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Navigating in the Land of Data Analytics

  • Katrina Kronberga
  • Marite Kirikova
  • Daiga Kiopa
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 295)

Abstract

The increasing popularity of data analytics comes together with the many approaches, methods, algorithms, and tools used in different tasks of analytics. When facing a new example of an application of analytics methods with its particular requirements, it would be useful to have an open repository where the experience of the use of different analytical tools is amalgamated. This research in progress paper presents the first results and challenges in the creation of such a repository. The challenges are related to variability in classification, granularity, field of application, purpose of research and other factors. The proposed structure of the repository meets these challenges to some extent and builds the foundation for further development of the repository.

Keywords

Data analytics Information retrieval Big data Data cleansing Social network analysis Business intelligence Data mining Machine learning 

Notes

Acknowledgment

The research was supported by the funding from the research project “Competence Centre of Information and Communication Technologies” of EU Structural funds, contract No. 1.2.1.1/16/A/007 signed between ITCC and CFCA, project No. 1.14. “Development of optimization model for the flow of data processing algorithms to be used for the identification of politically exposed persons”.

References

  1. 1.
    Mariusz, F.: Introduction to Artificial Intelligence, 1st edn. Springer International Publishing, Switzerland (2016)Google Scholar
  2. 2.
    Witten, I.H., Frank, E., Hall, M.A., Pal, C.J.: Data Mining, Fourth Edition: Practical Machine Learning Tools and Techniques (Morgan Kaufmann Series in Data Management Systems), 4th edn. Morgan Kaufmann, Burlington (2016)Google Scholar
  3. 3.
    Wang, Y., Xu, L.: Research on text categorization of KNN based on K-means for class imbalanced problem. In: 2016 Sixth International Conference on Instrumentation & Measurement, Computer, Communication and Control, pp. 357–583 (2016)Google Scholar
  4. 4.
    Jung, Y.G, Kim, K.T., et al.: Enhanced Naive Bayes classifier for real-time sentiment analysis with SparkR. In: 2016 International Conference on Information and Communication Technology Convergence (ICTC), pp. 141–147 (2016)Google Scholar
  5. 5.
    Tsai, C.W., Lai, C.F., Chao, H.-C., Vasilakos, A.: Big data analysis: a survey. J. Big Data 1, 2–21 (2015)Google Scholar
  6. 6.
    Singh, D., Reddy, C.K.: A survey on platforms for big data analytics. J. Big Data 2, 1–8 (2014)CrossRefGoogle Scholar
  7. 7.
    Batrinca, B., Treleaven, P.C.: Social media analytics: a survey of techniques, tools and platforms. AI & Soc. 30, 89–116 (2015)CrossRefGoogle Scholar
  8. 8.
    Grossmann, W., Rinderle-Ma, S.: Fundamentals of Business Intelligence. Springer, Heidelberg (2015). 361 p.CrossRefGoogle Scholar
  9. 9.
    Khotimah, H., Djatna, T., et al.: Tourism recommendation based on vector space model using composite social media extraction. In: Proceedings of the International Conference on Advanced Computer Science and Information Systems (ICACSIS), pp. 303–308 (2014)Google Scholar
  10. 10.
    Cao, L.: Data science and analytics: a new era. Int. J. Data Sci. Anal. 1, 1–2 (2016)CrossRefGoogle Scholar
  11. 11.
    Margaret, R.: Search business analytics, Association rules (in data mining) (2010). (updated February 2011). http://searchbusinessanalytics.techtarget.com/definition/association-rules-in-data-mining. Accessed 15 Dec 2016
  12. 12.
    Kattan, A., Abdullah, R., Geem Z.W.: Feed-forward neural networks. In: Artificial Neural Network Training and Software Implementation Techniques, pp. 3–10. Nova Science Publishers, New York, United States of America (2011)Google Scholar
  13. 13.
    Brando, C., Frontini, F., Ganascia, J.-G.: REDEN: named entity linking in digital literary editions using linked data sets. Complex Syst. Inform. Model. Q. CSIMQ (7), 60–80 (2016). doi: 10.7250/csimq.2016-7.04. ISSN 2255-9922
  14. 14.
    Azam, N., Abulaish, M., et al.: Twitter data mining for event classification and analysis. In: Proceedings of the Second International Conference on Soft Computing and Machine Intelligence, pp. 79–83 (2015)Google Scholar
  15. 15.
    Arif, T., Ali, R., Asger, M.: Author name disambiguation using vector space model and hybrid similarity measures. In: Seventh International Conference on Contemporary Computing (IC3), pp. 135–140 (2014)Google Scholar
  16. 16.
    Gunasinghe, U.L.D.N., De Silva, W.A.M., et al.: Sentence similarity measuring by vector space model. In: Proceedings of the International Conference on Advances in ICT for Emerging Regions (ICTer), pp. 185–189 (2014)Google Scholar
  17. 17.
    Liu, D., Liu, Z., et al.: A dependency grammar and WordNet based sentence similarity measure. J. Comput. Inf. Syst. 8(3), 1027–1035 (2012)Google Scholar
  18. 18.
    Jahan, J.A., Ragel, R.: Plagiarism detection on electronic text based assignments using vector space model. In: Proceedings of the 7th International Conference on Information and Automations for Sustainability, pp. 1–5 (2014)Google Scholar
  19. 19.
    Kastrati, Z., Imran, A.S., Yayilgan, S.Y.: An improved concept vector space model for ontology based classification. In: Proceedings of the 11th International Conference on Signal-Image Technology & Internet-based Systems, pp. 240–245 (2015)Google Scholar
  20. 20.
    Premalatha, R., Srinivasan, S.: Text processing in information retrieval system using vector space model. In: Proceedings of the International Conference on Information Communication and Embedded Systems ICICES2014, pp. 1–6 (2014)Google Scholar
  21. 21.
    Henderson-Sellers, B., Ralyté, J.: Situational method engineering: state-of-the-art review. J. Univ. Comput. Sci. 16(3), 424–478 (2010)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Katrina Kronberga
    • 1
  • Marite Kirikova
    • 1
  • Daiga Kiopa
    • 2
  1. 1.Department of Artificial Intelligence and Systems EngineeringRiga Technical UniversityRigaLatvia
  2. 2.Lursoft ITRigaLatvia

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