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High Quality Dataset for Machine Learning in the Business Intelligence Domain

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Intelligent Systems and Applications (IntelliSys 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1037))

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Abstract

This paper is aimed at showing the relevance and importance of high quality dataset in machine learning within the field of economic intelligence. As open source dataset flourish and algorithm are trained with different and often very narrow data of various kind, in the field of economic intelligence it is important to train machines with proper and high value data to avoid or reduce at maximum false positives, errors and biases of various kind. We propone the case study and the solution offered by Bureau Van Dijk, where economic data are carefully evaluated, organized and its API and REST services could matter in the very near future in the field of data mining and machine learning for economic intelligence purposes.

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References

  1. Cagala, T.: Improving Data Quality and Closing Data Gaps with Machine Learning. Deutsche Bundesbank, German Securities Holding Statistics (2017)

    Google Scholar 

  2. Gudivada, V.N., Apon, A., Dingh, J.: Data quality considerations for big data and machine learning: going beyond data cleaning and transformation. Int. J. Adv. Softw. 10(1–2), 1–20 (2017)

    Google Scholar 

  3. Borovicka, T., Jirina Jr., M., Kordik, P., Jirina, M.: Selecting Representative Data Sets. Advances in Data Mining Knowledge Discovery and Application. Intech (2012)

    Google Scholar 

  4. Schelter, S., Lange, D., Schmidt, P., Celikel, M., Biessmann, F., Grafberger, A.: Automating large-scale data quality verification. In: VLDB Endowment Proceedings of the VLDB Endowment, vol. 11, no. 12, pp. 1781–1794. VLDB Endowment (2016)

    Article  Google Scholar 

  5. Redman, T.C.: Bad Data Costs the U.S. 3 Trillion Per Year. Harvard Business Review (2016)

    Google Scholar 

  6. Echerson, W.W.: Data Quality and the Bottom Line, 1st edn. The Data Warehousing Institute (2002)

    Google Scholar 

  7. Marcheggiani, D., Sebastiani, F.: On the effects of low-quality training data on information extraction from clinical reports. Data Inf. Qual. 9(1), 1–25 (2017)

    Article  Google Scholar 

  8. Zellal, N., Zaouia, A.: An examination of factors influencing the quality of data in a data warehouse. IJCSNS Int. J. Comput. Sci. Netw. Secur. 17(1), 161–169 (2017)

    Google Scholar 

  9. Teti, A.: Open Source Intelligence and Cyberspace, 1st edn. Rubbettino, Roma (2015)

    Google Scholar 

  10. Collingwood, L., Wilkerson, J.: Tradeoffs in accuracy and efficiency in supervised learning methods. J. Inf. Technol. Polit. (JITP) 4, 298–318 (2011)

    Google Scholar 

  11. Sessions, V., Valtorta, M.: The Effects of Data Quality on Machine Learning Algorithms. Research in Progress, University of South Carolina (2017)

    Google Scholar 

  12. Singh, H., Singh, P.B.: Business intelligence: effective machine learning for business administration. Int. J. IT Eng. Appl. Sci. Res. (IJIEASR) 2(1), 13–19 (2013)

    Google Scholar 

  13. Duy Vo, Q., Jaya, T., Cho, S., De, P., Choi, B.J., Sael, L.: Next generation business intelligence and analytics: a survey. IEEE Commun. Surv. Tutor. 18(1), 491–506 (2017)

    Google Scholar 

  14. Financial Stability Board: Artificial Intelligence and Machine Learning in Financial Services. Financial Stability Board (2017)

    Google Scholar 

  15. Sapp, C.E.: Preparing and Architecting for Machine Learning. Gartner Technical Professional Advice (2017)

    Google Scholar 

  16. Pirani, Z., Marewar, A., Bhavnagarwala, Z., Kamble, M.: Application of business intelligence using machine learning approach. Int. J. Comput. Appl. 165(13), 28–31 (2017)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Hermes Bay, Rome, Italy

    Luisa Franchina

  2. Applied Social Sciences, Sapienza University of Rome, Rome, Italy

    Federico Sergiani

Authors
  1. Luisa Franchina
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  2. Federico Sergiani
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Corresponding authors

Correspondence to Luisa Franchina or Federico Sergiani .

Editor information

Editors and Affiliations

  1. School of Computing, Computer Science Research Institute, Ulster University, Newtownabbey, UK

    Yaxin Bi

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Rahul Bhatia

  3. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Supriya Kapoor

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Franchina, L., Sergiani, F. (2020). High Quality Dataset for Machine Learning in the Business Intelligence Domain. In: Bi, Y., Bhatia, R., Kapoor, S. (eds) Intelligent Systems and Applications. IntelliSys 2019. Advances in Intelligent Systems and Computing, vol 1037. Springer, Cham. https://doi.org/10.1007/978-3-030-29516-5_31

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