Abstract
Intelligence analysis involves routinely monitoring and correlating large amount of data streaming from multiple sources. In order to detect important patterns, the analyst normally needs to look at data gathered over a certain time window. Given the size of data and rate at which it arrives, it is usually impossible to manually process every record or case. Instead, automated filtering (classification) mechanisms are employed to identify information relevant to the analyst’s task. In this paper, we present a novel system framework called FREESIA (Filter REfinement Engine for Streaming InformAtion) to effectively generate, utilize and update filtering queries on streaming data.
This research was supported by the National Science Foundation under Award Numbers 0331707 and 0331690.
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Chaudhuri, S., Gravano, L.: Evaluating top-k selection queries. In: Proc. of the Twenty-fifth International Conference on Very Large Databases, VLDB 1999 (1999)
Day, W., Edelsbrunner, H.: Efficient algorithms for agglomerative hierarchical clustering methods, vol. 1(1), pp. 7–24 (1984)
Domingos, P., Hulten, G.: Mining high-speed data streams. In: Knowledge Discovery and Data Mining, pp. 71–80 (2000)
Fagin, R.: Combining Fuzzy Information from Multiple Systems. In: Proc. of the 15th ACM Symp. on PODS (1996)
Fagin, R., Lotem, A., Naor, M.: Optimal aggregation algorithms for middleware. In: PODS 2001, Santa Barnara, California, May 2001, pp. 83–99 (2001)
Lambert, D., Pinheiro, J.C.: Mining a stream of transactions for customer patterns. In: Knowledge Discovery and Data Mining, pp. 305–310 (2001)
Ling, C., Li, C.: Data mining for direct marketing: problems and solutions. In: Proceedings of ACM SIGKDD (KDD 1998), pp. 73–79 (1998)
Merz, C.J., Murphy, P.: UCI Repository of Machine Learning Databases (1996), http://www.cs.uci.edu/~mlearn/MLRepository.html
Piatetsky-Shapiro, G., Masand, B.: Estimating campaign benefits and modeling lift. In: Proceedings of ACM SIGKDD (KDD 1999), pp. 185–193 (1999)
Rocchio, J.: Relevance feedback in information retrieval. In: Salton, G. (ed.) The SMART Retrieval System: Experiments in Automatic Document Processing, pp. 313–323. Prentice Hall, Englewood Cliffs (1971)
Roy, N., McCallum, A.: Toward optimal active learning through sampling estimation of error reduction. In: Proceedings of ICML 2001, pp. 441–448 (2001)
Yates, R.B., Neto, R.: Modern information retrieval. ACM Press Series Addison Wesley, New York (1999)
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© 2006 Springer-Verlag Berlin Heidelberg
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Ma, Y., Seid, D.Y. (2006). Interactive Refinement of Filtering Queries on Streaming Intelligence Data. In: Mehrotra, S., Zeng, D.D., Chen, H., Thuraisingham, B., Wang, FY. (eds) Intelligence and Security Informatics. ISI 2006. Lecture Notes in Computer Science, vol 3975. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760146_4
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DOI: https://doi.org/10.1007/11760146_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34478-0
Online ISBN: 978-3-540-34479-7
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