Ebook: Data Streams: Models and Algorithms

In recent years, the progress in hardware technology has made it possible for organizations to store and record large streams of transactional data. Such data sets which continuously and rapidly grow over time are referred to as data streams.

Data Streams: Models and Algorithms primarily discusses issues related to the mining aspects of data streams rather than the database management aspect of streams. This volume covers mining aspects of data streams in a comprehensive style. Each contributed chapter, from a variety of well known researchers in the data mining field, contains a survey on the topic, the key ideas in the field from that particular topic, and future research directions.

Data Streams: Models and Algorithms is intended for a professional audience composed of researchers and practitioners in industry. This book is also appropriate for graduate-level students in computer science.

Charu C. Aggarwal obtained his B.Tech in Computer Science from IIT Kanpur in 1993 and Ph.D. from MIT in 1996. He has been a Research Staff Member at IBM since then, and has published over 90 papers in major conferences and journals in the database and data mining field. He has applied for, or been granted, over 50 US and International patents, and has twice been designated Master Inventor at IBM for the commercial value of his patents. He has been granted 14 invention achievement awards by IBM for his patents. His work on real time bio-terrorist threat detection in data streams won the IBM Epispire award for environmental excellence in 2003. He has served on the program committee of most major database conferences, and was program chair for the Data Mining and Knowledge Discovery Workshop, 2003, and a program vice-chair for the SIAM Conference on Data Mining, 2007. He is an associate editor of the IEEE Transactions on Data Engineering and an action editor of the Data Mining and Knowledge Discovery Journal. He is a senior member of the IEEE.

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In recent years, the progress in hardware technology has made it possible for organizations to store and record large streams of transactional data.  Such data sets which continuously and rapidly grow over time are referred to as data streams.

Data Streams: Models and Algorithms primarily discusses issues related to the mining aspects of data streams rather than the database management aspect of streams. This volume covers mining aspects of data streams in a comprehensive style. Each contributed chapter, from a variety of well known researchers in the data mining field, contains a survey on the topic, the key ideas in the field from that particular topic, and future research directions.

Data Streams: Models and Algorithms is intended for a professional audience composed of researchers and practitioners in industry. This book is also appropriate for graduate-level students in computer science.

Charu C. Aggarwal obtained his B.Tech in Computer Science from IIT Kanpur in 1993 and Ph.D. from MIT in 1996. He has been a Research Staff Member at IBM since then, and has published over 90 papers in major conferences and journals in the database and data mining field. He has applied for, or been granted, over 50 US and International patents, and has twice been designated Master Inventor at IBM for the commercial value of his patents. He has been granted 14 invention achievement awards by IBM for his patents. His work on real time bio-terrorist threat detection in data streams won the IBM Epispire award for environmental excellence in 2003. He has served on the program committee of most major database conferences, and was program chair for the Data Mining and Knowledge Discovery Workshop, 2003, and a program vice-chair for the SIAM Conference on Data Mining, 2007. He is an associate editor of the IEEE Transactions on Data Engineering and an action editor of the Data Mining and Knowledge Discovery Journal. He is a senior member of the IEEE.

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In recent years, the progress in hardware technology has made it possible for organizations to store and record large streams of transactional data.  Such data sets which continuously and rapidly grow over time are referred to as data streams.

Data Streams: Models and Algorithms primarily discusses issues related to the mining aspects of data streams rather than the database management aspect of streams. This volume covers mining aspects of data streams in a comprehensive style. Each contributed chapter, from a variety of well known researchers in the data mining field, contains a survey on the topic, the key ideas in the field from that particular topic, and future research directions.

Data Streams: Models and Algorithms is intended for a professional audience composed of researchers and practitioners in industry. This book is also appropriate for graduate-level students in computer science.

Charu C. Aggarwal obtained his B.Tech in Computer Science from IIT Kanpur in 1993 and Ph.D. from MIT in 1996. He has been a Research Staff Member at IBM since then, and has published over 90 papers in major conferences and journals in the database and data mining field. He has applied for, or been granted, over 50 US and International patents, and has twice been designated Master Inventor at IBM for the commercial value of his patents. He has been granted 14 invention achievement awards by IBM for his patents. His work on real time bio-terrorist threat detection in data streams won the IBM Epispire award for environmental excellence in 2003. He has served on the program committee of most major database conferences, and was program chair for the Data Mining and Knowledge Discovery Workshop, 2003, and a program vice-chair for the SIAM Conference on Data Mining, 2007. He is an associate editor of the IEEE Transactions on Data Engineering and an action editor of the Data Mining and Knowledge Discovery Journal. He is a senior member of the IEEE.

