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Knowledge Management for Model Driven Data Cleaning of Very Large Database

  • Yucong Duan
  • Roger Lee
Part of the Studies in Computational Intelligence book series (SCI, volume 443)

Abstract

From a knowledge management perspective, we explore data cleaning of very large databases with focus on semantic rich data and linked data. We identify four aspects of complexity which, if they were not explicitly addressed and fully managed will hinder both the recognizing and attaining of the best result: (a) the inconsistency of solution knowledge due to their partial applicability among multiple concerns; (b) the side effect which is introduced during the introduction of solution knowledge for pursuing a precision relating to the existence of multiple semantics; (c) unconscious ignorance of implicit weights of some parameters for value computation; (d) a holism based reasoning which is irreplaceable by simplification for some situations. After analyzing the state of the art, we propose an ongoing Model Driven Engineering (MDE) based knowledge management platform for identifying, refining, organizing and evaluating related variants and solutions with mitigated complexity.

Keywords

False Negative Knowledge Management Data Cleaning Data Clean Semantic State 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Dipartimento Informatica Sistemistica e ComunicazioneUniversity of Milano-BicoccaMilanoItaly
  2. 2.Software Engineering and Information Technology InstituteCentral Michigan UniversityMount PleasantU.S.A.

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