data to the query terminal, so that the query terminal is configured according to the first data, the second The data and the third data determine target data that is fed back to the user.

The principle of the method in this embodiment is consistent with the principle of the method in the embodiment shown in FIG. 3. The specific process is not described here.

In this embodiment, the data in the data application, the report, the knowledge base platform, and the cluster physical table are collected in advance into the search engine database, and the dimension feature, the index feature, and the time granularity feature are added to each collected data. At least one; when the search engine receives the search keyword input by the user, firstly splits the search keyword to obtain a dimension keyword, an index keyword, and a time granularity keyword; and then, in a pre-established search engine database, Find the data matching the dimension keyword, the index keyword and the time granularity keyword respectively, and display the matched data to the user; the user does not need to traverse each data export to perform data search, only need to input the search keyword once, the search engine The database can find out the data related to the search keyword in all data exits, thereby improving the efficiency of finding data.

FIG. 9 is a flowchart of a data processing method according to Embodiment 7 of the present disclosure. As shown in FIG. 9, the specific steps of the data processing method provided in this embodiment are as follows:

Step S801: The search engine database stores data in the data application, the report, the knowledge base platform, and the cluster physical table.

On the basis of the embodiment shown in FIG. 3, the search engine database 13 pre-stores data in the data application, the report, the knowledge base platform, and the cluster physical table before receiving the search keyword input by the user.

Specifically, the data in the data application 15 can be stored in the search engine database 13 by the syntax parser 19, and specifically, the data in the data application 15 is collected into the syntax parser 19 by the SDK. The syntax parser 19 can parse a dimensionality feature, an index feature, a time granular feature, and a read table name of a Structured Query Language (SQL). For example, a piece of SQL is as follows:

SELECT stat_date AS date

, user_type AS user type

,se_lpv_pc_1d_001 AS Pv

,se_uv_pc_1d_001 AS Uv

FROM tbbi.ads_tb_log_1d

WHERE ds='20151026'

The syntax parser 19 can parse that the dimension feature of the segment of SQL is "user type", the indicator feature is "Pv, Uv", the time granularity feature is "the last day", and the read table name is "tbbi.ads_tb_log_1d". Through the foregoing method, the syntax parser 19 can parse the dimensional features, index features, and time granularity features of each data in the data application 15. The parser parser 19 transmits the parsed data to the search engine database 13, which stores not only the data itself but also dimensional features, index features, and time granularity features of the data.

In addition, the search engine database 13 can also store the data in the report 16, the knowledge base platform 17, and the cluster physical table 18. The storage process is specifically: performing each data in the report 16, the knowledge base platform 17, and the cluster physical table 18. Splitting, extracting the dimensional features of each of the split data, and storing each of the data in the report 16, the knowledge base platform 17, and the cluster physical table 18, and the dimensional features of each data in the search engine database 13. As such, each data stored in the search engine database 13 has at least a dimensional feature.

Step S802: The search engine database receives the dimension keyword, the index keyword, and the time granularity keyword sent by the query terminal, where the dimension keyword, the index keyword, and the time granularity keyword are the check The querying terminal receives the query request of the user and obtains the search keyword included in the query request.

In this embodiment, the search engine database pre-stores data in the data exit and feature information of the data, and the feature information includes at least one of the following: a dimension feature, an index feature, and a time granular feature.

The data exit includes at least one of the following: a data application, a report, a knowledge base platform, and a cluster physical table.

Step S803, the search engine data acquires first data corresponding to the dimensional feature matched by the dimension keyword, second data corresponding to the index feature matched by the index keyword, and matching with the time granularity keyword. The time granularity feature corresponds to the third data.

Step S804, the search engine data sends the first data, the second data, and the third data to the query terminal, so that the query terminal is configured according to the first data, the second The data and the third data determine target data that is fed back to the user.

The principle of the method described in step S802 to step S804 is the same as that of the method described in step S701 to step S703, and details are not described herein again.

In this embodiment, the data in the data application, the report, the knowledge base platform, and the cluster physical table are collected in advance into the search engine database, and the dimension feature, the index feature, and the time granularity feature are added to each collected data. At least one; when the search engine receives the search keyword input by the user, firstly splits the search keyword to obtain a dimension keyword, an index keyword, and a time granularity keyword; and then, in a pre-established search engine database, Find the data matching the dimension keyword, the index keyword and the time granularity keyword respectively, and display the matched data to the user; the user does not need to traverse each data export to perform data search, only need to input the search keyword once, the search engine The database can find out the data related to the search keyword in all data exits, thereby improving the efficiency of finding data.

10 is a flowchart of a data processing method according to Embodiment 8 of the present disclosure. As shown in FIG. 10, the search engine database stores the data application, the report, the knowledge base platform, and the cluster physical table. The data in the specific may include the following steps S901 and S902:

Step S901: The search engine database stores data in the data application.

