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Temporal databases : theory, design, and implementation
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ISBN: 0805324135 Year: 1993 Volume: vol *1 Publisher: Bonn ; Madrid ; Redwood City The Benjamin/cummings publishing company

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Proceedings : 20th International Symposium on Temporal Representation and Reasoning (TIME 2013) : Pensacola, Florida, USA, 26-28 September 2013.
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ISBN: 1479922404 1479922420 1479922412 Year: 2013 Publisher: IEEE

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Thirteenth International Symposium on Temporal Representation and Reasoning (Time 2006) : proceedings : 15-17June, 2006, Budapest, Hungary
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ISBN: 0769526179 1509097805 Year: 2006 Publisher: [Place of publication not identified] IEEE Computer Society Press

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15th International Symposium on Temporal Representation and Reasoning : TIME 2008 proceedings : 16-18 June, 2008, Montreal, Quebec, Canada
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ISBN: 0769531814 1509084193 Year: 2008 Publisher: [Place of publication not identified] IEEE Computer Society

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16th International Symposium on Temporal Representation and Reasoning : proceedings : Bressanone-Brixen, Italy 23-25 July 2009
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ISBN: 0769537278 1509075143 Year: 2009 Publisher: [Place of publication not identified] IEEE Computer Society

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12th International Symposium on Temporal Representation and Reasoning (Time 2005) : proceedings : 23-25 June, 2005, Burlington, Vermont
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ISBN: 0769523706 1538602768 Year: 2005 Publisher: [Place of publication not identified] IEEE Computer Society Press

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14th International Symposium on Temporal Representation and Reasoning : proceedings : June 28-30, 2007, Alicante, Spain
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ISBN: 0769528368 1509089128 Year: 2006 Publisher: [Place of publication not identified] IEEE Computer Society Press

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TIME 2016 : proceedings : 23rd International Symposium on Temporal Representation and Reasoning : 17-19 October 2016, Kongens Lyngby, Denmark
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ISBN: 1509038256 1509038264 Year: 2016 Publisher: New York : IEEE,

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Time and relational theory
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ISBN: 0128006757 0128006315 1322076154 9781322076157 9780128006757 9780128006313 9780128006313 Year: 2014 Publisher: Waltham, Massachusetts

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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


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Temporal data mining via unsupervised ensemble learning
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ISBN: 0128118415 0128116544 9780128118412 9780128116548 Year: 2017 Publisher: Waltham, MA : Elsevier,

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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

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