Content:
Front Matter....Pages i-xviii
An Introduction to Data Streams....Pages 1-8
On Clustering Massive Data Streams: A Summarization Paradigm....Pages 9-38
A Survey of Classification Methods in Data Streams....Pages 39-59
Frequent Pattern Mining in Data Streams....Pages 61-84
A Survey of Change Diagnosis Algorithms in Evolving Data Streams....Pages 85-102
Multi-Dimensional Analysis of Data Streams Using Stream Cubes....Pages 103-125
Load Shedding in Data Stream Systems....Pages 127-147
The Sliding-Window Computation Model and Results....Pages 149-167
A Survey of Synopsis Construction in Data Streams....Pages 169-207
A Survey of Join Processing in Data Streams....Pages 209-236
Indexing and Querying Data Streams....Pages 237-259
Dimensionality Reduction and Forecasting on Streams....Pages 261-288
A Survey of Distributed Mining of Data Streams....Pages 289-307
Algorithms for Distributed Data Stream Mining....Pages 309-331
A Survey of Stream Processing Problems and Techniques in Sensor Networks....Pages 333-352
Back Matter....Pages 353-354

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In recent years, the progress in hardware technology has made it possible for organizations to store and record large streams of transactional data.  Such data sets which continuously and rapidly grow over time are referred to as data streams.

Data Streams: Models and Algorithms primarily discusses issues related to the mining aspects of data streams rather than the database management aspect of streams. This volume covers mining aspects of data streams in a comprehensive style. Each contributed chapter, from a variety of well known researchers in the data mining field, contains a survey on the topic, the key ideas in the field from that particular topic, and future research directions.

Data Streams: Models and Algorithms is intended for a professional audience composed of researchers and practitioners in industry. This book is also appropriate for graduate-level students in computer science.

Charu C. Aggarwal obtained his B.Tech in Computer Science from IIT Kanpur in 1993 and Ph.D. from MIT in 1996. He has been a Research Staff Member at IBM since then, and has published over 90 papers in major conferences and journals in the database and data mining field. He has applied for, or been granted, over 50 US and International patents, and has twice been designated Master Inventor at IBM for the commercial value of his patents. He has been granted 14 invention achievement awards by IBM for his patents. His work on real time bio-terrorist threat detection in data streams won the IBM Epispire award for environmental excellence in 2003. He has served on the program committee of most major database conferences, and was program chair for the Data Mining and Knowledge Discovery Workshop, 2003, and a program vice-chair for the SIAM Conference on Data Mining, 2007. He is an associate editor of the IEEE Transactions on Data Engineering and an action editor of the Data Mining and Knowledge Discovery Journal. He is a senior member of the IEEE.

Content:
Front Matter....Pages i-xviii
An Introduction to Data Streams....Pages 1-8
On Clustering Massive Data Streams: A Summarization Paradigm....Pages 9-38
A Survey of Classification Methods in Data Streams....Pages 39-59
Frequent Pattern Mining in Data Streams....Pages 61-84
A Survey of Change Diagnosis Algorithms in Evolving Data Streams....Pages 85-102
Multi-Dimensional Analysis of Data Streams Using Stream Cubes....Pages 103-125
Load Shedding in Data Stream Systems....Pages 127-147
The Sliding-Window Computation Model and Results....Pages 149-167
A Survey of Synopsis Construction in Data Streams....Pages 169-207
A Survey of Join Processing in Data Streams....Pages 209-236
Indexing and Querying Data Streams....Pages 237-259
Dimensionality Reduction and Forecasting on Streams....Pages 261-288
A Survey of Distributed Mining of Data Streams....Pages 289-307
Algorithms for Distributed Data Stream Mining....Pages 309-331
A Survey of Stream Processing Problems and Techniques in Sensor Networks....Pages 333-352
Back Matter....Pages 353-354
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