The Experts below are selected from a list of 721623 Experts worldwide ranked by ideXlab platform
Niall Rooney - One of the best experts on this subject based on the ideXlab platform.
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Temporal Data mining for smart homes
Lecture Notes in Computer Science, 2006Co-Authors: Mykola Galushka, Dave Patterson, Niall RooneyAbstract:Temporal Data mining is a relatively new area of research in computer science. It can provide a large variety of different methods and techniques for handling and analyzing Temporal Data generated by smart-home environments. Temporal Data mining in general fits into a two level architecture, where initially a transformation technique reduces Data dimensionality in the first level and indexing techniques provide efficient access to the Data in the second level. This infrastructure of Temporal Data mining provides the basis for high-level Data mining operations such as clustering, classification, rule discovery and prediction. These operations can form the basis for developing different smart-home applications, capable of addressing a number of situations occurring within this environment. This paper outlines the main Temporal Data mining techniques available and provides examples of where they can be applied within a smart home environment.
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Designing Smart Homes - Temporal Data mining for smart homes
Lecture Notes in Computer Science, 2006Co-Authors: Mykola Galushka, Dave Patterson, Niall RooneyAbstract:Temporal Data mining is a relatively new area of research in computer science. It can provide a large variety of different methods and techniques for handling and analyzing Temporal Data generated by smart-home environments. Temporal Data mining in general fits into a two level architecture, where initially a transformation technique reduces Data dimensionality in the first level and indexing techniques provide efficient access to the Data in the second level. This infrastructure of Temporal Data mining provides the basis for high-level Data mining operations such as clustering, classification, rule discovery and prediction. These operations can form the basis for developing different smart-home applications, capable of addressing a number of situations occurring within this environment. This paper outlines the main Temporal Data mining techniques available and provides examples of where they can be applied within a smart home environment.
Mykola Galushka - One of the best experts on this subject based on the ideXlab platform.
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Temporal Data mining for smart homes
Lecture Notes in Computer Science, 2006Co-Authors: Mykola Galushka, Dave Patterson, Niall RooneyAbstract:Temporal Data mining is a relatively new area of research in computer science. It can provide a large variety of different methods and techniques for handling and analyzing Temporal Data generated by smart-home environments. Temporal Data mining in general fits into a two level architecture, where initially a transformation technique reduces Data dimensionality in the first level and indexing techniques provide efficient access to the Data in the second level. This infrastructure of Temporal Data mining provides the basis for high-level Data mining operations such as clustering, classification, rule discovery and prediction. These operations can form the basis for developing different smart-home applications, capable of addressing a number of situations occurring within this environment. This paper outlines the main Temporal Data mining techniques available and provides examples of where they can be applied within a smart home environment.
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Designing Smart Homes - Temporal Data mining for smart homes
Lecture Notes in Computer Science, 2006Co-Authors: Mykola Galushka, Dave Patterson, Niall RooneyAbstract:Temporal Data mining is a relatively new area of research in computer science. It can provide a large variety of different methods and techniques for handling and analyzing Temporal Data generated by smart-home environments. Temporal Data mining in general fits into a two level architecture, where initially a transformation technique reduces Data dimensionality in the first level and indexing techniques provide efficient access to the Data in the second level. This infrastructure of Temporal Data mining provides the basis for high-level Data mining operations such as clustering, classification, rule discovery and prediction. These operations can form the basis for developing different smart-home applications, capable of addressing a number of situations occurring within this environment. This paper outlines the main Temporal Data mining techniques available and provides examples of where they can be applied within a smart home environment.
Richard T. Snodgrass - One of the best experts on this subject based on the ideXlab platform.
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Temporal Data management
IEEE Transactions on Knowledge and Data Engineering, 1999Co-Authors: Christian S. Jensen, Richard T. SnodgrassAbstract:A wide range of Database applications manage time-varying information. Existing Database technology currently provides little support for managing such Data. The research area of Temporal Databases has made important contributions in characterizing the semantics of such information and in providing expressive and efficient means to model, store, and query Temporal Data. This paper introduces the reader to Temporal Data management, surveys state-of-the-art solutions to challenging aspects of Temporal Data management, and points to research directions.
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Three Proposals for a Third-Generation Temporal Data Model
1993Co-Authors: Christian S. Jensen, Richard T. SnodgrassAbstract:We present three general proposals for a nextgeneration Temporal Data model. Each of these proposals express a synthesis of a variety of contributions from diverse sources within Temporal Databases. We believe that the proposals may aid in bringing consensus to the area of Temporal Data models. The current plethora of diverse and incompatible Temporal Data models has an impeding eect on the design of a consensus Temporal Data model. A single Data model is highly desirable, both to the Temporal Database community and to the Database user community at large. It is our contention that the simultaneous foci on the modeling, presentation, representation, and querying of Temporal Data have been a major cause of the proliferation of models. We advocate instead a separation of concerns. As the next step, we propose a Data model for the single, central task of Temporal Data modeling. In this model, tuples are stamped with biTemporal elements, i.e., sets of pairs of valid and transaction time chronons. This model has no intention of being suitable for the other tasks, where existing models may perhaps be more appropriate. However, this model does capture time-varying Data in a natural way. Finally, we argue that exible support for physical deletion is needed in biTemporal Databases. Physical deletion requires special attention in order not to compromise the correctness of query processing.
