Abstract
In many applications, patterns over time-varied data streams usually imply high domain value. The variations of patterns can often be measured from their internal structures. Traditional methods usually take each pattern as a whole to analyze data stream variations; however, few works have achieved a widely applicable resolution. This paper considers the feature of sub parts for data stream patterns and studies their variations and relationships from the perspective of multiple dimensions, to explore a comprehensive understanding for the variation history and effectively support different types of queries. This paper first decomposes patterns into different dimensions and then evaluates the variations of each dimension. After that, a data cube called VS-Cube is used to find out the variations of a single dimension as well as the relationships between different dimensions within a certain pattern. At last, the experimental results on real datasets are given to demonstrate the efficiency and effectiveness of our proposed methods.
This work was supported by Natural Science Foundation of China (No.60973002 and No.61170003), the National High Technology Research and Development Program of China (Grant No. 2012AA011002), National Science and Technology Major Program (Grant No. 2010ZX01042-002-002-02, 2010ZX01042-001-003-05).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Wu, H., Salzberg, B., Sharp, G., Jiang, S., et al.: Subsequence Matching on Structured Time Series Data. In: Proceedings of the 2005 ACM SIGMOD International Conference on Management of Data, pp. 682–693 (2005)
Tang, L., Cui, B., Li, H., et al.: Effective Variation Management for Pseudo Periodical Streams. In: Proceedings of the 2007 ACM SIGMOD International Conference on Management of Data, pp. 257–268 (2007)
Han, J., Dong, G., Yin, Y.: Efficient mining of partial periodic patterns in time series database. In: Proceedings of the 15th International Conference on Data Engineering, p. 106 (1999)
Yang, J., Wang, W., Sharp, G., Yu, P.: Mining Asynchronous Periodic Patterns in Time Series Data. IEEE Transactions on Knowledge and Data Engineering (TKDE) 15(3), 275–279 (2003)
Lin, J., Keogh, E., Lonardi, S., Chiu, B.: A Symbolic Representation of Time Series, with Implications for Streaming Algorithms. In: Proceedings of the 8th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, pp. 2–11 (2003)
Stonebraker, M., Cetintemel, U., Zdonik, S.: The 8 Requirements of Real-time Stream Processing. ACM SIGMOD Record 34(4), 42–47 (2005)
Mueen, A., Koegh, E.: Online Discovery and Maintenance of Time Series Motifs. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1089–1098 (2010)
Liu, M., Rundensteiner, E., Greenfield, K., et al.: E-Cube: Multi-Dimensional Event Sequence Analysis Using Hierarchical Pattern Query Sharing. In: Proceedings of the 2011 ACM SIGMOD International Conference on Management of Data, pp. 889–900 (2011)
Gonzalez, H., Han, J., Li, X.: FlowCube: Constructing RFID FlowCubes for Multi-Dimensional Analysis of Commodity Flows. In: Proceedings of the 32nd International Conference on Very Large Data Bases, pp. 834–845 (2006)
Han, J., Chen, Y., Dong, G., Pei, J., Wah, B.W., Wang, J., Cai, Y.D.: Stream Cube: An Architecture for Multi-dimensional Analysis of Data Streams. Distributed and Parallel Databases 18(2), 173–197 (2005)
Chen, Y., Dong, G., Han, J., et al.: Multi-Dimensional Regression Analysis of Time-Series Data Stream. In: Proceedings of the 28th International Conference on Very Large Data Bases, pp. 323–334 (2002)
Lim, H., Whang, K., Moon, Y.: Similar Sequence Matching Supporting Variable-length and Variable-tolerance Continuous Queries on Time-series Data Stream. Information Sciences: an International Journal 178(6), 1461–1478 (2008)
Keogh, E., Chu, S., Hart, D., et al.: An Online Algorithm for Segmenting Time Series. In: Proceeding IEEE International Conference on Data Mining, pp. 289–296 (2001)
Wang, X., Wang, Z.: A Structure-adaptive Piecewise Linear Segments Representation for Time Series. In: Proceedings of Information Reuse and Integration, pp. 433–437 (2004)
Lin, J., Keogh, E., Lonardi, S., Chiu, B.: A Symbolic Representation of Time Series, with Implications for Streaming Algorithms. In: Proceedings of the 8th ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, pp. 2–11 (2003)
Gilbert, A.C., Kotidis, Y., Muthukrishnan, S., Strauss, M.J.: One-Pass Wavelet Decompositions of Data Streams. IEEE Transactions on Knowledge and Data Engineering 15(3), 541–554 (2003)
Papadimitriou, S., Brockwell, A., Faloutsos, C.: Adaptive, Unsupervised Stream Mining. The International Journal on Very Large Data Bases 13(3), 222–239 (2004)
Gao, L., Wang, X.S.: Continuous Similarity-Based Queries on Streaming Time Series. In: Proceedings of the 2002 ACM SIGMOD International Conference on Management of Data, vol. 17(10), pp. 370–381 (2005)
Gao, L., Wang, X.S.: Continuous Similarity-Based Queries on Streaming Time Series. In: Proceedings of the 2002 ACM SIGMOD International Conference on Management of Data, vol. 17(10), pp. 370–381 (2005)
Morse, M.D., Patel, J.M.: An Efficient and Accurate Method for Evaluating Time Series Similarity. In: Proceedings of the 2007 ACM SIGMOD International Conference on Management of Data, pp. 569–580 (2007)
Chen, L., Ng, R.: On the Marriage of Lp-norms and Edit Distance. In: Proceedings of the Thirtieth International Conference on Very Large Data Bases, pp. 792–803 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Tang, Y., Li, H., Li, F., Miao, G. (2012). VS-Cube: Analyzing Variations of Multi-dimensional Patterns over Data Streams. In: Zhou, S., Zhang, S., Karypis, G. (eds) Advanced Data Mining and Applications. ADMA 2012. Lecture Notes in Computer Science(), vol 7713. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35527-1_64
Download citation
DOI: https://doi.org/10.1007/978-3-642-35527-1_64
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35526-4
Online ISBN: 978-3-642-35527-1
eBook Packages: Computer ScienceComputer Science (R0)