iJoin: Importance-Aware Join Approximation over Data Streams

Authors:
Dhananjay Kulkarni;Chinya V. Ravishankar
Affiliations:
Boston University, USA;University of California - Riverside, USA
Venue:
SSDBM '08 Proceedings of the 20th international conference on Scientific and Statistical Database Management
Year:
2008

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Abstract

We address approximate join processing over data streams when memory limitations cause incoming tuples to overflow the available memory, precluding exact processing. Moreover, in many real-world applications such as for news-feeds and sensor-data, different tuples may have different importancelevels. Current methods pay little attention to load-shedding when tuples bear such importance semantics, and perform poorly due to premature tupledrops and unproductive tupleretention. We propose a novel framework, called iJoin, which overcomes these drawbacks, maximizes result importance, and has the best performance compared to earlier work.