Step S901 can be implemented by the following steps S11-S13:

Step S11: The search engine database acquires access logic of the data application accessing the data source.

The access logic includes data in the data application, the data source storing output logic of the data.

This embodiment introduces a method of storing data in the data application program to the search engine database, and the method described in this embodiment is different from the data application 15 in the syntax parser 19 described in the above embodiment. The method of storing data in the search engine database 13.

In this embodiment, the data application may be specifically in the form of a web page, and needs to interact with the background data source frequently; the background data source may be specifically a device that stores the data application operation data. Since the data application is developed according to the SDK, the SDK has the greatest operational authority for the data application, so the first access logic of the data application to the background data source can be captured by the SDK, and the first access logic includes the data application. The time when the program accesses the background data source, the second access logic of the user to the data application, and so on. Therefore, the second access logic of the user to the data application can be obtained through the existing parsing method. The second access logic of the user to the data application includes field information such as the time when the user accesses the data application, the data in the data application currently accessed by the user, and the like. Therefore, through the existing parsing method, the data in the data application currently accessed by the user can be obtained.

Suppose the user's second access logic to the data application is:

SELECT stat_date AS date

, user_type AS user type

,se_lpv_pc_1d_001 AS Pv

,se_uv_pc_1d_001 AS Uv

FROM tbbi.ads_tb_log_1d

WHERE ds='20151026'

By parsing the second access logic, the data in the data application currently accessed by the user is “tbbi.ads_tb_log_1d”, that is, the information after the FROM field.

In addition, the background data source stores the output logic of each data. Therefore, in the background data source, the output logic of the data in the data application currently accessed by the user can be directly searched.

Step S12: The search engine database determines feature information of data in the data application according to the output logic.

Specifically, the output logic includes aggregation object information of the data, indicator information of the participation operation in the aggregation process, and time information of the indicator operation.

The implementation of step S12 is specifically: the search engine database determines that the aggregated object information of the data is a dimensional feature of the data; and the search engine database determines that the indicator information of the data participating in the operation in the aggregation process is the An index feature of the data; the search engine database is determined according to time information of the indicator operation Time granularity characteristics of the data.

The output logic of the data in the data application currently accessed by the user is parsed, and the aggregated object information of the current data, the index information of the participating operations in the aggregation process, and the time information of the index calculation are obtained.

In this embodiment, it is assumed that the output logic of the data in the data application currently accessed by the user is as follows:

Select stat_date, user_type, count(1)se_lpv_pc_1d_001, count(distinct uid)se_uv_pc_1d_001

From tbcdm.dwd_tb_log_1d where ds=’20160119’

Group by user_type, stat_date

By parsing the above output logic, the aggregated object information of the data in the data application currently accessed by the user is stat_date, user_type, that is, the information after the Group by field; the index information of the participating operation in the aggregation process is se_lpv_pc_1d_001, Se_uv_pc_1d_001, that is, the information after the count(1) and count(distinct uid) fields; the time information of the index operation is '20160119', that is, the fractional area after the where ds field.

Determining the aggregated object information of the current data as a dimensional feature of the current data, determining that the indicator information of the participating operation in the current data aggregation process is an indicator feature of the current data, and determining the time granularity characteristic of the current data according to the time information of the current data index operation .

In addition, the time interval represented by the time information of the above index calculation may be used as the time granularity feature of the current data, for example, the time information of the current data index operation, that is, the score area after the where field is “ds='20160119'” , the time granularity characteristic of the current data is 1, and, for example, the time information of the current data index operation, that is, the score area after the where field is “ds>='20160101'and ds<='20160107'”, the current data The time granularity feature is 7.

Step S13: The search engine database stores data in the data application and feature information of the data.

Finally, dimension features, metric features, and time granularity features are added to the data in the data application currently accessed by the user, and the data after the feature is added is stored in the search engine database.

In this embodiment, since the user accesses the data application once, the dimension feature, the index feature, and the time granularity feature of the data in the data application currently accessed by the user can be obtained, and the data application currently accessed by the user is obtained. The data in the program adds the above dimensional features, index features, and time granularity features. Finally, the data after adding the above features is stored in the search engine database. When the user accesses all the data in the data application, all the data in the data application can be stored in the search engine database, and the search engine data Each piece of data in the library has dimensional features, indicator characteristics, and time granularity characteristics.

Step S902: The search engine database stores the data in the report, the knowledge base platform, and the cluster physical table.

Step S902 can be implemented by the following steps S21-S23:

Step S21: The search engine database respectively acquires data in the report, the knowledge base platform, and the cluster physical table.

In this embodiment, each of the data in the report, the knowledge base platform, and the cluster physical table may be split by a TF-iDF algorithm.

Step S22: The search engine database determines, according to a preset algorithm, a dimensional feature of each data in the report, the knowledge base platform, and the cluster physical table.

