Query Result Exploration featuring Dr. Murali Mani
Users typically interact with a database by asking queries and examining the results. We refer to the user examining the query results and asking follow-up questions as query result exploration. The problem of query result exploration can utilize almost two decades of work done in provenance by the database community. Three approaches for computing provenance have been described in the literature: lazy approach, eager approach, and hybrid approach. In our work, we investigate lazy and eager approaches that utilize constraints that we have identified in the context of query result exploration, as well as novel hybrid approaches. We have evaluated our approaches against the TPC-H benchmark. We find that our approaches are applicable to 19 out of the 22 queries, and result in a better performance for all queries that have a join. Further, the performance benefit from our approaches are significant, sometimes several orders of magnitude.
Murali Mani is an associate professor of Computer Science at the University of Michigan, Flint. He received his Ph.D. in Computer Science from UCLA in 2003. Prior to that, he has received his MS in Computer Science from UCLA, and his Bachelor's degree in Computer Science and Engineering from IIT Madras. Some of his research projects have been on XML modeling and processing, stream processing, provenance, secure query processing in the cloud, and big data processing.
Thursday, July 16 at 7:30 PM CT.
Please email Dr. Works (email@example.com) if you wish to participate.
Thursday, July 16, 2020 at 7:30pmVirtual Event