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PhD Defense: Collective Relational Data Integration with Diverse and Noisy Evidence
Alex Memory
Friday, October 11, 2019, 3:00-5:00 pm Calendar
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Driven by the growth of the Internet, online applications and data sharing initiatives, available structured data sources are now vast in number. There is a growing need to integrate these structured sources to support a variety data science tasks, including predictive analysis, data mining, improving search results, and generating recommendations. A particularly important integration challenge is dealing with the heterogeneous structures of relational data sources. In addition to the large number of sources – both individual sources and their versions over time – the difficulty also lies in the growing complexity of sources, and in the noise and ambiguity present in real-world sources. Existing automated integration approaches handle the number and complexity of sources, but nearly all are based on brittle technologies that cannot handle noise and ambiguity. Corresponding progress has been made in probabilistic learning approaches to handle noise and ambiguity in inputs, but until recently those technologies have not scaled to the size and complexity of relational data integration problems. This dissertation addresses fundamental challenges arising from this gap in existing approaches, and demonstrates promising new relational data integration approaches employing collective, probabilistic reasoning to handle inputs that can be diverse, noisy, and ambiguous.

Examining Committee: 
                          Chair:               Dr. Lise Getoor
                          Co-Chair:        Dr. Dana Nau
                          Dean's rep:      Dr. Louiqa Raschid
                          Members:        Dr. Héctor Corrada Bravo
                                                    Dr. Alan Ritter

Alex Memory is a PhD student in the Department of Computer Science at the University of Maryland, College Park, and a Senior Research Scientist at Leidos. His research interests include machine learning, data integration, and anomaly detection.

This talk is organized by Tom Hurst