DeepOLA: An online aggregation approach to approximating deeply nested queries

Sheoran, Nikhil

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https://hdl.handle.net/2142/117660 Copy

Description

Title

DeepOLA: An online aggregation approach to approximating deeply nested queries

Author(s)

Sheoran, Nikhil

Issue Date

2022-12-05

Director of Research (if dissertation) or Advisor (if thesis)

Park, Yongjoo

Department of Study

Computer Science

Discipline

Computer Science

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

M.S.

Degree Level

Thesis

Keyword(s)

• Online Aggregation
• Approximate Query Processing

Language

eng

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 DeepOLA, an edf-based OLA system, by examining against state-of-the-art OLA and non-OLA systems. In our experiments on TPC-H dataset, DeepOLA produces its first estimates 12.8× faster (median) — with 1.3× median slowdown for exact answers — compared to conventional systems, while even producing faster exact results for some queries (i.e. no overhead). Besides its generality, DeepOLA is also 1.92× faster (median) than existing OLA systems in producing estimates of under 1% relative errors.

Graduation Semester

2022-12

Type of Resource

Thesis

Handle URL

https://hdl.handle.net/2142/117660

Copyright and License Information

Copyright 2022 Nikhil Sheoran

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