Data Mining and Knowledge Discovery Handbook, 2 Edition part 81. 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. | 780 Mohamed Medhat Gaber Arkady Zaslavsky and Shonali Krishnaswamy constrained device that generates or receive streams of information. AOG has three main stages. Mining followed by adaptation to resources and data stream rates represent the first two stages. Merging the generated knowledge structures when running out of memory represents the last stage. AOG has been used in clustering classification and frequency counting Gaber et al. 2005 . Figure shows a flowchart of AOG-mining process. It shows the sequence of the three stages of AOG. Fig. . AOG Approach Definitions advantages and disadvantages of all of the above task-based approaches are given in Table . Related Work The last few years have witnessed the emergence of data management strategies focusing on data stream issues Babcock et al. 2002 . Querying and summarizing data that could be stored for further analysis are the main processing tasks studied in data stream management systems. Extension of query languages query planning scheduling and optimization are the major research activities conducted in this area. Aurora Abadi et al. 2003 COUGAR Yao and Gehrke 2002 Gigascope Cra-nor et al. 2003 STREAM Arasu et al. 2003 TelegraphCQ Krishnamurthy et al. 2003 represent the first generation of data stream management systems. In this section a brief description of each one is given as follows STREAM STanford stREam datA Manager STREAM Arasu et al. 2003 is a data stream management system that handles multiple continuous data streams and supports long-running continuous queries. The intermediate results of a continuous query are stored in a data structure termed Scratch Store. The results of a query could be a data stream transferred to the user or it could be a relation that also could be stored for re-processing. To support continuous queries over data streams a continuous query language termed as CQL has been developed as part of the system. The language supports relation-to-relation .
