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 importance levels. Current methods pay little attention to load-shedding when tuples bear such importance semantics, and perform poorly due to premature tuple drops and unproductive tuple retention. We propose a novel framework, called iJoin, which overcomes these drawbacks, maximizes result importance, and has the best performance compared to earlier work. © 2008 Springer-Verlag.
CITATION STYLE
Kulkarni, D., & Ravishankar, C. V. (2008). iJoin: Importance-aware join approximation over data streams. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5069 LNCS, pp. 541–548). https://doi.org/10.1007/978-3-540-69497-7_36
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