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Data and Event Stream Mining


Kavya Naveen, M.V.Sathyanarayana, N.C. Naveen


Vol. 8  No. 4  pp. 140-143


Management and analysis of streaming data has become crucial with its applications on web and for transactions data . Data streams consist of mostly numeric data but what is more interesting are the events derived from the numerical data that need to be monitored. The events obtained from streaming data form event streams. Event streams have similar properties to data streams, i.e., they are seen only once in order as a continuous stream. Events appearing in the event stream have time stamps associated with them at a certain time granularity, such as second, minute, or hour. One type of frequently asked queries over event streams are count queries, i.e., the frequency of an event occurrence over time. Count queries can be answered over event streams easily; however, users may ask queries over deferent time granularities as well. For example, a banking consultant may ask how many times a certain type of transaction is increased in the same time frame, where the time frames specified could be an hour, day, or both. Such types of queries are challenging especially in the case of event streams where only a window of an event stream is available at a certain time instead of the whole stream. In this paper, we propose a technique for predicting the frequencies of event occurrences in event streams at multiple time granularities.


Mining, event streams, constraint ?based mining, data streams, knowledge