The LDA algorithm and the TOPIC MODEL algorithm are used to extract the features of the split data, and the extracted features are used as the dimensional features of the corresponding data.

Step S23: The search engine database stores the data in the report, the knowledge base platform, and the cluster physical table, and the dimensional features of the data.

Dimension features are added to each of the data in the report, the knowledge base platform, and the cluster physical table, and the data after adding the dimensional features is stored in a search engine database.

In this embodiment, all data in the data application is stored in the search engine database, and each data stored in the search engine database from the data application is associated with dimensional features, index features, and time granularity features; in addition, the search engine The database stores all the data in the report, the knowledge base platform, and the cluster physical table, and each data association stored from the report, the knowledge base platform, and the cluster physical table to the search engine database has dimensional characteristics.

FIG. 11 is a flowchart of a data processing method according to Embodiment 9 of the present disclosure. As shown in FIG. 11, the data processing method provided in this embodiment may include the following steps:

Step S1001: The search engine database acquires first data in the data application, and dimension features, index features, and time granularity features of the first data.

In this embodiment, the implementation manner of step S1001 may include the following two types:

The first type: the search engine database receives the first data sent by the parser, and the dimensional feature, the index feature, and the time granularity feature of the first data, where the parser is used to collect the data application The first data in the first data, and the dimensional feature, the index feature, and the time granularity feature of the first data are parsed.

Specifically, the data in the data application 15 can be stored in the search engine database 13 by the syntax parser 19, and specifically, the data in the data application 15 is collected into the syntax parser 19 by the SDK. The syntax parser 19 can parse a dimensionality feature, an index feature, a time granular feature, and a read table name of a Structured Query Language (SQL). For example, a piece of SQL is as follows:

SELECT stat_date AS date

, user_type AS user type

,se_lpv_pc_1d_001 AS Pv

,se_uv_pc_1d_001 AS Uv

FROM tbbi.ads_tb_log_1d

WHERE ds='20151026'

The syntax parser 19 can parse that the dimension feature of the segment of SQL is "user type", the indicator feature is "Pv, Uv", the time granularity feature is "the last day", and the read table name is "tbbi.ads_tb_log_1d". Through the foregoing method, the syntax parser 19 can parse the dimensional features, index features, and time granularity features of each data in the data application 15. The parser parser 19 transmits the parsed data to the search engine database 13, which stores not only the data itself but also dimensional features, index features, and time granularity features of the data.

The second type includes the following steps S31-S32:

Step S31: The search engine database acquires access logic of the data application access data source, where the access logic includes first data in the data application, and the data source stores the first data. Out of logic.

In this embodiment, the data application may be specifically in the form of a web page, and needs to interact with the background data source frequently; the background data source may be specifically a device that stores the data application operation data. Since the data application is developed according to the SDK, the SDK has the greatest operational authority for the data application, so the first access logic of the data application to the background data source can be captured by the SDK, and the first access logic includes the data application. The time when the program accesses the background data source, the second access logic of the user to the data application, and so on. Therefore, the second access logic of the user to the data application can be obtained through the existing parsing method. The second access logic of the user to the data application includes field information such as the time when the user accesses the data application, the data in the data application currently accessed by the user, and the like. Therefore, through the existing parsing method, the data in the data application currently accessed by the user can be obtained.

Suppose the user's second access logic to the data application is:

SELECT stat_date AS date

, user_type AS user type

,se_lpv_pc_1d_001 AS Pv

,se_uv_pc_1d_001 AS Uv

FROM tbbi.ads_tb_log_1d

WHERE ds='20151026'

By parsing the second access logic, the data in the data application currently accessed by the user is “tbbi.ads_tb_log_1d”, that is, the information after the FROM field.

In addition, the background data source stores the output logic of each data. Therefore, in the background data source, the output logic of the data in the data application currently accessed by the user can be directly searched.

The output logic includes aggregation object information of the first data, indicator information of the participation operation in the aggregation process, and time information of the indicator operation. Specifically, the search engine database determines that the aggregated object information of the first data is a dimensional feature of the first data; and the search engine database determines that the indicator information of the first data participating in the operation in the aggregation process is Determining an indicator characteristic of the first data; the search engine database determining a time granularity characteristic of the first data according to time information of the indicator operation.

Step S32: The search engine database determines, according to the output logic, feature information of the first data in the data application, where the feature information includes a dimension feature, an indicator feature, and a time granularity feature.

In this embodiment, it is assumed that the output logic of the data in the data application currently accessed by the user is as follows:

Select stat_date, user_type, count(1)se_lpv_pc_1d_001, count(distinct uid)se_uv_pc_1d_001

From tbcdm.dwd_tb_log_1d where ds=’20160119’

Group by user_type, stat_date

By parsing the above output logic, the aggregated object information of the data in the data application currently accessed by the user is stat_date, user_type, that is, the information after the Group by field; the index information of the participating operation in the aggregation process is se_lpv_pc_1d_001, Se_uv_pc_1d_001, that is, the information after the count(1) and count(distinct uid) fields; the time information of the index operation is '20160119', that is, the fractional area after the where ds field.

