Abstract
Computing statistical measures is a fundamental problem for mining data streams. Sometimes user wants to query the realtime correlation of data streams. In this paper, we introduce a system for computing realtime statistical measures of data streams. The system updates the realtime summaries which are used to compute affine relationships. We process every elements in every data stream only once, and get a similar accuracy rating compared with the static methods. To the best of our knowledge, we present a new method of computing affine relationship. Our system employs the multi-Hierarchies approach in the Sliding Window Model. First, we change AFCLST Clustering algorithm. Second, the Bottom-Up Updating algorithm updates the summaries which every hierarchy has stored after the Cumulative Calculation algorithms. Third, the Query Response algorithm uses summaries to compute the statistical measure. Finally, we establish the accuracy rating of our approach by performing several experiments on real datasets.
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Qi, P., Shi, S. (2014). Multi-Hierarchies: Accurately Computing Realtime Statistical Measures on Data Streams. In: Cai, Z., Wang, C., Cheng, S., Wang, H., Gao, H. (eds) Wireless Algorithms, Systems, and Applications. WASA 2014. Lecture Notes in Computer Science, vol 8491. Springer, Cham. https://doi.org/10.1007/978-3-319-07782-6_65
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DOI: https://doi.org/10.1007/978-3-319-07782-6_65
Publisher Name: Springer, Cham
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