Abstract
Data stream sliding window is a common query method, but traditional approaches can get inaccurate data results. Due to the blocked character of join and the infinite character of data stream, there must be some constraints on join operations. In order to solve these constraints, this paper proposes a strategy based on load shedding techniques for sliding window aggregation queries over data stream for basic query processing and optimization. The new strategy supports multiple streams and multiple queries. Through experimental tests, the results show that the new strategy based on load shedding techniques can greatly improve query processing efficiency. Finally, we make a comparison to other join query strategies over data stream to verify the new method. Join query strategy over data stream based on sliding windows is an effective method, which can effectively reduce query processing time.
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Sun, Y., Teng, L., Yin, S., Liu, J., Li, H. (2017). Study a Join Query Strategy Over Data Stream Based on Sliding Windows. In: Tan, Y., Takagi, H., Shi, Y. (eds) Data Mining and Big Data. DMBD 2017. Lecture Notes in Computer Science(), vol 10387. Springer, Cham. https://doi.org/10.1007/978-3-319-61845-6_34
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DOI: https://doi.org/10.1007/978-3-319-61845-6_34
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