Determining the aggregated object information of the current data as a dimensional feature of the current data, determining that the indicator information of the participating operation in the current data aggregation process is an indicator feature of the current data, and determining the time granularity characteristic of the current data according to the time information of the current data index operation .

In addition, the time interval represented by the time information of the above index calculation may be used as the time granularity feature of the current data, for example, the time information of the current data index operation, that is, the score area after the where field is “ds='20160119'” , the time granularity characteristic of the current data is 1, and, for example, the time information of the current data index operation, that is, the score area after the where field is “ds>='20160101'and ds<='20160107'”, the current data The time granularity feature is 7.

Step S1002: The search engine database respectively acquires a second data in a report, a knowledge base platform, a cluster physical table, and a dimensional feature of the second data.

Specifically, the search engine database respectively obtains the report, the knowledge base platform, and second data in the cluster physical table; and the search engine database determines the report and the knowledge base according to a preset algorithm. a platform and a dimensional feature of each second data in the cluster physical table.

In this embodiment, each of the data in the report, the knowledge base platform, and the cluster physical table may be split by a TF-iDF algorithm. The LDA algorithm and the TOPIC MODEL algorithm are used to extract the features of the split data, and the extracted features are used as the dimensional features of the corresponding data.

Step S1003: The search engine database stores the first data, and dimension features, index features, and time granularity features of the first data.

Step S1004: The search engine database stores the second data, and dimension features of the second data.

Dimension features are added to each of the data in the report, the knowledge base platform, and the cluster physical table, and the data after adding the dimensional features is stored in a search engine database.

In this embodiment, the data in the data application, the report, the knowledge base platform, and the cluster physical table are collected in advance into the search engine database, and the dimension feature, the index feature, and the time granularity feature are added to each collected data. At least one; when the search engine receives the search keyword input by the user, firstly splits the search keyword to obtain a dimension keyword, an index keyword, and a time granularity keyword; and then, in a pre-established search engine database, Find the data matching the dimension keyword, the index keyword and the time granularity keyword respectively, and display the matched data to the user; the user does not need to traverse each data export to perform data search, only need to input the search keyword once, the search engine The database can find out the data related to the search keyword in all data exits, thereby improving the efficiency of finding data.

FIG. 12 is a schematic structural diagram of a query terminal according to Embodiment 1 of the present disclosure. As shown in FIG. 12, the query terminal includes: a receiving unit, a processing unit, and a sending unit.

The receiving unit is configured to receive a query request of a user, where the query request includes a search keyword.

The processing unit is coupled to the receiving unit, configured to acquire a dimension keyword, an index keyword, and a time granularity keyword in the search keyword.

The sending unit is coupled to the processing unit, configured to send the dimension keyword, the index keyword, and the time granularity keyword to a search engine database, so that the search engine database acquires The first data corresponding to the dimension feature matched by the dimension keyword, the second data corresponding to the indicator feature matched by the index keyword, and the third data corresponding to the time granularity feature matched by the time granularity keyword, The search engine database pre-stores data in the data exit and characteristic information of the data, and the data export includes at least one of the following: a data application, a report, a knowledge base platform, and a cluster physical table, and the feature information At least one of the following is included: a dimensional feature, an index feature, and a time granular feature.

The receiving unit is further configured to receive the first data, the second data, and the third data that are sent by the search engine database.

The processing unit is further configured to determine target data that is fed back to the user according to the first data, the second data, and the third data.

In this embodiment, the data in the data application, the report, the knowledge base platform, and the cluster physical table are collected in advance into the search engine database, and the dimension feature, the index feature, and the time granularity feature are added to each collected data. At least one; when the search engine receives the search keyword input by the user, firstly splits the search keyword to obtain a dimension keyword, an index keyword, and a time granularity keyword; and then, in a pre-established search engine database, Find the data matching the dimension keyword, the index keyword and the time granularity keyword respectively, and display the matched data to the user; the user does not need to traverse each data export to perform data search, only need to input the search keyword once, the search engine The database can find out the data related to the search keyword in all data exits, thereby improving the efficiency of finding data.

On the basis of the embodiment shown in FIG. 12, the processing unit is specifically configured to perform word segmentation processing on the search keyword to obtain a plurality of target word segments; and query a preset mapping table according to each target word segment, where the mapping table includes a dimension. a word segmentation, an index word segmentation, and a time granularity word segmentation; determining, in the plurality of target word segments, a target word segment that matches the dimension word segment as the dimension keyword; and matching the plurality of target word segments with the target word segmentation target The word segmentation is determined as the index keyword; the target segmentation of the plurality of target word segments that matches the time granularity word segment is determined as the time granularity keyword.

