We consider indexing sliding windows in main memory over on-line data streams. Our proposed data structures and query semantics are based on a division of the sliding window into sub-windows. By classifying windowed operators according to their method of execution, we motivate the need for two types of windowed indices: those which provide a list of attribute values and their counts for answering set-valued queries, and those which provide direct access to tuples for answering attribute-valued queries. We propose and evaluate indices for both of these cases and show that our techniques are more efficient than executing windowed queries without an index. © Springer-Verlag 2004.
CITATION STYLE
Golab, L., Garg, S., & Tamer Özsu, M. (2004). On indexing sliding windows over online data streams. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2992, 712–729. https://doi.org/10.1007/978-3-540-24741-8_41
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