In this dissertation we investigate the opportunities and challenges involved in efficiently leveraging relationships within data stored in structured databases. First, we present GraphGen, a lightweight software layer that is independent from the underlying database, and provides interfaces for graph analysis of data in RDBMSs. GraphGen introduces an intuitive high-level language for specifying graphs of interest, and utilizes in-memory graph representations to tackle the problems associated with analyzing graphs that are hidden inside structured datasets. We show GraphGen can analyze such graphs in orders of magnitude less memory, and often computation time, while eliminating manual Extract-Transform-Load (ETL) effort.
Second, we examine how in-memory graph representations of RDBMS data can be used to enhance relational query processing. We present a novel, general framework for executing GROUP BY aggregation over conjunctive queries which avoids materialization of intermediate JOIN results, and wrap this framework inside a multi-way relational operator called Join-Agg. We observed that Join-Agg can compute aggregates over certain relational and graph queries using orders of magnitude less memory and computation time.
Dean's rep: Dr. Louiqa Raschid
Members: Dr. Daniel Abadi
Dr. Peter Keleher
Dr. Leilani Battle
Konstantinos Xirogiannopoulos is a PhD candidate in the Department of Computer Science at the University of Maryland, College Park. His research interests include database management systems, graph analytics systems and solving problems in data-intensive applications.