Further, the processing unit is specifically configured to determine the first data, the second data, and the third number Whether the data is the same data; if the first data, the second data, and the third data are the same data, the processing unit determines the same data as target data fed back to the user; The first data, the second data, and the third data are not the same data, and the processing unit sorts the first data, the second data, and the third data, and after sorting The data is determined to be target data that is fed back to the user.

In this embodiment, a plurality of target word segments are obtained by performing word segmentation processing on the search keywords, and the dimension keywords, index keywords, and time granularity keywords in the plurality of target word segments are queried according to the pre-established mapping table, thereby improving the determined search. The efficiency of dimensional keywords, index keywords and time-granulated keywords in keywords.

FIG. 13 is a schematic structural diagram of a query terminal according to Embodiment 2 of the present disclosure. As shown in FIG. 13, the query terminal further includes: a display.

The receiving unit is further configured to receive a click operation of the target data by the user.

The processing unit is further configured to establish an association relationship between the user and the target data according to the click operation.

The display is coupled to the processing unit, and when the user does not input the search keyword, the display displays the target data associated with the association relationship.

In this embodiment, by establishing an association relationship between the user and the target data that the user clicked on, when the user does not input the search keyword, the target data that the user clicked may be displayed according to the relationship between the user and the target data, thereby improving The convenience of users to query data.

14 is a schematic structural diagram of a query terminal according to Embodiment 3 of the present disclosure. Referring to FIG. 14, the query terminal 1900 includes a processing component 1922, which further includes one or more processors, and a memory resource represented by the memory 1932, for Instructions executable by the processing component 1922, such as an application, are stored. An application stored in memory 1932 can include one or more modules each corresponding to a set of instructions. Further, the processing component 1922 is configured to execute instructions to perform the methods of steps S201-S1004 described above.

Apparatus 1900 can also include a power supply component 1926 configured to perform power management of apparatus 1900, a wired or wireless network interface 1950 configured to connect apparatus 1900 to the network, and an input/output (I/O) interface 1958. Device 1900 can operate based on an operating system stored in memory 1932, such as Windows ServerTM, Mac OS XTM, UnixTM, LinuxTM, FreeBSDTM or the like.

FIG. 15 is a schematic structural diagram of a search engine database according to an embodiment of the present disclosure. As shown in FIG. 15, the search engine database includes: a receiver, a memory, a processor, and a transmitter.

The receiver is configured to receive a dimension keyword, an index keyword, and a time granularity keyword sent by the query terminal, where the dimension keyword, the index keyword, and the time granularity keyword are the query terminal Receiving a query request of the user and obtaining it from the search keywords included in the query request.

The memory is configured to store data in a data exit, and characteristic information of the data, where the data export includes at least one of the following: a data application, a report, a knowledge base platform, and a cluster physical table, and the feature information At least one of the following is included: a dimensional feature, an index feature, and a time granular feature.

The processor is coupled to the receiver and the memory, and is configured to acquire first data corresponding to the dimensional feature matched by the dimension keyword, and second data corresponding to the index feature matched by the index keyword And third data corresponding to the time granularity feature matched by the time granularity keyword.

The transmitter is coupled to the processor, configured to send the first data, the second data, and the third data to the query terminal, so that the query terminal is configured according to the first The data, the second data, and the third data determine target data that is fed back to the user.

In this embodiment, the data in the data application, the report, the knowledge base platform, and the cluster physical table are collected in advance into the search engine database, and the dimension feature, the index feature, and the time granularity feature are added to each collected data. At least one; when the search engine receives the search keyword input by the user, firstly splits the search keyword to obtain a dimension keyword, an index keyword, and a time granularity keyword; and then, in a pre-established search engine database, Find the data matching the dimension keyword, the index keyword and the time granularity keyword respectively, and display the matched data to the user; the user does not need to traverse each data export to perform data search, only need to input the search keyword once, the search engine The database can find out the data related to the search keyword in all data exits, thereby improving the efficiency of finding data.

On the basis of the embodiment shown in FIG. 15, the processor is specifically configured to acquire access logic of the data application accessing a data source, where the access logic includes data in the data application, where the data source is stored Output logic of the data; determining feature information of data in the data application according to the output logic; storing data in the data application, and feature information of the data to the memory .

Or, on the basis of the embodiment shown in FIG. 15, the receiver is further configured to receive data sent by a parser, and dimension features, index features, and time granularity features of the data, where the parser is used in a collection center. Data in the data application, and analyzing dimensional features, index features, and time granularity features of the data; the processor is further configured to use data in the data application, and dimensional characteristics and indicators of the data Feature, time granularity features are stored to the memory.

