Statistical Analysis of Continuous Data Streams Using DSMS

Nadeem Akhtar

ISBN 10: 3846545201 / ISBN 13: 9783846545201

Published by LAP LAMBERT Academic Publishing Apr 2012, 2012

About this Item

This item is printed on demand - Print on Demand Titel. Neuware -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.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 88 pp. Englisch.

Seller Inventory # 9783846545201

Bibliographic Details

• Title: Statistical Analysis of Continuous Data Streams Using DSMS
• Publisher: LAP LAMBERT Academic Publishing Apr 2012
• Publication Date: 2012
• Language: English
• ISBN 10: 3846545201
• ISBN 13: 9783846545201
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 6D millimeters 220 millimeters width by 150 millimeters height by 6 millimeters depth
• Weight: 149 grams

About this title

Synopsis

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.

About the Author

Nadeem Akhtar received his B. Tech and M.Tech degree in Computer Science from Aligarh Muslim University, Aligarh, India. He is currently working as Assistant Professor in the department of Computer Engineering, Aligarh Muslim University. His research interests include Data Mining, Database Management System and Operating Systems.