RHist: adaptive summarization over continuous data streams Lin Qiao, Divyakant Agrawal, and Amr El Abbadi   View Paper (PDF)   Return to Data streams and time-series Abstract Maintaining approximate aggregates and summaries over data streams is crucial to handle the OLAP query workload that arises in applications, such as network monitoring and telecommunications. Furthermore, since the entire data is not available at all times the maintenance task must be done incrementally. We show that R(elaxed)Hist(ogram) is an appropriate summarization under data stream scenario. In order to reduce query estimation errors, we propose adaptive approaches which not only capture the data distribution, but also integrate independent query patterns. We introduce a workload decay model to efficiently capture global workload information and ensure that the query patterns from the recent past are weighted more than queries that are further in the past. We verify experimentally that our approach successfully adapts to continuously changing workload as well as data streams. DiSC'03 © 2003 Association for Computing Machinery