Abstract
The sliding-window model of computation is motivated by the assumption that, in certain data-stream processing applications, recent data is more useful and pertinent than older data. In such cases, we would like to answer questions about the data only over the last N most recent data elements (N is a parameter). We formalize this model of computation and answer questions about how much space and computation time is required to solve certain problems under the sliding-window model.
Material in this chapter also appears in Data Stream Management: Processing High-Speed Data Streams, edited by Minos Garofalakis, Johannes Gehrke and Rajeev Rastogi, published by Springer-Verlag.
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Datar, M., Motwani, R. (2007). The Sliding-Window Computation Model and Results. In: Aggarwal, C.C. (eds) Data Streams. Advances in Database Systems, vol 31. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-47534-9_8
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DOI: https://doi.org/10.1007/978-0-387-47534-9_8
Publisher Name: Springer, Boston, MA
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