Or the processor is specifically configured to acquire data in the report, the knowledge base platform, and the cluster physical table, respectively, according to the preset embodiment, and determine the report according to a preset algorithm. The knowledge base platform and the dimensional characteristics of each data in the cluster physical table; storing the data in the report, the knowledge base platform, and the cluster physical table, and the dimensional characteristics of the data to the Said memory.

In this embodiment, all data in the data application is stored in the search engine database, and each data stored in the search engine database from the data application is associated with dimensional features, index features, and time granularity features; in addition, the search engine The database stores all the data in the report, the knowledge base platform, and the cluster physical table, and each data association stored from the report, the knowledge base platform, and the cluster physical table to the search engine database has dimensional characteristics.

It should be noted that the above embodiments are merely illustrative of the technical solutions of the present disclosure, and are not intended to be limiting; although the present disclosure has been described in detail with reference to the foregoing embodiments, those skilled in the art will understand that The technical solutions described in the foregoing embodiments may be modified, or some or all of the technical features may be equivalently replaced; and the modifications or substitutions do not deviate from the technical solutions of the embodiments of the present disclosure. range.

Claims (29)

A data processing system, comprising: a query terminal and a search engine database; The query terminal is configured to receive a query request of a user, where the query request includes a search keyword; the query terminal acquires a dimension keyword, an index keyword, and a time granularity keyword in the search keyword, and Transmitting the dimension keyword, the index keyword, and the time granularity keyword to the search engine database; The search engine database pre-stores data in the data exit and feature information of the data, and the feature information includes at least one of the following: a dimension feature, an index feature, and a time granularity feature; The search engine database is configured to acquire first data corresponding to the dimension feature matching the dimension keyword, second data corresponding to the index feature matched by the index keyword, and match the time granularity keyword Corresponding third data corresponding to the time granularity feature, and sending the first data, the second data, and the third data to the query terminal; The query terminal is further configured to determine, according to the first data, the second data, and the third data, target data that is fed back to the user, and display the target data to the user. A data processing method, comprising: The querying terminal receives a query request of the user, where the query request includes a search keyword; The query terminal acquires a dimension keyword, an index keyword, and a time granularity keyword in the search keyword; Transmitting, by the query terminal, the dimension keyword, the index keyword, and the time granularity keyword to a search engine database, so that the search engine database obtains a dimension feature that matches the dimension keyword. The first data, the second data corresponding to the indicator feature matched by the indicator keyword, and the third data corresponding to the time granularity feature matched by the time granularity keyword, the search engine database is pre-stored in the data exit Data, and characteristic information of the data, the feature information includes at least one of the following: a dimensional feature, an index feature, and a time granularity feature; Receiving, by the query terminal, the first data, the second data, and the third data that are sent by the search engine database; The querying terminal determines target data fed back to the user according to the first data, the second data, and the third data. The method of claim 2, wherein the data outlet comprises at least one of the following: a data application, a report, a knowledge base platform, and a cluster physical table. The method according to claim 3, wherein said query terminal acquires said search keyword Dimensional keywords, indicator keywords, and time granularity keywords, including: The query terminal performs word segmentation processing on the search keyword to obtain a plurality of target word segments; The querying terminal queries a preset mapping table according to each target word segment, where the mapping table includes a dimension word segmentation, an index word segmentation, and a time granularity word segmentation; The query terminal determines, as the dimension keyword, a target word segment that matches the dimension word segment among the plurality of target word segments; The query terminal determines, as the index keyword, a target word segment that matches the indicator word segment among the plurality of target word segments; The query terminal determines a target segmentation of the plurality of target word segments that matches the time granularity word segment as the time granularity keyword. The method according to claim 4, wherein the querying terminal determines the target data fed back to the user according to the first data, the second data, and the third data, including: Determining, by the query terminal, whether the first data, the second data, and the third data are the same data; If the first data, the second data, and the third data are the same data, the querying terminal determines the same data as target data fed back to the user; If the first data, the second data, and the third data are not the same data, the query terminal sorts the first data, the second data, and the third data, and sorts The subsequent data is determined as the target data fed back to the user. The method according to claim 5, wherein the querying terminal sorts the first data, the second data, and the third data, including: The query terminal calculates a weight value of each of the first data, the second data, and the third data; The query terminal calculates a similarity between each of the first data, the second data, and the third data and the search keyword; The querying terminal calculates a sorting value of each of the data according to the weight value and the similarity of each of the data; And the querying terminal sorts each of the first data, the second data, and the third data according to the sorting value of each data. The method according to claim 6, wherein the query terminal compares each of the first data, the second data, and the third data according to a ranking value of each of the data Sort, including: Determining, by the query terminal, the first data, the second data, and the location according to the ranking value of each data Data in which the ranking value is greater than the first threshold in the third data; The querying terminal sorts the data whose sorting value is greater than the first threshold according to the size of the sorting value. The method according to any one of claims 2 to 7, wherein the query terminal determines target data fed back to the user according to the first data, the second data, and the third data. After that, it