Abstract
In applications ranging from stock trading to space mission operations, it is important to monitor the correlations among multiple streaming time series efficiently in order to make timely decisions. The challenge is that both the number of streaming time series and the number of interested correlations can be large. The straightforward way of performing the evaluation by computing the correlation value for each relevant stream pair at each time position is not efficient enough in many situations.
In this paper, we introduce an efficient method for the case where we need to monitor composite correlations, i.e., conjunctions of high correlations among multiple pairs of streaming time series. We use a simple mechanism to predict the correlation values of relevant stream pairs at the next time position and rank the stream pairs carefully so that the pairs that are likely to have low correlation values are evaluated first. We show, through experiments, that the method significantly reduces the total number of pairs for which we need to compute the correlation values due to the conjunctive nature of the composites.
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References
Babu, S., Widom, J.: Continuous queries over data streams. SIGMOD Record 30(3), 109–120 (2001)
Chen, J., DeWitt, D.J., Tian, F., Wang, Y.: NiagaraCQ: a scalable continuous query system for Internet databases. In: SIGMOD Conference, pp. 379–390 (2000)
Chen, Y., Dong, G., Han, J., Wah, B.W., Wang, J.: Multi-dimensional regression analysis of time-series data streams. In: VLDB Conference, pp. 323–334 (2002)
Gao, L., Wang, X.S.: Continually evaluating similarity-based pattern queries on a streaming time series. In: SIGMOD Conference, pp. 370–381 (2002)
Gao, L., Wang, X.S.: Improving the performance of continuous queries on fast data streams: Time series case. In: Workshop on Research Issues in Data Mining and Knowledge Discovery, DMKD (2002)
Gestel, T.V., Suykens, J., Baestaens, D.-E., Lambrechts, A., Lanckriet, G., Vandaele, B., Moor, D.B., Vandewalle, J.: Financial time series prediction using least squares support vector machines within the evidence framework. IEEE Transactions on Neural Networks 12(4), 809–821 (2001)
Gyorfi, L., Lugosi, G., Morvai, G.: A simple randomized algorithm for sequential prediction of ergodic time series. IEEE Transactions on Information Theory 45(7), 2642–2650 (1999)
Kim, I., Lee, S.-R.: A fuzzy time series prediction method based on consecutive values. In: Fuzzy Systems Conference Proceedings, 1999. FUZZ-IEEE 1999, vol. 2, pp. 703–707 (1999)
Madden, S., Franklin, M.J.: Fjording the stream: An architecture for queries over streaming sensor data. In: ICDE Conference (2002)
Matias, Y., Vitter, J.S., Wang, M.: Wavelet-based histograms for selectivity estimation. In: Proceedings of the 1998 ACM SIGMOD International Conference on Management of Data, Seattle, WA, June 1998, pp. 448–459 (1998)
Plale, B., Schwan, K.: Optimizations enabled by a relational data model view to querying data streams. In: Proc. of 15th International Parallel and Distributed Processing Symposium, p. 20 (2001)
Policker, S., Geva, A.: A new algorithm for time series prediction by temporal fuzzy clustering. In: Proceedings. 15th International Conference on Pattern Recognition, vol. 2, pp. 728–731 (2000)
Poosala, V., Ioannidis, Y.E., Haas, P.J., Shekita, E.: Improved histograms for selectivity estimation of range predicates. In: Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data, Montreal, Canada (May 1996)
Terry, D., Goldberg, D., Nichols, D., Oki, B.: Continuous queries over appendonly databases. In: SIGMOD Conference, pp. 321–330 (1992)
Wang, L., Teo, K.K., Lin, Z.: Predicting time series with wavelet packet neural networks. In: Proc. International Joint Conference on Neural Networks, vol. 3, pp. 1593–1597 (2001)
Yunyue Zhu, D.S.: Statstream: Statistical monitoring of thousands of data streams in real time. In: Proceedings of the 28th International Conference on Very Large Data Bases, pp. 358–369 (2002)
Zipf, G.K.: Human Behaviour and the Principle of Least Effort. Addison-Wesley, Reading (1949)
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Wang, M., Wang, X.S. (2003). Efficient Evaluation of Composite Correlations for Streaming Time Series. In: Dong, G., Tang, C., Wang, W. (eds) Advances in Web-Age Information Management. WAIM 2003. Lecture Notes in Computer Science, vol 2762. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45160-0_37
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DOI: https://doi.org/10.1007/978-3-540-45160-0_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40715-7
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