Abstract
Processing continuous queries over unbounded streams require unbounded memory. A common solution to this issue is to restrict the range of continuous queries into a sliding window that contains the most recent data of data streams. Sliding window join aggregates are often-used queries in data stream applications. The processing method to date is to construct steaming binary operator tree and pipeline execute. This method consumes a great deal of memory in storing the sliding window join results, therefore it isn’t suitable for stream query processing. To handle this issue, we present a set of novel sliding window join aggregate operators and corresponding realized algorithms, which achieve memory-saving and efficient performance. Because the performances of proposed algorithms vary with the states of data streams, a scheduling strategy is also investigated to maximize the processing efficiency. The algorithms in this paper not only can process the complex sliding window join aggregate, but also can process the multi-way sliding window join aggregate.
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Wang, W., Li, J., Zhang, D., Guo, L. (2004). Processing Sliding Window Join Aggregate in Continuous Queries over Data Streams. In: Benczúr, A., Demetrovics, J., Gottlob, G. (eds) Advances in Databases and Information Systems. ADBIS 2004. Lecture Notes in Computer Science, vol 3255. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30204-9_24
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DOI: https://doi.org/10.1007/978-3-540-30204-9_24
Publisher Name: Springer, Berlin, Heidelberg
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