Rule-Based Impact Analysis for Enterprise Business Intelligence

Abstract : We address several common problems in the field of Business Intelligence, Data Warehousing and Decision Support Systems: the complexity to manage, track and understand data lineage and system component dependencies in long series of data transformation chains. The paper presents practical methods to calculate meaningful data transformation and component dependency paths, based on program parsing, heuristic impact analysis, probabilistic rules and semantic technologies. Case studies are employed to explain further data aggregation and visualization of the results to address different planning and decision support problems for various user profiles like business users, managers, data stewards, system analysts, designers and developers.
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Lazaros Iliadis; Ilias Maglogiannis; Harris Papadopoulos; Spyros Sioutas; Christos Makris. 10th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2014, Rhodes, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-437, pp.301-309, 2014, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-662-44722-2_32〉
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Kalle Tomingas, Tanel Tammet, Margus Kliimask. Rule-Based Impact Analysis for Enterprise Business Intelligence. Lazaros Iliadis; Ilias Maglogiannis; Harris Papadopoulos; Spyros Sioutas; Christos Makris. 10th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Sep 2014, Rhodes, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-437, pp.301-309, 2014, Artificial Intelligence Applications and Innovations. 〈10.1007/978-3-662-44722-2_32〉. 〈hal-01391057〉

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