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Prospects and Challenges in Online Data Mining

Experiences of Three-Year Labour Market Monitoring Project

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Data Mining and Big Data (DMBD 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9714))

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Abstract

The paper provides reflections on feasibility of online data mining (ODM) and its employment in decision-making and control. Besides reviewing existing works in different domains of Data Mining, we also report experiences from ongoing project dedicated to monitoring labour market with the aid of dedicated intelligent information system. Benefits of ODM include high efficiency, availability of data sources, potential extensiveness of datasets, timeliness and frequency of collection, good validity. Among special considerations we highlight a need for sophisticated tools, programming and maintenance efforts, hardware and network resources, multitude and diversity of data sources, disparity between real world and Internet. Finally, we describe some examples of the intelligent system application, in particular analyzing labour market data for several regions.

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Acknowledgement

This work was supported by RFBR according to the research project No. 16-37-60060 mol_a_dk.

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Authors and Affiliations

  1. Economic Informatics Department, Novosibirsk State Technical University, Novosibirsk, Russia

    Maxim Bakaev & Tatiana Avdeenko

Authors
  1. Maxim Bakaev
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  2. Tatiana Avdeenko
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Correspondence to Maxim Bakaev .

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Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Xi'an Jiaotong-Liverpool University, Suzhou, China

    Yuhui Shi

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Bakaev, M., Avdeenko, T. (2016). Prospects and Challenges in Online Data Mining. In: Tan, Y., Shi, Y. (eds) Data Mining and Big Data. DMBD 2016. Lecture Notes in Computer Science(), vol 9714. Springer, Cham. https://doi.org/10.1007/978-3-319-40973-3_2

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  • DOI: https://doi.org/10.1007/978-3-319-40973-3_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-40972-6

  • Online ISBN: 978-3-319-40973-3

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