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 repre- sent the first two stages. Merging the generated knowledge structures when running out of memory represents the last stage. AOG has been used in clustering, classifica- tion and frequency counting (Gaber et al., 2005). Figure 39.8 shows a flowchart of AOG-mining process. It shows the sequence of the three stages of AOG. Fig. 39.8. AOG Approach Definitions, advantages and disadvantages of all of the above task-based ap- proaches are given in Table 39.3. 39.8 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 con- tinuous 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, stream-to-relation, and relation-to-stream operators. • Gigascope: is a specialized data stream management system (Cranor et al., 2003) for the application of network monitoring. It has its own SQL-like query language termed as GSQL. Unlike CQL, the input and output of this language are only 39 Data Stream Mining 781 Table 39.3. Task-based Techniques Technique Definition Pros Cons Approximation Al- gorithms Design algorithms that approximate mining results with error bounds. • Efficiency in running time. • the problem of data rates with regard to the avail- able resources could not be solved using approximation algorithms. Sliding Window Analyzing the most recent data streams • Applicable to most of data stream applications. • don’t provide a model for the whole data stream. Algorithm Output Granularity Adapting the algorithm param- eters according to data stream rate and memory consumption • Generic ap- proach that could be used with any mining technique with no or minor modifications • It has an over- head when run- ning for long period of time data streams. GSQL supports merge, selection, join and aggregation operations on data streams. Query optimization and performance considerations have been addressed in developing the language. The system serves a number of network related applications including intrusion detection and traffic analysis. • TelegraphCQ: is a continuous query processing system (Krishnamurthy et al., 2003) built on the basis of PostgreSQL open source query language. The system supports creating data streams, sources, wrappers and queries. • COUGAR: is a data stream management system (Yao and Gehrke, 2002) de- signed for sensor networks. Motivated by the fact that local computation in sen- sor networks is cheaper than transferring data generated from sensors over wire- less connections, a loosely coupled distributed architecture has been proposed to answer in-network queries. • Aurora: is a data stream management system (Abadi et al., 2003) that has the optimization features for load shedding, real-time query scheduling and QoS as- sessment. It is mainly designed to deal with very large numbers of data streams. 782 Mohamed Medhat Gaber, Arkady Zaslavsky, and Shonali Krishnaswamy Queries over data streams have some similarities with data stream mining in terms of research issues and challenges. The two main constraints for querying data streams are the unbounded memory requirement and the high data rate. Thus, the computation time per data element/record should be less than the data rate or the sampling rate. Furthermore, the unbounded memory requirement compounds the challenge by necessitating approximate rather than exact results. Significant re- search efforts have been conducted to approximate the query results (Babcock et al., 2002, Garofalakis et al., 2002b). The data stream mining algorithms have used some of the techniques introduced in the data stream management research. Sampling and load shedding (Muthukrish- nan, 2003) are among the basic techniques that have been introduced in querying data streams and extended to the data mining process. 39.9 Future Directions The field of data stream mining is in a nascent stage of evolution. The last few years have witnessed increased attention to this area of research due to the dissemination of data stream sources. Based on the state-of-the-art in the area and demands of data streaming applications, we can identify the future directions of research as follows: • Developing data mining algorithms for wireless sensor networks to serve a num- ber of real-time critical applications. • Online medical, scientific and biological data stream mining using data generated from medical, biological instruments and various tools employed in scientific laboratories. • Hardware solutions to small devices emitting or receiving data streams in order to enable high performance computation on small devices. • Developing software architectures that serve data streaming applications. 39.10 Summary In this chapter, a review of the state of the art in mining data streams has been pre- sented. Clustering, classification, frequency counting, time series analysis techniques have been discussed. Different systems that use data stream mining techniques have been also presented. Generalization of the approaches used in developing data stream mining techniques is given. The approaches have been broadly classified into data- based and task-based strategies. Sampling, load shedding, sketching, synopsis data structure creation and aggregation represent the data-based approaches. Approxi- mation algorithms, sliding window and algorithm output granularity are the two ap- proaches that form the task-based approaches. The chapter is concluded with pointers to future research directions in the area. 39 Data Stream Mining 783 References A. Arasu, B. Babcock. S. Babu, M. Datar, K. Ito, I. Nishizawa, J. Rosenstein, and J. Widom. STREAM: The Stanford Stream Data Manager Demonstration description - short overview of system status and plans, in Proc. of the ACM Intl Conf. on Manage- ment of Data (SIGMOD 2003), June 2003, pp. 665 - 665. D. Abadi, D. Carney, U. Cetintemel, M. Cherniack, C. Convey, C. Erwin, E. Galvez, M. Hatoun, J. Hwang, A. Maskey, A. Rasin, A. Singer, M. 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Han, Mining Concept-Drifting Data Streams using Ensemble Classifiers, in the 9th ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD), Aug. 2003, Washington DC, USA. Y. Zhu and D. Shasha, Efficient Elastic Burst Detection in Data Streams, The Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining KDD- 2003 24 August 2003 - 27 August 2003, pp 336 - 345. 40 Mining Concept-Drifting Data Streams Haixun Wang 1 , Philip S. Yu 2 , and Jiawei Han 3 1 IBM T. J. Watson Research Center haixun@us.ibm.com 2 IBM T. J. Watson Research Center psyu@us.ibm.com 3 University of Illinois, Urbana Champaign hanj@cs.uiuc.edu Summary. Knowledge discovery from infinite data streams is an important and difficult task. We are facing two challenges, the overwhelming volume and the concept drifts of the stream- ing data. In this chapter, we introduce a general framework for mining concept-drifting data streams using weighted ensemble classifiers. We train an ensemble of classification models, such as C4.5, RIPPER, naive Bayesian, etc., from sequential chunks of the data stream. The classifiers in the ensemble are judiciously weighted based on their expected classification ac- curacy on the test data under the time-evolving environment. Thus, the ensemble approach improves both the efficiency in learning the model and the accuracy in performing classifica- tion. Our empirical study shows that the proposed methods have substantial advantage over single-classifier approaches in prediction accuracy, and the ensemble framework is effective for a variety of classification models. Key words: Data Mining, concept learning, classifier design and evaluation 40.1 Introduction Knowledge discovery on streaming data is a research topic of growing interest (Bab- cock et al., 2002, Chen et al., 2002, Domingos and Hulten, 2000, Hulten et al., 2001). 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