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Talks & Seminars
Title: GraphGen: Adaptive Graph Extraction and Analytics Over Relational Databases
Prof. Amol Deshpande, University of Maryland
Date & Time: February 26, 2018 10:00
Venue: Conference Room, C Block, 01st Floor, Department of Computer Science and Engineering, Kanwal Rekhi (KReSIT) Building
Abstract:
Graph querying and analytics are becoming an increasingly important component of the arsenal of tools for extracting different kinds of insights from data. However, graphs are not the primary representation choice for most data today, and users who want to employ graph analytics are forced to extract data from their data stores, construct the requisite graphs, and then use a specialized engine to write and execute their graph analysis tasks. This cumbersome and costly process not only raises barriers in using graph analytics, but also makes it hard to explore and identify hidden or implicit graphs in the data. In this talk, I will present our ongoing work on an end-to-end graph analysis framework, called GraphGen, that sits atop an RDBMS and enables users to declaratively specify graph extraction tasks, visually explore the extracted graphs, and write and execute graph algorithms over them, either directly or using existing graph libraries like the widely used NetworkX Python library. GraphGen has a fundamentally different goal from recent work on using relational databases to store graph data through "shredding". Instead, GraphGen is intended to analyze graphs that are present in existing relational databases. GraphGen attempts to utilize the underlying relational database to the full extent possible by pushing down computation, uses a novel condensed representation to handle graphs that may be too large to extract in their entirety, allows writing programs using a general subgraph-centric API, and features several optimizations for efficient extraction and querying of large graphs.
Speaker Profile:
Amol Deshpande is a Professor in the Department of Computer Science at the University of Maryland with a joint appointment in the University of Maryland Institute for Advanced Computer Studies (UMIACS). He received his Ph.D. from University of California at Berkeley in 2004. His research interests include uncertain data management, adaptive query processing, data streams, graph analytics, and sensor networks. He is a recipient of an NSF Career award, and has received best paper awards at the VLDB 2004, EWSN 2008, and VLDB 2009 conferences.
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