Abstract
Detecting changes in data streams is very important for many applications. This paper presents a hybrid method for detecting data stream changes in intensive care unit. In the method, we first use query processing to detect all the potential changes supporting semantics in big granularity, and then perform similarity matching, which has some features such as normalized subsequences and weighted distance. Our approach makes change detection with a better trade-off between sensitivity and specificity. Experiments on ICU data streams demonstrate its effectiveness.
Supported by Natural Science Foundation of China (NSFC) under grant number 60473072.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Zhu, Y., Shasha, D.: StatStream: Statistical Monitoring of Thousands of Data Streams in Real Time. In: VLDB, pp. 358–369 (2002)
Goldin, D.Q., Millstein, T.D., Kutlu, A.: Bounded similarity query for time series data. Information and Computation 194, 203–241 (2004)
Wu, H., Salzberg, B., Zhang, D.: Online Event-driven Subsequence Matching over Financial Data Streams. In: SIGMOD 2004, Paris, France (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yin, T., Li, H., Hu, Z., Fan, Y., Gao, J., Tang, S. (2005). A Hybrid Method for Detecting Data Stream Changes with Complex Semantics in Intensive Care Unit. In: Grumbach, S., Sui, L., Vianu, V. (eds) Advances in Computer Science – ASIAN 2005. Data Management on the Web. ASIAN 2005. Lecture Notes in Computer Science, vol 3818. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11596370_39
Download citation
DOI: https://doi.org/10.1007/11596370_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30767-9
Online ISBN: 978-3-540-32249-8
eBook Packages: Computer ScienceComputer Science (R0)