Graduation Year

2026

Document Type

Dissertation

Degree

Ph.D.

Degree Name

Doctor of Philosophy (Ph.D.)

Degree Granting Department

Computer Science and Engineering

Major Professor

Jay Ligatti, Ph.D.

Committee Member

Lingyao Li, Ph.D.

Committee Member

Sriram Chellappan, Ph.D.

Committee Member

Xinming (Simon) Ou, Ph.D.

Committee Member

Achilleas Kourtellis, Ph.D.

Keywords

Adverse Drug Reactions (ADR), Centralized vs. Distributed Information Systems, Computational Risk Assessment, Geometric Overlap Score (GOS), SQL-Identifier Injection Vulnerabilities (SQL-IDIV)

Abstract

Risk detection in large scale information systems increasingly depends on heterogeneous data generatedby both centralized and distributed ecosystems. While centralized systems provide curated and validated reports, distributed environments produce large-scale and real-time observational evidence. Existing computational approaches analyze these ecosystems in isolation, limiting systematic comparison of risk repre-sentations across heterogeneous sources.

This dissertation presents a unified computational framework for comparative risk detection across centralized and distributed information systems. The framework provides a domain independent methodology for transforming heterogeneous risk reporting data into comparable multidimensional representations. To enable interpretable comparison of heterogeneous risk distributions, this work introduces the Geometric Overlap Score (GOS), a novel geometric similarity metric that quantifies overlap between multidimensional feature distributions. The framework further incorporates origami and radar based geometric representations that support scalable visualization and analysis of complex risk reporting datasets.

The proposed methodology is evaluated through two large scale case studies. In cybersecurity, SQL-Identifier Injection Vulnerabilities are analyzed across 4,762,175 source code files from 944,316 GitHub repositories and compared with 3,757 SQL injection related Common Vulnerabilities and Exposures records. In healthcare, natural language processing techniques are applied to 859,751 semaglutide related social media posts to analyze sentiment, discussion topics, and adverse drug reactions, which are compared with FDA clinical trial data. The results demonstrate that centralized and distributed ecosystems provide complementary risk perspectives and that comparative analysis reveals insights not observable from either ecosystem independently. These findings establish a generalizable methodology for multidimensional risk comparison across heterogeneous information systems.

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