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Quality Assessment of Data Using Statistical and Machine Learning Methods

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Computational Intelligence in Data Mining - Volume 2

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 32))

Abstract

Data warehouses are used in organization for efficiently managing the information. The data from various heterogeneous data sources are integrated in data warehouse in order to do analysis and make decision. Data warehouse quality is very important as it is the main tool for strategic decision. Data warehouse quality is influenced by Data model quality which is further influenced by conceptual data model. In this paper, we first summarize the set of metrics for measuring the understand ability of conceptual data model for data warehouses. The statistical and machine learning methods are used to predict effect of structural metrics, on understand ability, efficiency and effectiveness of Data warehouse Multidimensional (MD) conceptual model.

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Author information

Authors and Affiliations

  1. Jagan Institute of Management Studies, New Delhi, India

    Prerna Singh

  2. USICT, Dwarka, New Delhi, India

    Bharti Suri

Authors
  1. Prerna Singh
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  2. Bharti Suri
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Corresponding author

Correspondence to Prerna Singh .

Editor information

Editors and Affiliations

  1. University of Canberra, Canberra, Australia and University of South Australia, Adelaide, South Australia, Australia

    Lakhmi C. Jain

  2. Department of Computer Science and Engineering, Veer Surendra Sai University of Technology, Sambalpur, Odisha, India

    Himansu Sekhar Behera

  3. Computer Science & Engineering, Kalyani University, Nadia, West Bengal, India

    Jyotsna Kumar Mandal

  4. Dept. of Computer Science and Engineering, National Institute of Technology Rourkela, Rourkela, India

    Durga Prasad Mohapatra

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Singh, P., Suri, B. (2015). Quality Assessment of Data Using Statistical and Machine Learning Methods. In: Jain, L., Behera, H., Mandal, J., Mohapatra, D. (eds) Computational Intelligence in Data Mining - Volume 2. Smart Innovation, Systems and Technologies, vol 32. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2208-8_10

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  • DOI: https://doi.org/10.1007/978-81-322-2208-8_10

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  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-2207-1

  • Online ISBN: 978-81-322-2208-8

  • eBook Packages: EngineeringEngineering (R0)

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