Abstract
A novel method for analyzing time-series data and extracting time-correlations (time-dependent relationships) among multiple time-series data streams is described. The application of time-correlation detection in business impact analysis (BIA) is explained on an example. The method described in this paper is the first one that can efficiently detect and report time-dependent relationships among multiple time-series data streams. Detected time-correlation rules explain how the changes in the values of one set of time-series data streams influence the values in another set of time-series data streams. Those rules can be stored digitally and fed into various data analysis tools, such as simulation, forecasting, impact analysis, etc., for further analysis of the data. Performance experiments showed that the described method is 95% accurate, and has a linear running time with respect to the amount of input data.
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References
Agrawal, R., Faloutsos, C., Swami, A.N.: Efficient similarity search in sequence databases. In: FODO, Evanstons, IL (October 1993)
Berndt, D.J., Clifford, J.: Finding patterns in time series: A dynamic programming approach. In: Advances in Knowledge Discovery and Data Mining, pp. 229–248. MIT Press, Cambridge (1996)
Das, G., Gunopulos, D., Mannila, H.: Finding similar time series. In: Proceedings of Principles of Data Mining and Knowledge Discovery (PKDD), Trondheim, Norway (June 1997)
Faloutsos, C., Ranganathan, M., Manolopoulos, Y.: Fast subsequence matching in time-series databases. In: SIGMOD (1994)
Goldin, D.Q., Kanellakis, P.: On similarity queries for time-series data: Constraint specification and implementation. In: International Conference on the Principles and Practice of Constraint Programming (1995)
Jakobson, G., Weissman, M.D.: Alarm correlation. IEEE Network, 52–59 (November 1993)
Jakobson, G., Weissman, M.D.: Real-time telecommunication network management: Extending event correlation with temporal constraints. In: Proceedings of IFIP/IEEE International Symposium on Integrated Network Management, pp. 290–302 (1995)
Lewis, L.: A case-based reasoning approach to the resolution of faults in communications networks. In: Proceedings of IFIP/IEEE International Symposium on Integrated Network Management, pp. 671–681 (1993)
Leymann, F., Roller, D.: Production Workflows. Prentice-Hall, Englewood Cliffs (2000)
Liu, G., Mok, A.K., Yang, E.J.: Composite events for network event correlation. In: Proceedings of IFIP/IEEE International Symposium on Integrated Network Management, pp. 247–260 (1999)
Lor, K.-W.E.: A network diagnostic expert system for AcculinkTM multiplexers based on a general network diagnostic scheme. In: Proceedings of IFIP/IEEE International Symposium on Integrated Network Management, pp. 659–669 (1993)
Nygate, Y.A.: Event correlation using rule and object based techniques. In: Proceedings of IFIP/IEEE International Symposium on Integrated Network Management, pp. 278–289 (1995)
Page, E.S.: Continuous Inspection Schemes. Biometrika 41, 100–114 (1954)
Perng, C.-S., Wang, H., Zhang, S., Parker, D.S.: Landmarks: A New Model for Similarity-Based Pattern Querying in Time Series Databases. In: Proceedings of the 16th International Conference of Data Engineering (ICDE), San Diego, CA (February 2000)
Rafiei, D., Mendelzon, A.O.: Similarity-based queries for time series data. In: SIGMOD (1997)
Yi, B.-K., Jagadish, H., Faloutsos, C.: Efficient retrieval of similar time sequences under time warping. In: ICDE (1998)
Zhu, Y., Shasha, D.: Statstream: Statistical monitoring of thousands of data streams in real time. In: Proceedings of 28th International Conference on Very Large Data Bases (VLDB), Hong Kong, China, August 20-23, pp. 358–369 (2002)
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Sayal, M. (2006). Business Impact Analysis Using Time Correlations. In: Lee, J., Shim, J., Lee, Sg., Bussler, C., Shim, S. (eds) Data Engineering Issues in E-Commerce and Services. DEECS 2006. Lecture Notes in Computer Science, vol 4055. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11780397_14
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DOI: https://doi.org/10.1007/11780397_14
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