University of Illinois Urbana-Champaign

Accelerating queries for structured and unstructured data

Jin, Tengjun

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https://hdl.handle.net/2142/124567

Description

Title
Accelerating queries for structured and unstructured data
Author(s)
Jin, Tengjun
Issue Date
2024-04-30
Director of Research (if dissertation) or Advisor (if thesis)
Kang, Daniel
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Keyword(s)
  • Database Systems
  • Machine Learning
  • Approximate Query Processing
Abstract
Data analytics is important for making data-driven decisions. As data volumes expand, the efficiency and cost of executing queries become critical concerns for analysts. Traditionally, analytics systems have prioritized structured data. Approximate Query Processing (AQP) systems, which provide faster aggregation queries by delivering approximate results, have been developed to enhance efficiency. However, they are limited used in real-application due to compatibility issues with popular databases and restrictions on the types of queries they can handle. To overcome these limitations, we have designed an innovative AQP system that functions as middleware. This system uses online sampling techniques to accelerate aggregation queries and can meet user-specified error targets. With advancements in machine learning (ML), analysts are increasingly interested in analyzing unstructured data (videos, images, text, and audio) to extract semantic information. Current analytics systems typically integrate ML models through user-defined functions (UDFs). These UDFs can be difficult to optimize and require application users to write complex, nested table expressions. To address these challenges, we introduce a new data model, AIDM, enabling users to query ML model outputs as standard SQL tables, through virtual columns and virtual tables. We implement AIDM, as well as novel optimizations for accelerating both approximate and exact queries in AIDB. Our evaluations show that the AQP system can provide speedups of up to 87x and AIDB can reduce the number of ML model invocations by up to 98%.
Graduation Semester
2024-05
Type of Resource
Thesis
Copyright and License Information
Copyright 2024 Tengjun Jin

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