also includes: The query terminal receives a click operation of the target data by the user; The querying terminal establishes an association relationship between the user and the target data according to the click operation; When the user does not input the search keyword, the query terminal displays the target data according to the association relationship. The method according to claim 8, wherein the association relationship comprises a degree of association, the degree of association identifying a degree of association of the user with the target data; The querying terminal displays the target data according to the association relationship, including: The query terminal displays the target data whose degree of association is greater than a second threshold. A data processing method, comprising: The querying terminal receives a query request of the user, where the query request includes a search keyword; The query terminal acquires at least two types of keywords in the search keyword; The querying terminal sends at least two types of keywords to the search engine database, so that the search engine database obtains source data corresponding to the at least two types of keywords respectively; The query terminal receives the source data sent by the search engine database; The query terminal determines target data fed back to the user according to the source data. A data processing method, comprising: The search engine database receives the dimension keyword, the index keyword, and the time granularity keyword sent by the query terminal, where the dimension keyword, the index keyword, and the time granularity keyword are queries of the query terminal receiving the user. Requesting, and obtaining from the search keywords included in the query request; The search engine database pre-stores data in the data exit and feature information of the data, and the feature information includes at least one of the following: a dimension feature, an index feature, and a time granularity feature; The search engine data acquires first data corresponding to the dimensional feature matched by the dimension keyword, second data corresponding to the index feature matched by the index keyword, and time granularity matched with the time granularity keyword Third data corresponding to the feature; The search engine data transmits the first data, the second data, and the third data to the query And the terminal, so that the querying terminal determines, according to the first data, the second data, and the third data, target data that is fed back to the user. The method of claim 11, wherein the data outlet comprises at least one of the following: a data application, a report, a knowledge base platform, and a cluster physical table. The method according to claim 12, wherein before the search engine database receives the dimension keyword, the index keyword, and the time granularity keyword sent by the query terminal, the method further includes: The search engine database stores data in the data application, the report, the knowledge base platform, and the cluster physical table. The method of claim 13 wherein said search engine database stores data in said data application, comprising: The search engine database acquires access logic of the data application access data source, the access logic includes data in the data application, and the data source stores output logic of the data; Determining, by the search engine database, feature information of data in the data application according to the output logic; The search engine database stores data in the data application and feature information of the data. The method according to claim 14, wherein the output logic comprises aggregation object information of the data, indicator information of participating operations in the aggregation process, and time information of the indicator operation; Determining, by the search engine database, feature information of data in the data application according to the output logic, including: The search engine database determines that the aggregated object information of the data is a dimensional feature of the data; The index information of the search engine database determining that the data participates in the aggregation process is an indicator feature of the data; The search engine database determines time granularity characteristics of the data according to time information of the indicator operation. The method according to claim 13, wherein the search engine database stores the data in the report, the knowledge base platform, and the cluster physical table, including: The search engine database respectively acquires data in the report, the knowledge base platform, and the cluster physical table; Determining, by the search engine database, dimension characteristics of each data in the report, the knowledge base platform, and the cluster physical table according to a preset algorithm; The search engine database stores the report, the knowledge base platform, and each of the data in the cluster physical table, and dimensional characteristics of the data. A data processing method, comprising: The search engine database acquires first data in the data application, and dimension features, indicator features, and time granularity features of the first data; The search engine database respectively acquires a second data in the report, the knowledge base platform, the cluster physical table, and the dimensional characteristics of the second data; The search engine database stores the first data, and dimension features, index features, and time granularity features of the first data; The search engine database stores the second data and dimensional features of the second data. The method according to claim 17, wherein the search engine database acquires the first data in the data application, and the dimensional features, the indicator features, and the time granularity features of the first data, including: The search engine database receives the first data sent by the parser, and the dimensional feature, the index feature, and the time granularity feature of the first data, where the parser is used to collect the first in the data application. Data, and analyzing dimensional features, index features, and time granularity features of the first data. The method according to claim 17, wherein the search engine database acquires the first data in the data application, and the dimensional features, the indicator features, and the time granularity features of the first data, including: The search engine database acquires access logic of the data application access data source, the access logic includes first data in the data application, and the data source stores output logic of the first data; The search engine database determines feature information of the first data in the data application according to the output logic, and the feature information includes a dimension feature, an indicator feature, and a time granular feature. The method according to claim 19, wherein the output logic comprises aggregation object information of the first data, indicator information of participating operations in the aggregation process, and time information of the indicator operation; Determining, by the search engine database, feature information of the first data in the data application according to the output logic, including: The search engine database determines that the aggregated object information of the first data is a dimensional feature