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In recent years, the progress in hardware technology has made it possible for organizations to store and record large streams of transactional data. Such data sets which continuously and rapidly grow over time are referred to as data streams. Data Streams: Models and Algorithms primarily discusses issues related to the mining aspects of data streams rather than the database management aspect of streams. This volume covers mining aspects of data streams in a comprehensive style. Each contributed chapter, from a variety of well known researchers in the data mining field, contains a survey on the topic, the key ideas in the field from that particular topic, and future research directions. Data Streams: Models and Algorithms is intended for a professional audience composed of researchers and practitioners in industry. This book is also appropriate for graduate-level students in computer science. Charu C. Aggarwal obtained his B.Tech in Computer Science from IIT Kanpur in 1993 and Ph.D. from MIT in 1996. He has been a Research Staff Member at IBM since then, and has published over 90 papers in major conferences and journals in the database and data mining field. He has applied for, or been granted, over 50 US and International patents, and has twice been designated Master Inventor at IBM for the commercial value of his patents. He has been granted 14 invention achievement awards by IBM for his patents. His work on real time bio-terrorist threat detection in data streams won the IBM Epispire award for environmental excellence in 2003. He has served on the program committee of most major database conferences, and was program chair for the Data Mining and Knowledge Discovery Workshop, 2003, and a program vice-chair for the SIAM Conference on Data Mining, 2007. He is an associate editor of the IEEE Transactions on Data Engineering and an action editor of the Data Mining and Knowledge Discovery Journal. He is a senior member of the IEEE.

Algorithms --- Computer science --- Data mining --- 681.3*E1 --- 681.3*E2 --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- Computer mathematics --- Discrete mathematics --- Electronic data processing --- Algorism --- Algebra --- Arithmetic --- 681.3*E2 Data storage representations: composite structures; contiguous representations; hash-table representations; linked representations; primitive data items --- Data storage representations: composite structures; contiguous representations; hash-table representations; linked representations; primitive data items --- 681.3*E1 Data structures: arrays; graphs; lists; tables; trees --- Data structures: arrays; graphs; lists; tables; trees --- Mathematics --- Foundations --- Algorithms. --- Data mining. --- Mathematics. --- Artificial intelligence. --- Database management. --- Information storage and retrieva. --- Multimedia systems. --- Computer Communication Networks. --- Data Mining and Knowledge Discovery. --- Artificial Intelligence. --- Database Management. --- Information Storage and Retrieval. --- Multimedia Information Systems. --- Information storage and retrieval. --- Data base management --- Data services (Database management) --- Database management services --- DBMS (Computer science) --- Generalized data management systems --- Services, Database management --- Systems, Database management --- Systems, Generalized database management --- Computer-based multimedia information systems --- Multimedia computing --- Multimedia information systems --- Multimedia knowledge systems --- Information storage and retrieval systems --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Information storage and retrieval systems. --- Automatic data storage --- Automatic information retrieval --- Automation in documentation --- Computer-based information systems --- Data processing systems --- Data storage and retrieval systems --- Discovery systems, Information --- Information discovery systems --- Information processing systems --- Information retrieval systems --- Machine data storage and retrieval --- Mechanized information storage and retrieval systems --- Computer systems --- Electronic information resources --- Data libraries --- Digital libraries --- Information organization --- Information retrieval --- Multimedia information systems. --- Computer communication systems. --- Communication systems, Computer --- Computer communication systems --- Data networks, Computer --- ECNs (Electronic communication networks) --- Electronic communication networks --- Networks, Computer --- Teleprocessing networks --- Data transmission systems --- Digital communications --- Electronic systems --- Information networks --- Telecommunication --- Cyberinfrastructure --- Network computers --- Distributed processing

