Proceedings of the 2006 ACM SIGMOD international conference on Management of data
GrubJoin: An Adaptive, Multi-Way, Windowed Stream Join with Time Correlation-Aware CPU Load Shedding
IEEE Transactions on Knowledge and Data Engineering
Probabilistic lossy counting: an efficient algorithm for finding heavy hitters
ACM SIGCOMM Computer Communication Review
Data-driven memory management for stream join
Information Systems
Small synopses for group-by query verification on outsourced data streams
ACM Transactions on Database Systems (TODS)
Evaluating top-k queries over incomplete data streams
Proceedings of the 18th ACM conference on Information and knowledge management
RRPJ: result-rate based progressive relational join
DASFAA'07 Proceedings of the 12th international conference on Database systems for advanced applications
Danaïdes: continuous and progressive complex queries on RSS feeds
DASFAA'07 Proceedings of the 12th international conference on Database systems for advanced applications
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In this paper, we investigate a new approach to process queries in data stream applications. We show that reference locality characteristics of data streams could be exploited in the design of superior and flexible data stream query processing techniques. We identify two different causes of reference locality: popularity over long time scales and temporal correlations over shorter time scales. An elegant mathematical model is shown to precisely quantify the degree of those sources of locality. Furthermore, we analyze the impact of locality-awareness on achievable performance gains over traditional algorithms on applications such asMAX-subset approximate sliding window join and approximate count estimation. In a comprehensive experimental study, we compare several existing algorithms against our locality-aware algorithms over a number of real datasets. The results validate the usefulness and efficiency of our approach.