Abstract
In the real-time series streaming environments, such as data analysis in sensor networks, online stock analysis, video surveillance and weather forecasting, similarity search, which aims at retrieving the similarity between two or more streams, is a hot issue in the recent years. How to find continuous k-nearest neighbors (CKNN) queries has been one of the most common applications in computing on DSMS. In this paper, we developed traditional skylines technique and propose W-Skyline to process CKNN queries as a bandwidth efficient approach over distributed streams. It tries to use of wavelet transformations as a dimensionality reduction technique to permit efficient similarity search over time-series data in memory. Finally, we will give an extensive experimental study with real-time data sets that verifies the effectiveness of our W-Skyline transformation approach in similarity search and CKNN discovery within arbitrary ranges in the time series streaming environments.
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.
References
Arasu, A., Babcock, B., Babu, S., Datar, M., Ito, K., Motwani, R., Nishizawa, I., Srivastava, U., Thomas, D., Varma, R., Widom, J.: STREAM: The Stanford Stream Data Manager. In: IEEE Data Engineering Bulletin, pp. 19–26. IEEE Press, Los Alamitos (2003)
Liarou, E., Goncalves, R., Idreos, S.: Exploiting the Power of Relational Databases for Efficient Stream Processing. In: EDBT 2009, pp. 323–334. ACM Press, Russia (2009)
Anna, C.G., Yannis, K., Muthukrishnan, S., Martin, J.S.: One-Pass Wavelet Decompositions of Data Streams. IEEE Transactions on Knowledge and Data Engineering, 541–554 (2003)
Panagiotis, K., Nikos, M.: One-pass Wavelet Synopses for Maximum-Error Metrics. In: VLDB, pp. 421–432. VLDB Endowment Press, Norway (2005)
Sudipto, G., Boulos, H.: Wavelet Synopsis for Data Streams: Minimizing Non-Euclidean Error. In: ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, pp. 88–97. ACM Press, Illinois (2005)
Hao, P.H., Ming, S.C.: Efficient Range-Constrained Similarity Search on Wavelet Synopses Over Multiple Streams. In: 15th ACM International Conference on Information and Knowledge Management, pp. 327–336. ACM Press, Virginia (2006)
Tao, Y.F., Yi, K., Sheng, C., Kainis, P.: Quality and Efficiency in High Dimensional Nearest Neighbor Search. In: SIGMOD 2009, pp. 563–576. ACM Press, Rhode Island (2009)
Haar, A.: Zur Theorie der Orthogonalen Funktionensysteme. Mathematische Annalen 69 (1910)
Yu, F.T., Dimitris, P.: Maintaining Sliding Window Skylines on Data Streams. IEEE Transactions on Knowledge and Data Engineering, 377–391 (2006)
Bulut, A., Singh, A.K.: A Unified Framework for Monitoring Data Streams in Real Time. In: 21st International Conference on Data Engineering, ICDE 2005, pp. 44–55. IEEE Press, Tokyo (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, L., Zhou, T.H., Shon, H.S., Lee, Y.K., Ryu, K.H. (2010). Extract and Maintain the Most Helpful Wavelet Coefficients for Continuous K-Nearest Neighbor Queries in Stream Processing. In: Huang, DS., McGinnity, M., Heutte, L., Zhang, XP. (eds) Advanced Intelligent Computing Theories and Applications. ICIC 2010. Communications in Computer and Information Science, vol 93. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14831-6_48
Download citation
DOI: https://doi.org/10.1007/978-3-642-14831-6_48
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-14830-9
Online ISBN: 978-3-642-14831-6
eBook Packages: Computer ScienceComputer Science (R0)