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Tarique Siddiqui
Tarique Siddiqui
Senior Researcher, Microsoft Research
Verified email at microsoft.com - Homepage
Title
Cited by
Cited by
Year
Effortless data exploration with zenvisage: an expressive and interactive visual analytics system
T Siddiqui, A Kim, J Lee, K Karahalios, A Parameswaran
PVLDB 2016, 2016
1292016
Towards visualization recommendation systems
M Vartak, S Huang, T Siddiqui, S Madden, A Parameswaran
ACM SIGMOD Record 45 (4), 34-39, 2017
1252017
Cost Models for Big Data Query Processing: Learning, Retrofitting, and Our Findings
T Siddiqui, A Jindal, S Qiao, H Patel
ACM SIGMOD 2020, 2020
342020
FacetGist: Collective extraction of document facets in large technical corpora
T Siddiqui, X Ren, A Parameswaran, J Han
ACM CIKM 2016, 2016
312016
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
242017
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
222019
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
142020
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
142018
ShapeSearch: Flexible Pattern-based Querying of Trend Line Visualizations
T Siddiqui, P Luh, Z Wang, K Karahalios, A Parameswaran
PVLDB 2018, 2018
112018
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
92017
Optimally leveraging density and locality to support limit queries
A Kim, L Xu, T Siddiqui, S Huang, S Madden, A Parameswaran
arXiv preprint arXiv:1611.04705, 2016
42016
COMPARE: Accelerating Groupwise Comparison in Relational Databases for Data Analytics
T Siddiqui, S Chaudhuri, V Narasayya
VLDB 2021, 2021
32021
Speedy browsing and sampling with needletail
A Kim, L Xu, T Siddiqui, S Huang, S Madden, A Parameswaran
CoRR, 2016
32016
Budget-aware Index Tuning with Reinforcement Learning
W Wu, C Wang, T Siddiqui, J Wang, V Narasayya, S Chaudhuri, ...
Proceedings of the 2022 International Conference on Management of Data, 1528 …, 2022
12022
ISUM: Efficiently Compressing Large and Complex Workloads for Scalable Index Tuning
T Siddiqui, S Jo, W Wu, C Wang, V Narasayya, S Chaudhuri
Proceedings of the 2022 International Conference on Management of Data, 660-673, 2022
12022
From Sketching to Natural Language: Expressive Visual Querying for Accelerating Insight
T Siddiqui, P Luh, Z Wang, K Karahalios, AG Parameswaran
ACM SIGMOD Record 50 (1), 51-58, 2021
12021
Learned resource consumption model for optimizing big data queries
TA Siddiqui, A Jindal, Q Shi, HS Patel
US Patent App. 16/511,966, 2020
12020
Three lessons from accelerating scientific insight discovery via visual querying
DJL Lee, T Siddiqui, K Karahalios, A Parameswaran
Patterns 1 (7), 100126, 2020
12020
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
2022
ISUM: Efficiently Compressing Large and Complex Workloads for Scalable Index Tuning (Extended Version)
T Siddiqui, S Jo, W Wu, C Wang, V Narasayya, S Chaudhuri
2022
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