The Experts below are selected from a list of 2311137 Experts worldwide ranked by ideXlab platform
Katharina Pietzka - One of the best experts on this subject based on the ideXlab platform.
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MD3M: The master Data Management maturity model
Computers in Human Behavior, 2014Co-Authors: Marco Spruit, Katharina PietzkaAbstract:This research aims to assess the master Data maturity of an organization. It is based on thorough literature study to derive the main concepts and best practices in master Data maturity assessment. A maturity matrix relating 13 focus areas and 65 capabilities was designed and validated. Furthermore, an assessment questionnaire was developed which can be used to assess the master Data Management maturity. Emphasis is laid on the academic validity of the model development process. Our extensive case study provides an example of iterative human learning, behavior and collaboration resulting from technological needs in a large-scale infrastructural network. Concludingly, this research uncovers reasons and incentives for prudent master Data Management and provides a benchmarking tool with which different organizations can compare their levels.
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.
Marco Spruit - One of the best experts on this subject based on the ideXlab platform.
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MD3M: The master Data Management maturity model
Computers in Human Behavior, 2014Co-Authors: Marco Spruit, Katharina PietzkaAbstract:This research aims to assess the master Data maturity of an organization. It is based on thorough literature study to derive the main concepts and best practices in master Data maturity assessment. A maturity matrix relating 13 focus areas and 65 capabilities was designed and validated. Furthermore, an assessment questionnaire was developed which can be used to assess the master Data Management maturity. Emphasis is laid on the academic validity of the model development process. Our extensive case study provides an example of iterative human learning, behavior and collaboration resulting from technological needs in a large-scale infrastructural network. Concludingly, this research uncovers reasons and incentives for prudent master Data Management and provides a benchmarking tool with which different organizations can compare their levels.
Yue Pan - One of the best experts on this subject based on the ideXlab platform.
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semantic enhancement for enterprise Data Management
International Semantic Web Conference, 2009Co-Authors: Xingzhi Sun, Feng Cao, Chen Wang, Xiaoyuan Wang, Nick Kanellos, Dan Wolfson, Yue PanAbstract:Taking customer Data as an example, the paper presents an approach to enhance the Management of enterprise Data by using Semantic Web technologies. Customer Data is the most important kind of core business entity a company uses repeatedly across many business processes and systems, and customer Data Management (CDM) is becoming critical for enterprises because it keeps a single, complete and accurate record of customers across the enterprise. Existing CDM systems focus on integrating customer Data from all customer-facing channels and front and back office systems through multiple interfaces, as well as publishing customer Data to different applications. To make the effective use of the CDM system, this paper investigates semantic query and analysis over the integrated and centralized customer Data, enabling automatic classification and relationship discovery. We have implemented these features over IBM Websphere Customer Center, and shown the prototype to our clients. We believe that our study and experiences are valuable for both Semantic Web community and Data Management community.
Christian S. Jensen - 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.
