Abstract
Maintaining frequency counts for items over data stream has a wide range of applications such as web advertisement fraud detection. Study of this problem has attracted great attention from both researchers and practitioners. Many algorithms have been proposed. In this paper, we propose a new method, error-adaptive pruning method, to maintain frequency more accurately. We also propose a method called fractionization to record time information together with the frequency information. Using these two methods, we design three algorithms for finding frequent items and top-k frequent items. Experimental results show these methods are effective in terms of improving the maintenance accuracy.
This work was supported in part by the National Natural Science Foundation of China under Grant No. 70471006 and 70321001, and by the U.S. National Science Foundation NSF IIS- 02-09199 and IIS-03-08215.
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Liu, H., Lu, Y., Han, J., He, J. (2006). Error-Adaptive and Time-Aware Maintenance of Frequency Counts over Data Streams. In: Yu, J.X., Kitsuregawa, M., Leong, H.V. (eds) Advances in Web-Age Information Management. WAIM 2006. Lecture Notes in Computer Science, vol 4016. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11775300_41
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DOI: https://doi.org/10.1007/11775300_41
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-35225-9
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