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Inhaltsangabe
Most developed learning algorithms are designed for environments in which all the relevant data is stored at single computer site. Advances in network technology and the Internet as well as the growing size of data have contributed to the proliferation of distributed data mining. Therefore, previously developed learning algorithms for computations with such distributed databases require that all data stored in distributed locations must be transferred to a common site and recompiled as one complete and local dataset before the construction can take place. The danger in this transfer is obvious if the data itself is innately sensitive, or the necessary bandwidth to efficiently transmit the data to a single site is not available. In this book, new algorithms have been developed to preserve the privacy of the data and minimize the cost of communication among the database nodes by gathering statistical summaries at each distributed database and then passing messages describing those summaries between the participating sites. This is much more efficient than transferring the complete databases to a single site, join these databases, and then execute algorithms with this data.
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Reseña del editor
Most developed learning algorithms are designed for environments in which all the relevant data is stored at single computer site. Advances in network technology and the Internet as well as the growing size of data have contributed to the proliferation of distributed data mining. Therefore, previously developed learning algorithms for computations with such distributed databases require that all data stored in distributed locations must be transferred to a common site and recompiled as one complete and local dataset before the construction can take place. The danger in this transfer is obvious if the data itself is innately sensitive, or the necessary bandwidth to efficiently transmit the data to a single site is not available. In this book, new algorithms have been developed to preserve the privacy of the data and minimize the cost of communication among the database nodes by gathering statistical summaries at each distributed database and then passing messages describing those summaries between the participating sites. This is much more efficient than transferring the complete databases to a single site, join these databases, and then execute algorithms with this data.
Biografía del autor
Ibrahim Attiya is a faculty member at Zagazig University, Egypt. He received his BSc and MSc degrees in Computer Science from the Faculty of Science, Zagazig University, Egypt. Currently, he is a PhD candidate at University of Science and Technology Beijing (USTB), Beijing, China. His research interests include Distributed and Cloud Computing.
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Efficient Learning Algorithms for Distributed Databases
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LAP LAMBERT Academic Publishing, 2014
ISBN 10: 3659477923
ISBN 13: 9783659477928
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Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Attiya IbrahimIbrahim Attiya is a faculty member at Zagazig University, Egypt. He received his BSc and MSc degrees in Computer Science from the Faculty of Science, Zagazig University, Egypt. Currently, he is a PhD candidate at Univer. Bestandsnummer des Verkäufers 5158518
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Efficient Learning Algorithms for Distributed Databases
Verlag:
LAP LAMBERT Academic Publishing, 2014
ISBN 10: 3659477923
ISBN 13: 9783659477928
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Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Most developed learning algorithms are designed for environments in which all the relevant data is stored at single computer site. Advances in network technology and the Internet as well as the growing size of data have contributed to the proliferation of distributed data mining. Therefore, previously developed learning algorithms for computations with such distributed databases require that all data stored in distributed locations must be transferred to a common site and recompiled as one complete and local dataset before the construction can take place. The danger in this transfer is obvious if the data itself is innately sensitive, or the necessary bandwidth to efficiently transmit the data to a single site is not available. In this book, new algorithms have been developed to preserve the privacy of the data and minimize the cost of communication among the database nodes by gathering statistical summaries at each distributed database and then passing messages describing those summaries between the participating sites. This is much more efficient than transferring the complete databases to a single site, join these databases, and then execute algorithms with this data. Bestandsnummer des Verkäufers 9783659477928
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Efficient Learning Algorithms for Distributed Databases
Verlag:
LAP LAMBERT Academic Publishing Nov 2014, 2014
ISBN 10: 3659477923
ISBN 13: 9783659477928
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Anbieter: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Deutschland
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Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Most developed learning algorithms are designed for environments in which all the relevant data is stored at single computer site. Advances in network technology and the Internet as well as the growing size of data have contributed to the proliferation of distributed data mining. Therefore, previously developed learning algorithms for computations with such distributed databases require that all data stored in distributed locations must be transferred to a common site and recompiled as one complete and local dataset before the construction can take place. The danger in this transfer is obvious if the data itself is innately sensitive, or the necessary bandwidth to efficiently transmit the data to a single site is not available. In this book, new algorithms have been developed to preserve the privacy of the data and minimize the cost of communication among the database nodes by gathering statistical summaries at each distributed database and then passing messages describing those summaries between the participating sites. This is much more efficient than transferring the complete databases to a single site, join these databases, and then execute algorithms with this data. 132 pp. Englisch. Bestandsnummer des Verkäufers 9783659477928
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Efficient Learning Algorithms for Distributed Databases
Verlag:
LAP LAMBERT Academic Publishing Nov 2014, 2014
ISBN 10: 3659477923
ISBN 13: 9783659477928
Neu
Taschenbuch
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Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Most developed learning algorithms are designed for environments in which all the relevant data is stored at single computer site. Advances in network technology and the Internet as well as the growing size of data have contributed to the proliferation of distributed data mining. Therefore, previously developed learning algorithms for computations with such distributed databases require that all data stored in distributed locations must be transferred to a common site and recompiled as one complete and local dataset before the construction can take place. The danger in this transfer is obvious if the data itself is innately sensitive, or the necessary bandwidth to efficiently transmit the data to a single site is not available. In this book, new algorithms have been developed to preserve the privacy of the data and minimize the cost of communication among the database nodes by gathering statistical summaries at each distributed database and then passing messages describing those summaries between the participating sites. This is much more efficient than transferring the complete databases to a single site, join these databases, and then execute algorithms with this data.Books on Demand GmbH, Überseering 33, 22297 Hamburg 132 pp. Englisch. Bestandsnummer des Verkäufers 9783659477928
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