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This book comprehensively covers the topic of recommender systems, which provide personalized recommendations of products or services to users based on their previous searches or purchases. Recommender system methods have been adapted to diverse applications including query log mining, social networking, news recommendations, and computational advertising. This book synthesizes both fundamental and advanced topics of a research area that has now reached maturity. The chapters of this book are organized into three categories: - Algorithms and evaluation: These chapters discuss the fundamental algorithms in recommender systems, including collaborative filtering methods, content-based methods, knowledge-based methods, ensemble-based methods, and evaluation. - Recommendations in specific domains and contexts: the context of a recommendation can be viewed as important side information that affects the recommendation goals. Different types of context such as temporal data, spatial data, social data, tagging data, and trustworthiness are explored. - Advanced topics and applications: Various robustness aspects of recommender systems, such as shilling systems, attack models, and their defenses are discussed. In addition, recent topics, such as learning to rank, multi-armed bandits, group systems, multi-criteria systems, and active learning systems, are introduced together with applications. Although this book primarily serves as a textbook, it will also appeal to industrial practitioners and researchers due to its focus on applications and references. Numerous examples and exercises have been provided, and a solution manual is available for instructors.

Computer Science --- Engineering & Applied Sciences --- Data mining. --- Artificial intelligence. --- Data Mining and Knowledge Discovery. --- Artificial Intelligence. --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- (Produktform)Paperback / softback. --- (BISAC Subject Heading)COM021030. --- (BISAC Subject Heading)UNF. --- Artificial intelligence --- Data mining --- Recommender systems (Information filtering) --- Computer science --- Information systems

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Advances in hardware technology have lead to an ability to collect data with the use of a variety of sensor technologies. In particular sensor notes have become cheaper and more efficient, and  have even been integrated into day-to-day devices of use, such as mobile phones. This has lead to a much larger scale of applicability and mining of sensor data sets. The human-centric aspect of sensor data has created tremendous opportunities in integrating social aspects of sensor data collection into the mining process.  Managing and Mining Sensor Data is a contributed volume by prominent leaders in this field, targeting advanced-level students in computer science as a secondary text book or reference. Practitioners and researchers working in this field will also find this book useful. .

Computer crimes -- Investigation. --- Computer networks -- Security measures. --- Computer security. --- Data mining. --- Data mining --- Multisensor data fusion --- Sensor networks --- Engineering & Applied Sciences --- Electrical & Computer Engineering --- Computer Science --- Electrical Engineering --- Sensor networks. --- Information retrieval. --- Computer storage devices. --- Computer memory systems --- Computers --- Electronic digital computers --- Storage devices, Computer --- Data retrieval --- Data storage --- Discovery, Information --- Information discovery --- Information storage and retrieval --- Retrieval of information --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Networks, Sensor --- Memory systems --- Storage devices --- Computer science. --- Computer communication systems. --- Database management. --- Information storage and retrieval. --- Computer Science. --- Data Mining and Knowledge Discovery. --- Database Management. --- Information Storage and Retrieval. --- Computer Communication Networks. --- Information Systems Applications (incl. Internet). --- Database searching --- Computer input-output equipment --- Memory management (Computer science) --- Documentation --- Information science --- Information storage and retrieval systems --- Detectors --- Context-aware computing --- Information storage and retrieva. --- Data base management --- Data services (Database management) --- Database management services --- DBMS (Computer science) --- Generalized data management systems --- Services, Database management --- Systems, Database management --- Systems, Generalized database management --- Electronic data processing --- Information storage and retrieval systems. --- Automatic data storage --- Automatic information retrieval --- Automation in documentation --- Computer-based information systems --- Data processing systems --- Data storage and retrieval systems --- Discovery systems, Information --- Information discovery systems --- Information processing systems --- Information retrieval systems --- Machine data storage and retrieval --- Mechanized information storage and retrieval systems --- Computer systems --- Electronic information resources --- Data libraries --- Digital libraries --- Information organization --- Information retrieval --- Application software. --- Application computer programs --- Application computer software --- Applications software --- Apps (Computer software) --- Computer software --- Communication systems, Computer --- Computer communication systems --- Data networks, Computer --- ECNs (Electronic communication networks) --- Electronic communication networks --- Networks, Computer --- Teleprocessing networks --- Data transmission systems --- Digital communications --- Electronic systems --- Information networks --- Telecommunication --- Cyberinfrastructure --- Network computers --- Distributed processing --- Multisensor data fusion.

