Defense Date


Document Type


Degree Name

Doctor of Philosophy



First Advisor

Victoria Yoon

Second Advisor

Allen Lee

Third Advisor

Manoj Thomas

Fourth Advisor

Christopher Whyte


Terrorist attacks can cause massive casualties and severe property damage, resulting in terrorism crises surging across the world; accordingly, counter-terrorism analytics that take advantage of big data have been attracting increasing attention. The knowledge and clues essential for analyzing terrorist activities are often spread across heterogeneous data sources, which calls for an effective data integration solution. In this study, employing the goal definition template in the Goal-Question-Metric approach, we design and implement an automated goal-driven data integration framework for counter-terrorism analytics. The proposed design elicits and ontologizes an input user goal of counter-terrorism analytics; recognizes goal-relevant datasets; and addresses semantic heterogeneity in the recognized datasets. Our proposed design, following the design science methodology, presents a theoretical framing for on-demand data integration designs that can accommodate diverse and dynamic user goals of counter-terrorism analytics and output integrated data tailored to these goals.


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Date of Submission


Available for download on Monday, July 29, 2024


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