Statistical Analysis of Continuous Data Streams Using DSMS
Mining of Data Streams
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Several applications involve a transient stream of data which has to be modeled and analyzed continuously. Their continuous arrival in multiple, rapid, time-varying and possibly unpredictable and unbounded way make the analysis difficult and opens fundamentally new research problems. Examples of such data intensive applications include stock market, road traffic analysis, whether forecasting systems etc. Data Stream Management Systems are specifically designed for handling continuous data streams. They can handle multiple, time-varying, unpredictable and unbounded streams which cannot be handl...
Several applications involve a transient stream of data which has to be modeled and analyzed continuously. Their continuous arrival in multiple, rapid, time-varying and possibly unpredictable and unbounded way make the analysis difficult and opens fundamentally new research problems. Examples of such data intensive applications include stock market, road traffic analysis, whether forecasting systems etc. Data Stream Management Systems are specifically designed for handling continuous data streams. They can handle multiple, time-varying, unpredictable and unbounded streams which cannot be handled using traditional tools. In this work, we have used a Data Stream Management System- Stanford STREAM in three different application domain namely Road Traffic analysis, Habitat Monitoring analysis and Network Packet analysis. We have also used another DSMS, telegraphCQ, coupled with jamdroid, an open source road traffic analysis system, for mining road traffic data.
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