Extracting and utilizing hidden structures in large datasets

The hidden structure within datasets --- capturing the inherent structure within the data not explicitly captured or encoded in the data format --- can often be automatically extracted and used to improve various data processing applications. Utilizing such hidden structure enables us to potentially surpass traditional algorithms that do not take this structure into account. In this thesis, we propose a general framework for algorithms that automatically extract and employ hidden structures to improve data processing performance, and discuss a set of design principles for developing such algorithms. We provide three examples to demonstrate the power of this framework in practice, showcasing how we can use hidden structures to either outperform state-of-the-art methods, or enable new applications that are previously impossible. We believe that this framework can offer new opportunities for the design of algorithms that surpass the current limit, and empower new applications in database research and many other data-centric disciplines.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Computer Science

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2019

Author dc:creator

• Gao, Yihan

Contributors dc:contributor

• Parameswaran, Aditya
• Chang, Kevin
• Sundaram, Hari
• Wang, Jiannan

• Structure Extraction
• Automatic Data Processing

Statement dc:rights

• Copyright 2019 Yihan Gao

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/104764

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/104764