Dave Patterson - One of the best experts on this subject based on the ideXlab platform.
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Temporal Data mining for smart homes
Lecture Notes in Computer Science, 2006Co-Authors: Mykola Galushka, Dave Patterson, Niall RooneyAbstract:Temporal Data mining is a relatively new area of research in computer science. It can provide a large variety of different methods and techniques for handling and analyzing Temporal Data generated by smart-home environments. Temporal Data mining in general fits into a two level architecture, where initially a transformation technique reduces Data dimensionality in the first level and indexing techniques provide efficient access to the Data in the second level. This infrastructure of Temporal Data mining provides the basis for high-level Data mining operations such as clustering, classification, rule discovery and prediction. These operations can form the basis for developing different smart-home applications, capable of addressing a number of situations occurring within this environment. This paper outlines the main Temporal Data mining techniques available and provides examples of where they can be applied within a smart home environment.
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Designing Smart Homes - Temporal Data mining for smart homes
Lecture Notes in Computer Science, 2006Co-Authors: Mykola Galushka, Dave Patterson, Niall RooneyAbstract:Temporal Data mining is a relatively new area of research in computer science. It can provide a large variety of different methods and techniques for handling and analyzing Temporal Data generated by smart-home environments. Temporal Data mining in general fits into a two level architecture, where initially a transformation technique reduces Data dimensionality in the first level and indexing techniques provide efficient access to the Data in the second level. This infrastructure of Temporal Data mining provides the basis for high-level Data mining operations such as clustering, classification, rule discovery and prediction. These operations can form the basis for developing different smart-home applications, capable of addressing a number of situations occurring within this environment. This paper outlines the main Temporal Data mining techniques available and provides examples of where they can be applied within a smart home environment.
Christian S. Jensen - One of the best experts on this subject based on the ideXlab platform.
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eBISS - Temporal Data Management – An Overview
Lecture Notes in Business Information Processing, 2018Co-Authors: Michael H. Böhlen, Johann Gamper, Anton Dignös, Christian S. JensenAbstract:Despite the ubiquity of Temporal Data and considerable research on the effective and efficient processing of such Data, Database systems largely remain designed for processing the current state of some modeled reality. More recently, we have seen an increasing interest in the processing of Temporal Data that captures multiple states of reality. The SQL:2011 standard incorporates some Temporal support, and commercial DBMSs have started to offer Temporal functionality in a step-by-step manner, such as the representation of Temporal intervals, Temporal primary and foreign keys, and the support for so-called time-travel queries that enable access to past states.
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Temporal Data management an overview
European Business Intelligence and Big Data Summer School, 2017Co-Authors: Michael H. Böhlen, Johann Gamper, Anton Dignös, Christian S. JensenAbstract:Despite the ubiquity of Temporal Data and considerable research on the effective and efficient processing of such Data, Database systems largely remain designed for processing the current state of some modeled reality. More recently, we have seen an increasing interest in the processing of Temporal Data that captures multiple states of reality. The SQL:2011 standard incorporates some Temporal support, and commercial DBMSs have started to offer Temporal functionality in a step-by-step manner, such as the representation of Temporal intervals, Temporal primary and foreign keys, and the support for so-called time-travel queries that enable access to past states.
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Temporal Data management
IEEE Transactions on Knowledge and Data Engineering, 1999Co-Authors: Christian S. Jensen, Richard T. SnodgrassAbstract:A wide range of Database applications manage time-varying information. Existing Database technology currently provides little support for managing such Data. The research area of Temporal Databases has made important contributions in characterizing the semantics of such information and in providing expressive and efficient means to model, store, and query Temporal Data. This paper introduces the reader to Temporal Data management, surveys state-of-the-art solutions to challenging aspects of Temporal Data management, and points to research directions.
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Three Proposals for a Third-Generation Temporal Data Model
1993Co-Authors: Christian S. Jensen, Richard T. SnodgrassAbstract:We present three general proposals for a nextgeneration Temporal Data model. Each of these proposals express a synthesis of a variety of contributions from diverse sources within Temporal Databases. We believe that the proposals may aid in bringing consensus to the area of Temporal Data models. The current plethora of diverse and incompatible Temporal Data models has an impeding eect on the design of a consensus Temporal Data model. A single Data model is highly desirable, both to the Temporal Database community and to the Database user community at large. It is our contention that the simultaneous foci on the modeling, presentation, representation, and querying of Temporal Data have been a major cause of the proliferation of models. We advocate instead a separation of concerns. As the next step, we propose a Data model for the single, central task of Temporal Data modeling. In this model, tuples are stamped with biTemporal elements, i.e., sets of pairs of valid and transaction time chronons. This model has no intention of being suitable for the other tasks, where existing models may perhaps be more appropriate. However, this model does capture time-varying Data in a natural way. Finally, we argue that exible support for physical deletion is needed in biTemporal Databases. Physical deletion requires special attention in order not to compromise the correctness of query processing.