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International Conference on Data Mining and Big Data

DMBD 2016: Data Mining and Big Data pp 15–23Cite as

Prospects and Challenges in Online Data Mining

Experiences of Three-Year Labour Market Monitoring Project

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

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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References

  1. Chen, M., Mao, S., Liu, Y.: Big data: a survey. Mob. Netw. Appl. 19(2), 171–209 (2014)

    Article  MathSciNet  Google Scholar 

  2. Liao, S.H., Chu, P.H., Hsiao, P.Y.: Data mining techniques and applications–a decade review from 2000 to 2011. Exp. Syst. Appl. 39(12), 11303–11311 (2012)

    Article  Google Scholar 

  3. Wu, X., et al.: Top 10 algorithms in data mining. Knowl. Inf. Syst. 14(1), 1–37 (2008)

    Article  Google Scholar 

  4. Wu, X., Zhu, X., Wu, G.Q., Ding, W.: Data mining with big data. IEEE Trans. Knowl. Data Eng. 26(1), 97–107 (2014)

    Article  Google Scholar 

  5. Beręsewicz, M.: Estimating the size of the secondary real estate market based on internet data sources. Folia Oeconomica Stetinensia 14(2), 259–269 (2014)

    Google Scholar 

  6. Seera, M., Lim, C.P.: A hybrid intelligent system for medical data classification. Exp. Syst. Appl. 41(5), 2239–2249 (2014)

    Article  Google Scholar 

  7. Esfandiari, N., Babavalian, M.R., Moghadam, A.M.E., Tabar, V.K.: Knowledge discovery in medicine: current issue and future trend. Exp. Syst. Appl. 41(9), 4434–4463 (2014)

    Article  Google Scholar 

  8. Chen, H., Chiang, R.H., Storey, V.C.: Business intelligence and analytics: from big data to big impact. MIS Q. 36(4), 1165–1188 (2012)

    Google Scholar 

  9. Peña-Ayala, A.: Educational data mining: a survey and a data mining-based analysis of recent works. Exp. Syst. Appl. 41(4), 1432–1462 (2014)

    Article  Google Scholar 

  10. Tsai, C.W., Lai, C.F., Chiang, M.C., Yang, L.T.: Data mining for internet of things: a survey. IEEE Commun. Surv. Tutor. 16(1), 77–97 (2014)

    Article  Google Scholar 

  11. Polanco, W.: Web Mining technologies for the e-Commerce solutions in the social networks systems. A Thesis Master of Science - Information Systems, pp. 1–60. SIT, NJ (2013)

    Google Scholar 

  12. Milne, D., Witten, I.H.: An open-source toolkit for mining Wikipedia. Artif. Intell. 194, 222–239 (2013)

    Article  MathSciNet  Google Scholar 

  13. Beresewicz, M.E.: On representativeness of Internet data sources for real estate market in Poland. Austrian J. Stat. 44(2), 45–57 (2015)

    Article  Google Scholar 

  14. Gök, A., Waterworth, A., Shapira, P.: Use of web mining in studying innovation. Scientometrics 102(1), 653–671 (2015)

    Article  Google Scholar 

  15. Barcaroli, G.: Internet as data source in the Istat survey on ICT in enterprises. Austrian J. Stat. 44(2), 31–43 (2015)

    Article  Google Scholar 

  16. Ferrara, E., De Meo, P., Fiumara, G., Baumgartner, R.: Web data extraction, applications and techniques: a survey. Knowl.-Based Syst. 70, 301–323 (2014)

    Article  Google Scholar 

  17. Kraychev, B., Koychev, I.: Computationally effective algorithm for information extraction and online review mining. In: Proceedings of the 2nd International Conference on Web Intelligence, Mining and Semantics, vol. 64 (2012)

    Google Scholar 

  18. Choudhary, S. et al.: Crawling rich internet applications: the state of the art. In: Proceedings of the 2012 Confrence of the Center for Advanced Studies on Collaborative Research, pp. 146–160 (2012)

    Google Scholar 

  19. Liu, W., Meng, X., Meng, W.: Vide: a vision-based approach for deep web data extraction. IEEE Trans. Know. Data Eng. 22(3), 447–460 (2010)

    Article  Google Scholar 

  20. Bakaev, M., Avdeenko, T.: Data extraction for decision-support systems application in labour market monitoring and analysis. Int. J. e-Educ. e-Bus. e-Manage. e-Lear. (IJEEEE) 4(1), 23–27 (2014)

    Google Scholar 

  21. Bakaev, M., Avdeenko, T.: Intelligent information system to support decision-making based on unstructured web data. ICIC Expr. Lett. 9(4), 1017–1023 (2015)

    Google Scholar 

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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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Corresponding author

Correspondence to Maxim Bakaev .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

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

    Yuhui Shi

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© 2016 Springer International Publishing Switzerland

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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

  • eBook Packages: Computer ScienceComputer Science (R0)

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