The Experts below are selected from a list of 357135 Experts worldwide ranked by ideXlab platform
Tova Milo - One of the best experts on this subject based on the ideXlab platform.
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using schema matching to simplify Heterogeneous Data translation
Very Large Data Bases, 1998Co-Authors: Tova Milo, Sagit ZoharAbstract:A broad spectrum of Data is available on the Web in distinct Heterogeneous sources, and stored under different formats. As the number of systems that utilize this Heterogeneous Data grows, the importance of Data translation and conversion mechanisms increases greatly. In this paper we present a new translation system, based on schema-matching, aimed at simplifying the intricate task of Data conversion. We observe that in many cases the schema of the Data in the source system is very similar to that of the target system. In such cases, much of the translation work can be done automatically, based on the schemas similarity. This saves a lot of effort for the user, limiting the amount of programming needed. We define common schema and Data models, in which schemas and Data (resp.) from many common models can be represented. Using a rule-based method, the source schema is compared with the target one, and each component in the source schema is matched with a corresponding component in the target schema. Then, based on the matching achieved, Data instances of the source schema can be translated to instances of the target schema. We show that our schema-based translation system allows a convenient specification and customization of Data conversions, and can be easily combined with the traditional Data-based translation languages.
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correspondence and translation for Heterogeneous Data
International Conference on Database Theory, 1997Co-Authors: Serge Abiteboul, Sophie Cluet, Tova MiloAbstract:We presented a specification of the integration of Heterogeneous Data based on correspondence rules. We showed how a unique specification can served many purposes (including two-way translation) assuming some reasonable restrictions. We claim that the framework and restrictions are acceptable in practice, and in particular one can show that all the document-OODB correspondences/translations of [2, 3] are covered. We are currently working on further substantiating this by more experimentation.
Eddie Soulier - One of the best experts on this subject based on the ideXlab platform.
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“Sustainable assemblage for energy (SAE)” inside intelligent urban areas: How massive Heterogeneous Data could help to reduce energy footprints and promote sustainable practices and an ecological transition
2014Co-Authors: Philippe Calvez, Eddie SoulierAbstract:Worldwide, human activities have a major impact on energy production and consumption. Urban areas, where the majority of the world population lives, are confronted with many environmental problems especially in emerging countries where a potential ecological transition is shadowed by frenetic economic development. At the same time, the deployment of intelligent infrastructures (Smart Grids), new technologies or paradigms (ubiquitous computing, Big Data) impact behaviors and practices of inhabitants of these areas. The ability to aggregate and model these digital traces in multiple dimensions could allow people to better understand their daily activities and promote more sustainable behaviors and practices by reducing the footprints related to every human collective activity. This paper aims to explore how to facilitate the decision-making process for inhabitants of these intelligent urban areas about their sustainable practices and lifestyles based on massive Heterogeneous Data in order to optimize the daily production and consumption of energy and meet the challenges of energy access and ecological transition.
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BigData Conference - “Sustainable assemblage for energy (SAE)” inside intelligent urban areas: How massive Heterogeneous Data could help to reduce energy footprints and promote sustainable practices and an ecological transition
2014 IEEE International Conference on Big Data (Big Data), 2014Co-Authors: Philippe Calvez, Eddie SoulierAbstract:Worldwide, human activities have a major impact on energy production and consumption. Urban areas, where the majority of the world population lives, are confronted with many environmental problems especially in emerging countries where a potential ecological transition is shadowed by frenetic economic development. At the same time, the deployment of intelligent infrastructures (Smart Grids), new technologies or paradigms (ubiquitous computing, Big Data) impact behaviors and practices of inhabitants of these areas. The ability to aggregate and model these digital traces in multiple dimensions could allow people to better understand their daily activities and promote more sustainable behaviors and practices by reducing the footprints related to every human collective activity. This paper aims to explore how to facilitate the decision-making process for inhabitants of these intelligent urban areas about their sustainable practices and lifestyles based on massive Heterogeneous Data in order to optimize the daily production and consumption of energy and meet the challenges of energy access and ecological transition.
Larry Kerschberg - One of the best experts on this subject based on the ideXlab platform.
