Data Mining and Knowledge Discovery Handbook, 2 Edition part 79. Knowledge Discovery demonstrates intelligent computing at its best, and is the most desirable and interesting end-product of Information Technology. To be able to discover and to extract knowledge from data is a task that many researchers and practitioners are endeavoring to accomplish. There is a lot of hidden knowledge waiting to be discovered – this is the challenge created by today’s abundance of data. Data Mining and Knowledge Discovery Handbook, 2nd Edition organizes the most current concepts, theories, standards, methodologies, trends, challenges and applications of data mining (DM) and knowledge discovery. | 760 Mohamed Medhat Gaber Arkady Zaslavsky and Shonali Krishnaswamy Data stream mining performing traditional data mining techniques with lin-ear sublinear time and space complexity Muthukrishnan 2003 . Applications of data stream mining can vary from time-critical astronomical and geophysical applications to real-time decision support in business applications. Data stream mining has been used in many applications including Analyzing biosensor measurements around a city for security reasons Cormode and Muthukrishnan 2004 Analysis of simulation results and on-board sensors in scientific laboratories and spacecrafts has its potential in changing the mission plan or the experimental settings in real time Burl et al. 1999 Castano et al. 2003 Srivastava and Stroeve 2003 Tanner et al. 2002 Analysis of web logs and web clickstreams Nasraoui et al. 2003 Real-time analysis of data streams generated from stock markets Kargupta et al. 2002 A traveling salesperson performing customer profiling Grossman 1998 Continuous monitoring and analyzing of status information received for intrusion detection or laboratory experiments Gaber et al. 2004 Moskovitch et al. 2008 Analysis of data from sensors in moving vehicles to prevent fatal accidents through early detection by monitoring and analysis of status information Kar-gupta 2004 and Performing preliminary mining of data generated in a sensor network Krishna-machari and Iyengar 2003 Krishnamachari and Iyengar 2004 . Table shows the major differences between data stream processing and traditional data processing. The objective of this table is to clearly differentiate between traditional stored data processing and stream processing as a step towards focusing on the data mining aspects of data stream processing systems. Knowledge extraction from data streams has attracted attention in recent years Gaber et al. 2005 . The continuous high-speed generation of data from sensors web clickstreams and stock market information has created
