Data Streams

Cover -- TOC$Contents -- List of Figures -- List of Tables -- Preface -- CH$1 An Introduction to Data Streams -- 1. Introduction -- 2. Stream Mining Algorithms -- 3. Conclusions and Summary -- References -- CH$2 On Clustering Massive Data Streams: A Summarization Paradigm -- 1. Introduction -- 2. The Micro-clustering Based Stream Mining Framework -- 3. Clustering Evolving Data Streams: A Micro-clustering Approach -- 3.1 Micro-clustering Challenges -- 3.2 Online Micro-cluster Maintenance: The CluStream Algorithm -- 3.3 High Dimensional Projected Stream Clustering -- 4. Classification of Data Streams: A Micro-clustering Approach -- 4.1 On-Demand Stream Classification -- 5 . Other Applications of Micro-clustering and Research Directions -- 6. Performance Study and Experimental Results -- 7. Discussion -- References -- CH$3 A Survey of Classification Methods in Data Streams -- 1. Introduction -- 2. Research Issues -- 3. Solution Approaches -- 4. Classification Techniques -- 4.1 Ensemble Based Classification -- 4.2 Very Fast Decision Trees (VFDT) -- 4.3 On Demand Classification -- 4.4 Online Information Network (OLIN) -- 4.5 LWClass Algorithm -- 4.6 ANNCAD Algorithm -- 4.7 SCALLOP Algorithm -- 5. Summary -- References -- CH$4 Frequent Pattern Mining in Data Streams -- 1. Introduction -- 2. Overview -- 3. New Algorithm -- 4. Work on Other Related Problems -- 5. Conclusions and Future Directions -- References -- CH$5 A Survey of Change Diagnosis Algorithms in Evolving Data Streams -- 1. Introduction -- 2. The Velocity Density Method -- 2.1 Spatial Velocity Profiles -- 2.2 Evolution Computations in High Dimensional Case -- 2.3 On the use of clustering for characterizing stream evolution -- 3. On the Effect of Evolution in Data Mining Algorithms -- 4. Conclusions -- References -- CH$6 Multi-Dimensional Analysis of Data Streams Using Stream Cubes -- 1. Introduction -- 2. Problem Definition -- 3. Architecture for On-line Analysis of Data Streams -- 3.1 Tilted time frame -- 3.2 Critical layers -- 3.3 Partial materialization of stream cube -- 4. Stream Data Cube Computation -- 4.1 Algorithms for cube computation -- 5. Performance Study -- 6. Related Work -- 7. Possible Extensions -- 8. Conclusions -- References -- CH$7 Load Shedding in Data Stream Systems -- 1. Load Shedding for Aggregation Queries -- 1.1 Problem Formulation -- 1.2 Load Shedding Algorithm -- 1.3 Extensions -- 2. Load Shedding in Aurora -- 3. Load Shedding for Sliding Window Joins -- 4. Load Shedding for Classification Queries -- 5. Summary -- References -- CH$8 The Sliding-Window Computation Model and Results -- 0.1 Motivation and Road Map -- 1. A Solution to the BASICCOUNTING Problem -- 1.1 The Approximation Scheme -- 2. Space Lower Bound for BASICCOUNTING Problem -- 3. Beyond 0's and 1's -- 4. References and Related Work -- 5. Conclusion -- References -- CH$9 A Survey of Synopsis Construction in Data Streams -- 1. Introduction -- 2. Sampling Methods -- 2.1 Random S.