Abstract
In this paper, we introduce a data stream reduction method using lossy wavelets compression. The lossy compression means that compressed data carry as much information about the original data stream as possible while the original data size remarkably reduced. We think that wavelets technique should be an efficient method for such lossy compression. Especially we consider storing a plenty of past data stream into stable storage (flash memory or micro HDD) rather than keeping only recent streaming data allowable in memory, because data stream mining and tracking of past data stream are often required. In the general method using wavelets, a specific amount of streaming data from a sensor is periodically compressed into fixed size and the fixed amount of compressed data is stored into stable storage. However, differently from the general method, our method flexibly adjusts the compressing size based on a heuristic criterion. Experimental results with some real stream data show that wavelets technique is useful in data stream reduction and our flexible approach has lower estimation error than the general fixed approach.
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
Aggarwal, C.C., Han, J., Wang, J., Yu, P.S.: A Framework for Clustering Evolving Data Streams. In: Proc. 29th International Conf. on VLDB, Berlin, Germany, pp. 81–92 (2003)
Karras, P., Mamoulis, N.: One-Pass Wavelet Synopses for Maxium-Error Metrics. In: Proc. 31th International Conf. on VLDB, Trondheim, Norway, pp. 421–432 (2005)
Babcock, B., Babu, S., Datar, M., Motwani, R., Widom, J.: Models and Issues in Data Stream Systems. In: Proc. the 21th ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, Madison, USA, pp. 1–16 (2002)
Matias, Y., Vitter, J.S., Wang, M.: Dynamic Maintenance of Wavelet-Based Histograms. In: Proc. 26th International Conf. on VLDB, Egypt, pp. 101–110 (2000)
Matias, Y., Vitter, J.S., Wang, M.: Wavelet-Based Histograms for Selectivity Estimation. In: Proc. the ACM SIGMOD International Conf. on Management of Data, Seattle, USA, pp. 448–459 (1998)
Kim, J., Park, S.: Periodic Streaming Data Reduction Using Flexible Adjustment of Time Section Size. International Journal of Data Warehousing & Mining 1(1), 37–56 (2005)
Stollnitz, E.J., Derose, T.D., Salesin, D.H.: Wavelets for Computer Graphics. Morgan Kaufmann, San Francisco (1996)
Tatbul, N., Çetintemel, U., Zdonik, S.B., Cherniack, M., Stonebraker, M.: Load Shedding in a Data Stream Manager. In: Proc. 29th International Conf. on VLDB, Berlin, Germany, pp. 309–320 (2003)
Istepanian, R.S., Jovanov, E., Zhang, Y.T.: Introduction to the special section on M-Health: beyond seamless mobility and global wireless health-care connectivity, Guest Editorial. IEEE Transactions on Information Technology in Biomedicine 8(4), 405–413 (2004)
Deshpande, A., Guestrin, C., Madden, S.R., Hellerstein, J.M., Hong, W.: Model-Driven Data Acquisition in Sensor Networks. In: Proc. 30th International Conf. on VLDB, Toronto, Canada, pp. 588–599 (2004)
Time Series Data Mining Archive, http://www.cs.ucr.edu/~eamonn/TSDMA/index.html
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kim, J., Park, S. (2007). Flexible Selection of Wavelet Coefficients Based on the Estimation Error of Predefined Queries. In: Washio, T., et al. Emerging Technologies in Knowledge Discovery and Data Mining. PAKDD 2007. Lecture Notes in Computer Science(), vol 4819. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77018-3_64
Download citation
DOI: https://doi.org/10.1007/978-3-540-77018-3_64
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-77016-9
Online ISBN: 978-3-540-77018-3
eBook Packages: Computer ScienceComputer Science (R0)