Data Streams: Models and Algorithms Data Streams: Models and Algorithms


In recent years, advances in hardware technology have facilitated new ways of collecting data continuously. In many applications such as network monitoring, the volume of such data is so large that it may be impossible to store the data on disk. Furthermore, even when the data can be stored, the volume of the incoming data may be so large that it may be impossible to process any particular record more than once. Therefore, many data mining and database operations such as classification, clustering, frequent pattern mining and indexing become significantly more challenging in this context. In many cases, the data patterns may evolve continuously, as a result of which it is necessary to design the mining algorithms effectively in order to account for changes in underlying structure of the data stream. This makes the solutions of the underlying problems even more difficult from an algorithmic and computational point of view. This book contains a number of chapters which are carefully chosen in order to discuss the broad research issues in data streams. The purpose of this chapter is to provide an overview of the organization of the stream processing and mining techniques which are covered in this book.

Extracted Key Phrases

Cite this paper

@inproceedings{Aggarwal2006DataSM, title={Data Streams: Models and Algorithms Data Streams: Models and Algorithms}, author={Charu C. Aggarwal and Jiawei Han and Jianyong Wang and Mohamed Medhat Gaber and Arkady B. Zaslavsky and Shonali Krishnaswamy and Dora Cai and Yixin Chen and Guozhu Dong and Jian Pei and Benjamin W. Wah}, year={2006} }