research-article

A Step Toward Deep Online Aggregation

Authors:

Nikhil Sheoran,

Supawit Chockchowwat,

Yongjoo ParkAuthors Info & Claims

Proceedings of the ACM on Management of Data, Volume 1, Issue 2

Article No.: 124, Pages 1 - 28

https://doi.org/10.1145/3589269

Published: 20 June 2023 Publication History

Abstract

For exploratory data analysis, it is often desirable to know what answers you are likely to get before actually obtaining those answers. This can potentially be achieved by designing systems to offer the estimates of a data operation result-say op(data)-earlier in the process based on partial data processing. Those estimates continuously refine as more data is processed and finally converge to the exact answer. Unfortunately, the existing techniques-called Online Aggregation (OLA)-are limited to a single operation; that is, we cannot obtain the estimates for op(op(data)) or op(...(op(data))). If this Deep OLA becomes possible, data analysts will be able to explore data more interactively using complex cascade operations.

In this work, we take a step toward Deep OLA with evolving data frames (edf), a novel data model to offer OLA for nested ops-op(...(op(data)))-by representing an evolving structured data (with converging estimates) that is closed under set operations. That is, op(edf) produces yet another edf; thus, we can freely apply successive operations to edf and obtain an OLA output for each op. We evaluate its viability with Wake, an edf-based OLA system, by examining against state-of-the-art OLA and non-OLA systems. In our experiments on TPC-H dataset, Wake produces its first estimates 4.93× faster (median)-with 1.3× median slowdown for exact answers-compared to conventional systems. Besides its generality, Wake is also 1.92× faster (median) than existing OLA systems in producing estimates of under 1% relative errors.

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Cited By

Zhang HJing YHe ZZhang KWang X(2024)Learning-Based Sample Tuning for Approximate Query Processing in Interactive Data ExplorationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.334145136:11(6532-6546)Online publication date: Nov-2024
https://doi.org/10.1109/TKDE.2023.3341451
Chockchowwat SLi ZPark YBoehm MHulsebos MShankar SVarma P(2023)Transactional Python for Durable Machine Learning: Vision, Challenges, and FeasibilityProceedings of the Seventh Workshop on Data Management for End-to-End Machine Learning10.1145/3595360.3595855(1-5)Online publication date: 18-Jun-2023
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https://doi.org/10.1109/ICDE55515.2023.00393

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A Step Toward Deep Online Aggregation

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cover image Proceedings of the ACM on Management of Data

Proceedings of the ACM on Management of Data Volume 1, Issue 2

PACMMOD

June 2023

2310 pages

EISSN:2836-6573

DOI:10.1145/3605748

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Zhang HJing YHe ZZhang KWang X(2024)Learning-Based Sample Tuning for Approximate Query Processing in Interactive Data ExplorationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.334145136:11(6532-6546)Online publication date: Nov-2024
https://doi.org/10.1109/TKDE.2023.3341451
Chockchowwat SLi ZPark YBoehm MHulsebos MShankar SVarma P(2023)Transactional Python for Durable Machine Learning: Vision, Challenges, and FeasibilityProceedings of the Seventh Workshop on Data Management for End-to-End Machine Learning10.1145/3595360.3595855(1-5)Online publication date: 18-Jun-2023
https://dl.acm.org/doi/10.1145/3595360.3595855
Li ZPi XPark Y(2023)S/C: Speeding up Data Materialization with Bounded Memory2023 IEEE 39th International Conference on Data Engineering (ICDE)10.1109/ICDE55515.2023.00393(1981-1994)Online publication date: Apr-2023
https://doi.org/10.1109/ICDE55515.2023.00393

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