of the first data; Determining, by the search engine database, indicator information that the first data participates in the aggregation process is an indicator feature of the first data; The search engine database determines time granularity of the first data according to time information of the indicator operation feature. The method according to any one of claims 17 to 20, wherein the search engine database respectively acquires a second data in a report, a knowledge base platform, a cluster physical table, and a dimensional feature of the second data, include: The search engine database respectively acquires the report, the knowledge base platform, and the second data in the cluster physical table; The search engine database determines, according to a preset algorithm, a dimensional feature of each of the second data in the report, the knowledge base platform, and the cluster physical table. An Querying Terminal, comprising: a receiving unit, a processing unit, and a sending unit; The receiving unit is configured to receive a query request of a user, where the query request includes a search keyword; The processing unit is coupled to the receiving unit, configured to acquire a dimension keyword, an index keyword, and a time granularity keyword in the search keyword; The sending unit is coupled to the processing unit, configured to send the dimension keyword, the index keyword, and the time granularity keyword to a search engine database, so that the search engine database acquires The first data corresponding to the dimension feature matched by the dimension keyword, the second data corresponding to the indicator feature matched by the index keyword, and the third data corresponding to the time granularity feature matched by the time granularity keyword, The search engine database pre-stores data in the data exit and characteristic information of the data, and the data export includes at least one of the following: a data application, a report, a knowledge base platform, and a cluster physical table, and the feature information Including at least one of the following: dimensional features, indicator features, and time granularity features; The receiving unit is further configured to receive the first data, the second data, and the third data that are sent by the search engine database; The processing unit is further configured to determine target data that is fed back to the user according to the first data, the second data, and the third data. The query terminal according to claim 22, wherein the processing unit is specifically configured to perform word segmentation processing on the search keyword to obtain a plurality of target word segments; and query a preset mapping table according to each target word segment, the mapping The table includes a dimension word segmentation, an index word segmentation, and a time granularity word segmentation; determining, in the plurality of target word segments, the target word segment that matches the dimension word segment as the dimension keyword; and dividing the plurality of target word segments with the indicator word segmentation The matched target participle is determined as the index keyword; the target participle matching the time granularity participle among the plurality of target participles is determined as the time granularity keyword. The query terminal according to claim 23, wherein the processing unit is specifically configured to determine Whether the first data, the second data, and the third data are the same data; If the first data, the second data, and the third data are the same data, the processing unit determines the same data as target data fed back to the user; If the first data, the second data, and the third data are not the same data, the processing unit sorts the first data, the second data, and the third data, and sorts The subsequent data is determined as the target data fed back to the user. The query terminal according to claim 24, further comprising: a display; The receiving unit is further configured to receive a click operation of the target data by the user; The processing unit is further configured to establish an association relationship between the user and the target data according to the click operation; The display is coupled to the processing unit, and when the user does not input the search keyword, the display displays the target data associated with the association relationship. A search engine database, comprising: a receiver, a memory, a processor, and a transmitter; The receiver is configured to receive a dimension keyword, an index keyword, and a time granularity keyword sent by the query terminal, where the dimension keyword, the index keyword, and the time granularity keyword are the query terminal Receiving a query request of the user, and obtaining the search keyword included in the query request; The memory is configured to store data in a data exit, and characteristic information of the data, where the data export includes at least one of the following: a data application, a report, a knowledge base platform, and a cluster physical table, and the feature information Including at least one of the following: dimensional features, indicator features, and time granularity features; The processor is coupled to the receiver and the memory, and is configured to acquire first data corresponding to the dimensional feature matched by the dimension keyword, and second data corresponding to the index feature matched by the index keyword And third data corresponding to the time granularity feature matching the time granularity keyword; The transmitter is coupled to the processor, configured to send the first data, the second data, and the third data to the query terminal, so that the query terminal is configured according to the first The data, the second data, and the third data determine target data that is fed back to the user. The search engine database according to claim 26, wherein the processor is specifically configured to acquire access logic of the data application accessing a data source, where the access logic includes data in the data application, The data source stores output logic of the data; determining, according to the output logic, feature information of data in the data application; data in the data application, and feature information of the data Stored to the memory. The search engine database according to claim 26, wherein the receiver is further configured to receive data sent by the parser, and dimension features, index features, and time granularity features of the data, and the syntax parser uses Collecting data in the data application, and analyzing dimensional features, index features, and time granularity characteristics of the data; The processor is further configured to store data in the data application, and dimension features, indicator features, and time granular features of the data to the memory. The search engine database according to claim 26, wherein the processor is specifically configured to separately acquire data in the report, the knowledge base platform, and the cluster physical table; and determine, according to a preset algorithm, a report, the knowledge base platform, and dimensional characteristics of each data in the cluster physical table; each of the data in the report, the knowledge base platform, and the cluster physical table, and dimensional characteristics of the data Stored to the memory.

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