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A Session-Based Approach to Fast-But-Approximate Interactive Data Cube Exploration

Authors:

Niranjan Kamat,

Arnab NandiAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 12, Issue 1

Article No.: 9, Pages 1 - 26

https://doi.org/10.1145/3070648

Published: 13 February 2018 Publication History

Abstract

With the proliferation of large datasets, sampling has become pervasive in data analysis. Sampling has numerous benefits—from reducing the computation time and cost to increasing the scope of interactive analysis. A popular task in data science, well-suited toward sampling, is the computation of fast-but-approximate aggregations over sampled data. Aggregation is a foundational block of data analysis, with data cube being its primary construct. We observe that such aggregation queries are typically issued in an ad-hoc, interactive setting. In contrast to one-off queries, a typical query session consists of a series of quick queries, interspersed with the user inspecting the results and formulating the next query. The similarity between session queries opens up opportunities for reusing computation of not just query results, but also error estimates. Error estimates need to be provided alongside sampled results for the results to be meaningful. We propose Sesame, a rewrite and caching framework that accelerates the entire interactive <underline>ses</underline>sion of aggregation queries over <underline>sam</underline>pl<underline>e</underline>d data. We focus on two unique and computationally expensive aspects of this use case: query speculation in the presence of sampling, and error computation, and provide novel strategies for result and error reuse. We demonstrate that our approach outperforms conventional sampled aggregation techniques by at least an order of magnitude, without modifying the underlying database.

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Cited By

Battle LScheidegger C(2021)A Structured Review of Data Management Technology for Interactive Visualization and AnalysisIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2020.302889127:2(1128-1138)Online publication date: Feb-2021
https://doi.org/10.1109/TVCG.2020.3028891
Zhang MWang H(2021)LAQP: Learning-based approximate query processingInformation Sciences10.1016/j.ins.2020.09.070546(1113-1134)Online publication date: Feb-2021
https://doi.org/10.1016/j.ins.2020.09.070
Rahman PNandi AHebert C(2020)Amplifying Domain Expertise in Clinical Data PipelinesJMIR Medical Informatics10.2196/196128:11(e19612)Online publication date: 5-Nov-2020
https://doi.org/10.2196/19612
Show More Cited By

Index Terms

A Session-Based Approach to Fast-But-Approximate Interactive Data Cube Exploration
1. Human-centered computing
  1. Interaction design
    1. Interaction design process and methods
      1. Activity centered design
2. Information systems
  1. Data management systems
    1. Database management system engines
      1. Online analytical processing engines

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cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 12, Issue 1

Special Issue (IDEA) and Regular Papers

February 2018

363 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/3178542

Editors:
Charu Aggarwal
IBM T. J. Watson Research, USA
,
Xindong Wu
University of Louisiana at Lafayette, USA

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Copyright © 2018 ACM.

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Publication History

Published: 13 February 2018

Accepted: 01 March 2017

Revised: 01 January 2017

Received: 01 December 2015

Published in TKDD Volume 12, Issue 1

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Cited By

Battle LScheidegger C(2021)A Structured Review of Data Management Technology for Interactive Visualization and AnalysisIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2020.302889127:2(1128-1138)Online publication date: Feb-2021
https://doi.org/10.1109/TVCG.2020.3028891
Zhang MWang H(2021)LAQP: Learning-based approximate query processingInformation Sciences10.1016/j.ins.2020.09.070546(1113-1134)Online publication date: Feb-2021
https://doi.org/10.1016/j.ins.2020.09.070
Rahman PNandi AHebert C(2020)Amplifying Domain Expertise in Clinical Data PipelinesJMIR Medical Informatics10.2196/196128:11(e19612)Online publication date: 5-Nov-2020
https://doi.org/10.2196/19612
Lu MWong EBarajas DLi XOgundipe MWilson NGarg PJoshi AMalensek M(2020)AGAMI: Scalable Visual Analytics over Multidimensional Data Streams2020 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT)10.1109/BDCAT50828.2020.00020(57-66)Online publication date: Dec-2020
https://doi.org/10.1109/BDCAT50828.2020.00020
Budiu MGopalan PSuresh LWieder UKruiger HAguilera M(2019)HillviewProceedings of the VLDB Endowment10.14778/3342263.334227912:11(1442-1457)Online publication date: 1-Jul-2019
https://dl.acm.org/doi/10.14778/3342263.3342279
Bagozi A(2019)IDEAaS: Interactive Data Exploration As-a Service2019 IEEE World Congress on Services (SERVICES)10.1109/SERVICES.2019.00096(345-348)Online publication date: Jul-2019
https://doi.org/10.1109/SERVICES.2019.00096
Bagozi ABianchini DDe Antonellis VGarda MMarini A(2019)A Relevance-based approach for Big Data ExplorationFuture Generation Computer Systems10.1016/j.future.2019.05.056Online publication date: May-2019
https://doi.org/10.1016/j.future.2019.05.056
Peng JZhang DWang JPei JDas GJermaine CBernstein P(2018)AQP++Proceedings of the 2018 International Conference on Management of Data10.1145/3183713.3183747(1477-1492)Online publication date: 27-May-2018
https://dl.acm.org/doi/10.1145/3183713.3183747
Kamat NNandi AChoudhary AWu KDong B(2017)A Unified Correlation-based Approach to Sampling Over JoinsProceedings of the 29th International Conference on Scientific and Statistical Database Management10.1145/3085504.3085524(1-12)Online publication date: 27-Jun-2017
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