| Effortless data exploration with zenvisage: an expressive and interactive visual analytics system T Siddiqui, A Kim, J Lee, K Karahalios, A Parameswaran VLDB 2016, 2016 | 209 | 2016 |
| Towards visualization recommendation systems M Vartak, S Huang, T Siddiqui, S Madden, A Parameswaran ACM SIGMOD Record 45 (4), 34-39, 2017 | 187 | 2017 |
| Cost Models for Big Data Query Processing: Learning, Retrofitting, and Our Findings T Siddiqui, A Jindal, S Qiao, H Patel ACM SIGMOD 2020, 2020 | 129 | 2020 |
| Budget-aware Index Tuning with Reinforcement Learning W Wu, C Wang, T Siddiqui, J Wang, V Narasayya, S Chaudhuri, ... SIGMOD 2022, 1528-1541, 2022 | 52 | 2022 |
| You can't always sketch what you want: Understanding Sensemaking in Visual Query Systems DJL Lee, J Lee, T Siddiqui, J Kim, K Karahalios, A Parameswaran IEEE TVCG 2019, 2019 | 48 | 2019 |
| ShapeSearch: A Flexible and Efficient System for Shape-based Exploration of Trendlines T Siddiqui, Z Wang, P Luh, K Karahalios, A Parameswaran ACM SIGMOD 2020 (Awarded Best Paper), 2020 | 43 | 2020 |
| Fast-Forwarding to Desired Visualizations with Zenvisage. T Siddiqui, J Lee, A Kim, E Xue, X Yu, S Zou, L Guo, C Liu, C Wang, ... CIDR 2017, 2017 | 41 | 2017 |
| FacetGist: Collective extraction of document facets in large technical corpora T Siddiqui, X Ren, A Parameswaran, J Han ACM CIKM 2016, 2016 | 36 | 2016 |
| ISUM: Efficiently Compressing Large and Complex Workloads for Scalable Index Tuning T Siddiqui, S Jo, W Wu, C Wang, V Narasayya, S Chaudhuri SIGMOD 2022, 660-673, 2022 | 35 | 2022 |
| DISTILL: Low-Overhead Data-Driven Techniques for Filtering and Costing Indexes for Scalable Index Tuning T Siddiqui, W Wu, V Narasayya, S Chaudhuri VLDB 2022, 2022 | 29 | 2022 |
| Learned resource consumption model for optimizing big data queries TA Siddiqui, A Jindal, HS Patel US Patent App. 16/511,966, 2020 | 28 | 2020 |
| ML-powered index tuning: An overview of recent progress and open challenges T Siddiqui, W Wu ACM SIGMOD Record 52 (4), 19-30, 2024 | 18 | 2024 |
| Optimally leveraging density and locality for exploratory browsing and sampling A Kim, L Xu, T Siddiqui, S Huang, S Madden, A Parameswaran Proceedings of the Workshop on Human-In-the-Loop Data Analytics, 1-7, 2018 | 18 | 2018 |
| ShapeSearch: Flexible Pattern-based Querying of Trend Line Visualizations T Siddiqui, P Luh, Z Wang, K Karahalios, A Parameswaran VLDB 2018, 2018 | 14 | 2018 |
| Wred: Workload Reduction for Scalable Index Tuning M Brucato, T Siddiqui, W Wu, V Narasayya, S Chaudhuri SIGMOD 2024 2 (1), 1-26, 2024 | 13 | 2024 |
| Accelerating scientific data exploration via visual query systems DJL Lee, J Lee, T Siddiqui, J Kim, K Karahalios, A Parameswaran arXiv preprint arXiv:1710.00763, 2017 | 13* | 2017 |
| COMPARE: Accelerating Groupwise Comparison in Relational Databases for Data Analytics T Siddiqui, S Chaudhuri, V Narasayya VLDB 2021, 2021 | 12 | 2021 |
| SIBYL: Forecasting Time-Evolving Query Workloads H Huang, T Siddiqui, R Alotaibi, C Curino, J Leeka, A Jindal, J Zhao, ... SIGMOD 2024, 2024 | 11* | 2024 |
| Expressive querying for accelerating visual analytics T Siddiqui, P Luh, Z Wang, K Karahalios, AG Parameswaran Communications of the ACM 65 (7), 85-94, 2022 | 11* | 2022 |
| Scalable index tuning with index filtering and index cost models TA Siddiqui, VR Narasayya, S Chaudhuri, W Wu US Patent 12,248,454, 2025 | 6 | 2025 |
Aditya ParameswaranAssociate Professor, EECS, UC BerkeleyVerified email at berkeley.edu
