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Information systems --- Temporal data bases --- Temporal databases.
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Artificial intelligence --- Reasoning --- Time --- Temporal databases --- Temporal data bases --- Databases
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Artificial intelligence --- Reasoning --- Time --- Temporal databases --- Computer Science --- Engineering & Applied Sciences --- Temporal data bases --- Databases
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Artificial intelligence --- Reasoning --- Time --- Temporal databases --- Computer Science --- Engineering & Applied Sciences --- Temporal data bases --- Databases
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Artificial intelligence --- Reasoning --- Time --- Temporal databases --- Engineering & Applied Sciences --- Computer Science --- Temporal data bases --- Databases
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Artificial intelligence --- Reasoning --- Time --- Temporal databases --- Computer Science --- Engineering & Applied Sciences --- Temporal data bases --- Databases
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Artificial intelligence --- Reasoning --- Time --- Temporal databases --- Computer Science --- Engineering & Applied Sciences --- Temporal data bases --- Databases
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Temporal databases --- Time --- Logic, Symbolic and mathematical --- Temporal data bases --- Databases
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Time and Relational Theory provides an in-depth description of temporal database systems, which provide special facilities for storing, querying, and updating historical and future data. Traditionally, database management systems provide little or no special support for temporal data at all. This situation is changing because: Cheap storage enables retention of large volumes of historical data in data warehousesUsers are now faced with temporal data problems, and need solutions Temporal features have recently been incorporated into the SQL standard, and vendors have begun to add temporal su
Database management. --- Temporal databases. --- Temporal data bases --- 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 --- Electronic data processing
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Temporal Data Mining via Unsupervised Ensemble Learning provides the principle knowledge of temporal data mining in association with unsupervised ensemble learning and the fundamental problems of temporal data clustering from different perspectives. By providing three proposed ensemble approaches of temporal data clustering, this book presents a practical focus of fundamental knowledge and techniques, along with a rich blend of theory and practice. Furthermore, the book includes illustrations of the proposed approaches based on data and simulation experiments to demonstrate all methodologies, and is a guide to the proper usage of these methods. As there is nothing universal that can solve all problems, it is important to understand the characteristics of both clustering algorithms and the target temporal data so the correct approach can be selected for a given clustering problem. Scientists, researchers, and data analysts working with machine learning and data mining will benefit from this innovative book, as will undergraduate and graduate students following courses in computer science, engineering, and statistics. Includes fundamental concepts and knowledge, covering all key tasks and techniques of temporal data mining, i.e., temporal data representations, similarity measure, and mining tasks Concentrates on temporal data clustering tasks from different perspectives, including major algorithms from clustering algorithms and ensemble learning approaches Presents a rich blend of theory and practice, addressing seminal research ideas and looking at the technology from a practical point-of-view
Data mining. --- Temporal databases. --- Temporal data bases --- Databases --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching
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