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Managing and Mining Uncertain Data contains surveys by well known researchers in the field of uncertain databases. The book presents the most recent models, algorithms, and applications in the uncertain data field in a structured and concise way. This book is organized so as to cover the most important management and mining topics in the field. The idea is to make it accessible not only to researchers, but also to application-driven practitioners for solving real problems. Given the lack of structurally organized information on the new and emerging area of uncertain data, this book provides insights which are not easily accessible elsewhere. Managing and Mining Uncertain Data is designed for a varied audience composed of professors, researchers and practitioners in industry. This book is also suitable as a reference book for advanced-level database students in computer science and engineering. Editor Biography Charu C. Aggarwal obtained his B.Tech in Computer Science from IIT Kanpur in 1993 and Ph.D. from MIT in 1996. He has been a Research Staff Member at IBM since then, and has published over 120 papers in major conferences and journals in the database and data mining field. He has applied for or been granted over 65 US and International patents, and has thrice been designated Master Inventor at IBM for the commercial value of his patents. He has been granted 17 invention achievement awards by IBM for his patents. His work on real time bio-terrorist threat detection in data streams won the IBM Corporate award for Environmental Excellence in 2003. He is a recipient of the IBM Outstanding Innovation Award in 2008 for his scientific contributions to privacy technology, and a recipient of the IBM Research Division award for his contributions to stream mining for the System S project. He has served on the program committee of most major database conferences, and was program chair for the Data Mining and Knowledge Discovery Workshop, 2003, and program vice-chairs for the SIAM Conference on Data Mining 2007, ICDM Conference 2007, and the WWW Conference, 2009. He served as an associate editor of the IEEE Transactions on Data Engineering from 2004 to 2008. He is an associate editor of the ACM SIGKDD Explorations and an action editor of the Data Mining and Knowledge Discovery Journal. He is a senior member of the IEEE and a life-member of the ACM.

