Analyzing intentions from big data traces of human activities

Zhang, Aston

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https://hdl.handle.net/2142/97738 Copy

Description

Title

Analyzing intentions from big data traces of human activities

Author(s)

Zhang, Aston

Issue Date

2017-04-20

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

• Gunter, Carl A.
• Han, Jiawei

Doctoral Committee Chair(s)

• Gunter, Carl A.
• Han, Jiawei

Committee Member(s)

• Zhai, ChengXiang
• Baeza-Yates, Ricardo

Department of Study

Computer Science

Discipline

Computer Science

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

• Data mining
• Machine learning
• Optimization
• Privacy

Abstract

The rapid growth of big data formed by human activities makes research on intention analysis both challenging and rewarding. We study multifaceted problems in analyzing intentions from big data traces of human activities, and such problems span a range of machine learning, optimization, and security and privacy. We show that analyzing intentions from industry-scale human activity big data can effectively improve the accuracy of computational models. Specifically, we take query auto-completion as a case study. We identify two hitherto-undiscovered problems: adaptive query auto-completion and mobile query auto-completion. We develop two computational models by analyzing intentions from big data traces of human activities on search interface interactions and on mobile application usage respectively. Solving the large-scale optimization problems in the proposed query auto-completion models drives deeper studies of the solvers. Hence, we consider the generalized machine learning problem settings and focus on developing lightweight stochastic algorithms as solvers to the large-scale convex optimization problems with theoretical guarantees. For optimizing strongly convex objectives, we design an accelerated stochastic block coordinate descent method with optimal sampling; for optimizing non-strongly convex objectives, we design a stochastic variance reduced alternating direction method of multipliers with the doubling-trick. Inevitably, human activities are human-centric, thus its research can inform security and privacy. On one hand, intention analysis research from human activities can be motivated from the security perspective. For instance, to reduce false alarms of medical service providers' suspicious accesses to electronic health records, we discover potential de facto diagnosis specialties that reflect such providers' genuine and permissible intentions of accessing records with certain diagnoses. On the other hand, we examine the privacy risk in anonymized heterogeneous information networks representing large-scale human activities, such as in social networking. Such data are released for external researchers to improve the prediction accuracy for users' online social networking intentions on the publishers' microblogging site. We show a negative result that makes a compelling argument: privacy must be a central goal for sensitive human activity data publishers.

Graduation Semester

2017-05

Type of Resource

text

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http://hdl.handle.net/2142/97738

Copyright and License Information

Copyright 2017 Aston Zhang

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