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DaWaK - Knowledge Management in Heterogeneous Data Warehouse Environments
Data Warehousing and Knowledge Discovery, 2001Co-Authors: Larry KerschbergAbstract:This paper addresses issues related to Knowledge Management in the context of Heterogeneous Data warehouse environments. The traditional notion of Data warehouse is evolving into a federated warehouse augmented by a knowledge repository, together with a set of processes and services to support enterprise knowledge creation, refinement, indexing, dissemination and evolution.
Philippe Calvez - One of the best experts on this subject based on the ideXlab platform.
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“Sustainable assemblage for energy (SAE)” inside intelligent urban areas: How massive Heterogeneous Data could help to reduce energy footprints and promote sustainable practices and an ecological transition
2014Co-Authors: Philippe Calvez, Eddie SoulierAbstract:Worldwide, human activities have a major impact on energy production and consumption. Urban areas, where the majority of the world population lives, are confronted with many environmental problems especially in emerging countries where a potential ecological transition is shadowed by frenetic economic development. At the same time, the deployment of intelligent infrastructures (Smart Grids), new technologies or paradigms (ubiquitous computing, Big Data) impact behaviors and practices of inhabitants of these areas. The ability to aggregate and model these digital traces in multiple dimensions could allow people to better understand their daily activities and promote more sustainable behaviors and practices by reducing the footprints related to every human collective activity. This paper aims to explore how to facilitate the decision-making process for inhabitants of these intelligent urban areas about their sustainable practices and lifestyles based on massive Heterogeneous Data in order to optimize the daily production and consumption of energy and meet the challenges of energy access and ecological transition.
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BigData Conference - “Sustainable assemblage for energy (SAE)” inside intelligent urban areas: How massive Heterogeneous Data could help to reduce energy footprints and promote sustainable practices and an ecological transition
2014 IEEE International Conference on Big Data (Big Data), 2014Co-Authors: Philippe Calvez, Eddie SoulierAbstract:Worldwide, human activities have a major impact on energy production and consumption. Urban areas, where the majority of the world population lives, are confronted with many environmental problems especially in emerging countries where a potential ecological transition is shadowed by frenetic economic development. At the same time, the deployment of intelligent infrastructures (Smart Grids), new technologies or paradigms (ubiquitous computing, Big Data) impact behaviors and practices of inhabitants of these areas. The ability to aggregate and model these digital traces in multiple dimensions could allow people to better understand their daily activities and promote more sustainable behaviors and practices by reducing the footprints related to every human collective activity. This paper aims to explore how to facilitate the decision-making process for inhabitants of these intelligent urban areas about their sustainable practices and lifestyles based on massive Heterogeneous Data in order to optimize the daily production and consumption of energy and meet the challenges of energy access and ecological transition.
Ruggero G. Pensa - One of the best experts on this subject based on the ideXlab platform.
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Parameter-less co-clustering for star-structured Heterogeneous Data
Data Mining and Knowledge Discovery, 2013Co-Authors: Dino Ienco, Céline Robardet, Ruggero G. PensaAbstract:The availability of Data represented with multiple features coming from Heterogeneous domains is getting more and more common in real world applications. Such Data represent objects of a certain type, connected to other types of Data, the features, so that the overall Data schema forms a star structure of inter-relationships. Co-clustering these Data involves the specification of many parameters, such as the number of clusters for the object dimension and for all the features domains. In this paper we present a novel co-clustering algorithm for Heterogeneous star-structured Data that is parameter-less. This means that it does not require either the number of row clusters or the number of column clusters for the given feature spaces. Our approach optimizes the Goodman–Kruskal’s τ , a measure for cross-association in contingency tables that evaluates the strength of the relationship between two categorical variables. We extend τ to evaluate co-clustering solutions and in particular we apply it in a higher dimensional setting. We propose the algorithm CoStar which optimizes τ by a local search approach. We assess the performance of CoStar on publicly available Datasets from the textual and image domains using objective external criteria. The results show that our approach outperforms state-of-the-art methods for the co-clustering of Heterogeneous Data, while it remains computationally efficient.