Applied human geography. --- Crime analysis. --- Crime prevention. --- Crime --- Applied human geography --- Crime analysis --- Criminology, Penology & Juvenile Delinquency --- Social Welfare & Social Work --- Social Sciences --- Applied anthropogeography --- Applied cultural geography --- Applied social geography --- Analysis --- Geochemistry, Biogeochemistry --- Geochemistry, Biogeochemistry. --- Brottsbekämpning. --- Crime prevention measures. --- Geographic distribution of crime. --- Measure of uncertainty (Information theory) --- Shannon's measure of uncertainty --- System uncertainty --- Crime. --- City crime --- Crime and criminals --- Crimes --- Delinquency --- Felonies --- Misdemeanors --- Urban crime --- Social aspects --- Computer science. --- Computer security. --- Database management. --- Data mining. --- Information storage and retrieval. --- Artificial intelligence. --- Computer Science. --- Data Mining and Knowledge Discovery. --- Database Management. --- Artificial Intelligence (incl. Robotics). --- Information Storage and Retrieval. --- Systems and Data Security. --- Information Systems Applications (incl. Internet). --- Sociology. --- Demography. --- Criminology and Criminal Justice. --- Criminology and Criminal Justice, general. --- Sociology, general. --- Police administration --- Social problems --- Criminal justice, Administration of --- Criminal law --- Criminals --- Criminology --- Transgression (Ethics) --- Human geography --- 504 --- 550.47 --- 550.47 Biogeochemistry --- Biogeochemistry --- 504 Environment. Environmental science --- Environment. Environmental science --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Data base management --- Data services (Database management) --- Database management services --- DBMS (Computer science) --- Generalized data management systems --- Services, Database management --- Systems, Database management --- Systems, Generalized database management --- Computer privacy --- Computer system security --- Computer systems --- Computers --- Cyber security --- Cybersecurity --- Electronic digital computers --- Protection of computer systems --- Security of computer systems --- Data protection --- Security systems --- Hacking --- Informatics --- Science --- Protection --- Security measures --- Uncertainty (Information theory) --- Information measurement --- Probabilities --- Questions and answers --- Biogeochemical cycles. --- Environmental chemistry. --- Environmental Sciences and Forestry. Geology --- Environmental Sciences and Forestry. Environmental Management --- Environmental Pollution. --- Biogeochemical cycles --- Environmental chemistry --- Chemistry, Environmental --- Chemistry --- Ecology --- Cycles --- Chimie de l'environnement --- Pollution --- Environmental aspects --- Pollutants --- Toxicology --- Criminology. --- Information storage and retrieva. --- Artificial Intelligence. --- Historical demography --- Social sciences --- Population --- Vital statistics --- Study and teaching --- Information storage and retrieval systems. --- Automatic data storage --- Automatic information retrieval --- Automation in documentation --- Computer-based information systems --- Data processing systems --- Data storage and retrieval systems --- Discovery systems, Information --- Information discovery systems --- Information processing systems --- Information retrieval systems --- Machine data storage and retrieval --- Mechanized information storage and retrieval systems --- Electronic information resources --- Data libraries --- Digital libraries --- Information organization --- Information retrieval --- Application software. --- Application computer programs --- Application computer software --- Applications software --- Apps (Computer software) --- Computer software --- Social theory --- Ecology. --- Metals. --- Adsorption --- Chemical industry. --- Chemical industry --- Microbiology. --- Microbiology --- Oxidation. --- Oxidation --- Air --- Biological transport --- Photochemistry --- Plant metabolism --- Reaction (chemistry) --- Sedimentation --- Soils --- Solubility --- Chelation --- Metabolism (animal) --- Reaction kinetics --- Microbial degradation --- Asbestos --- Carbon black --- Cellulose --- Molybdenum --- Phosphorus --- ENVIRONMENTAL CHEMISTRY --- ENVIRONMENTAL TOXICOLOGY --- POLLUTION --- HYDROCARBONS, FLUORO --- MERCURY --- CADMIUM --- ALKANES --- DYES --- PIGMENTS --- ENVIRONMENTAL ASPECTS --- Amines --- Aromatic compounds --- Esters --- Phosphates --- Thallium

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This book comprehensively covers the topic of recommender systems, which provide personalized recommendations of products or services to users based on their previous searches or purchases. Recommender system methods have been adapted to diverse applications including query log mining, social networking, news recommendations, and computational advertising. This book synthesizes both fundamental and advanced topics of a research area that has now reached maturity. The chapters of this book are organized into three categories: - Algorithms and evaluation: These chapters discuss the fundamental algorithms in recommender systems, including collaborative filtering methods, content-based methods, knowledge-based methods, ensemble-based methods, and evaluation. - Recommendations in specific domains and contexts: the context of a recommendation can be viewed as important side information that affects the recommendation goals. Different types of context such as temporal data, spatial data, social data, tagging data, and trustworthiness are explored. - Advanced topics and applications: Various robustness aspects of recommender systems, such as shilling systems, attack models, and their defenses are discussed. In addition, recent topics, such as learning to rank, multi-armed bandits, group systems, multi-criteria systems, and active learning systems, are introduced together with applications. Although this book primarily serves as a textbook, it will also appeal to industrial practitioners and researchers due to its focus on applications and references. Numerous examples and exercises have been provided, and a solution manual is available for instructors.

Recommender systems (Information filtering). --- Computer science --- Information systems --- Artificial intelligence --- Data mining --- Computer Science --- Recommender systems (Information filtering)