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Data Management is the process of planning, coordinating and controlling data resources. More often, applications need to store and search a large amount of data. Managing Data has been continuously challenged by demands from various areas and applications and has evolved in parallel with advances in hardware and computing techniques. This volume focuses on its recent advances and it is composed of five parts and a total of eighteen chapters. The first part of the book contains five contributions in the area of information retrieval & Web intelligence: a novel approach to solving index selection problem, integrated retrieval from Web of documents and data, bipolarity in database querying, deriving data summarization through ontologies, and granular computing for Web intelligence. The second part of the book contains four contributions in knowledge discovery area. Its third part contains three contributions in information integration & data security area. The remaining two parts of the book contain six contributions in the area of intelligent agents and applications of data management in medical domain.

Computer Science --- Applied Mathematics --- Civil Engineering --- Civil & Environmental Engineering --- Engineering & Applied Sciences --- Database management. --- Database security. --- Information storage and retrieval systems. --- Medicine --- Research --- Data processing. --- Clinical sciences --- Medical profession --- 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 --- Data base security --- Databases --- 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 --- Security measures --- Computer science. --- Data structures (Computer science). --- Artificial intelligence. --- Applied mathematics. --- Engineering mathematics. --- Computer Science. --- Artificial Intelligence (incl. Robotics). --- Data Structures, Cryptology and Information Theory. --- Appl.Mathematics/Computational Methods of Engineering. --- Engineering --- Engineering analysis --- Mathematical analysis --- 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 --- Information structures (Computer science) --- Structures, Data (Computer science) --- Structures, Information (Computer science) --- File organization (Computer science) --- Abstract data types (Computer science) --- Informatics --- Science --- Mathematics --- Human biology --- Life sciences --- Medical sciences --- Pathology --- Physicians --- Computer systems --- Electronic information resources --- Data libraries --- Digital libraries --- Information organization --- Information retrieval --- Computer security --- Data structures (Computer scienc. --- Artificial Intelligence. --- Data Structures and Information Theory. --- Mathematical and Computational Engineering.

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Engineering sciences. Technology --- Artificial intelligence. Robotics. Simulation. Graphics --- analyse (wiskunde) --- KI (kunstmatige intelligentie) --- ingenieurswetenschappen --- robots --- AI (artificiële intelligentie)

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This volume gives an overview of research done by CCI faculty in the area of Information & Intelligent Systems. Presented papers focus on recent advances in four major directions: Complex Systems, Knowledge Management, Knowledge Discovery, and Visualization. A major reason for producing this book was to demonstrate a new, important thrust in academic research where college-wide interdisciplinary efforts are brought to bear on large, general, and important problems. As shown in the research described here, these efforts need not be formally organized joint undertakings (through parts could be) but are rather a convergence of interests around grand themes. Taken together, the sections and chapters in this book reveal a variety of methods and tools that can be applied to quite diverse types of data and analysis problems but where each produces knowledge and features that can be combined, organized, explored, understood, and used in common ways (for example, through interactive visualization interfaces).

Engineering sciences. Technology --- Artificial intelligence. Robotics. Simulation. Graphics --- analyse (wiskunde) --- KI (kunstmatige intelligentie) --- ingenieurswetenschappen --- robots

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The aim of this book is to gather the most recent works that address issues related to the concept of mining complex data. The whole knowledge discovery process being involved, our goal is to provide researchers dealing with each step of this process by key entries. Actually, managing complex data within the KDD process implies to work on every step, starting from the pre-processing (e.g. structuring and organizing) to the visualization and interpretation (e.g. sorting or filtering) of the results, via the data mining methods themselves (e.g. classification, clustering, frequent patterns extraction, etc.). The papers presented here are selected from the workshop papers held yearly since 2006. The book is composed of four parts and a total of sixteen chapters. Part I gives a general view of complex data mining by illustrating some situations and the related complexity. It contains five chapters. Chapter 1 illustrates the problem of analyzing the scientific literature. The chapter gives some background to the various techniques in this area, explains the necessary pre-processing steps involved, and presents two case studies, one from image mining and one from table identification.

Engineering sciences. Technology --- Artificial intelligence. Robotics. Simulation. Graphics --- analyse (wiskunde) --- data mining --- ingenieurswetenschappen --- robots

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The aim of this book is to gather the most recent works that address issues related to the concept of mining complex data. The whole knowledge discovery process being involved, our goal is to provide researchers dealing with each step of this process by key entries. Actually, managing complex data within the KDD process implies to work on every step, starting from the pre-processing (e.g. structuring and organizing) to the visualization and interpretation (e.g. sorting or filtering) of the results, via the data mining methods themselves (e.g. classification, clustering, frequent patterns extraction, etc.). The papers presented here are selected from the workshop papers held yearly since 2006. The book is composed of four parts and a total of sixteen chapters. Part I gives a general view of complex data mining by illustrating some situations and the related complexity. It contains five chapters. Chapter 1 illustrates the problem of analyzing the scientific literature. The chapter gives some background to the various techniques in this area, explains the necessary pre-processing steps involved, and presents two case studies, one from image mining and one from table identification.

Engineering sciences. Technology --- Artificial intelligence. Robotics. Simulation. Graphics --- analyse (wiskunde) --- data mining --- ingenieurswetenschappen --- robots