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.

An Introduction to Data Streams.- On Clustering Massive Data Streams: A Summarization Paradigm.- A Survey of Classification Methods in Data Streams.- Frequent Pattern Mining in Data Streams.- A Survey of Change Diagnosis Algorithms in Evolving Data Streams.- Multi-Dimensional Analysis of Data Streams Using Stream Cubes.- Load Shedding in Data Stream Systems.- The Sliding-Window Computation Model and Results.- A Survey of Synopsis Construction in Data Streams.- A Survey of Join Processing in Data Streams.- Indexing and Querying Data Streams.- Dimensionality Reduction and Forecasting on Streams.- A Survey of Distributed Mining of Data Streams.- Algorithms for Distributed Data Stream Mining.- A Survey of Stream Processing Problems and Techniques in Sensor Networks.

Data Streams: Models and Algorithms primarily discusses issues related to the mining aspects of data streams. Recent progress in hardware technology makes it possible for organizations to store and record large streams of transactional data. For example, even simple daily transactions such as using the credit card or phone result in automated data storage, which brings us to a fairly new topic called data streams.
This volume covers mining aspects of data streams comprehensively: each contributed chapter contains a survey on the topic, the key ideas in the field for 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 advanced-level students in computer science.