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A Semantic-Based Analytics Architecture and Its Application to Commodity Pricing

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Enterprise Applications, Markets and Services in the Finance Industry (FinanceCom 2016)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 276))

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Abstract

Over the past decade, several sophisticated analytic techniques such as machine learning, neural networks, and predictive modelling have evolved to enable scientists to derive insights from data. Data Science is characterised by a cycle of model selection, customization and testing, as scientists often do not know the exact goal or expected results beforehand. Existing research efforts which explore maximising automation, reproducibility and interoperability are quite mature and fail to address a third criterion, usability. The main contribution of this paper is to explore the development of more complex semantic data models linked with existing ontologies (e.g. FIBO) that enable the standardisation of data formats as well as meaning and interpretation of data in automated data analysis. A model-driven architecture with the reference model that capture statistical learning requirement is proposed together with a prototype based around a case study in commodity pricing.

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Notes

  1. 1.

    Function is multiple linear regression, which is a widely used form in statistical learning.

    .

References

  1. Info Package for UNSW Data Science Hackathon. http://www.cse.unsw.edu.au/~fethir/HackathonInfo/HackathonStudentPack_v7.pdf. Accessed on 10 Sep 2016

  2. OASIS SOA Reference Model Technical Committee. https://www.oasis-open.org/committees/tc_home.php?wg_abbrev=soa-rm/. Accessed on 10 Sep 2016

  3. Financial Industry Business Ontology Foundations, The Enterprise Data Management Council. http://www.edmcouncil.org/edmcouncil. Accessed on 10 Sep 2016

  4. Financial Industry Business Ontology (FIBO), Object Management Group. http://www.omg.org/spec/EDMC-FIBO/. Accessed on 10 Sep 2016

  5. Merriam Webster, Measure (Definition). http://www.merriam-webster.com/dictionary/measure. Accessed on 10 Sep 2016

  6. Roach, T.M.: CAPSICUM – A Semantic Framework for Strategically Aligned Business Architecture. Ph.D Thesis, UNSW, Sydney, Australia (2011)

    Google Scholar 

  7. Behnaz, A., Rabhi, F., Peat, M.: A software architecture for enabling time series analysis on real-time event data. In: Proceedings of International Work-Conference on Time Series, June 2016

    Google Scholar 

  8. Vapnik, V.: The Nature of Statistical Learning Theory, 2nd edn. Springer, New York (1999)

    MATH  Google Scholar 

  9. Rabhi, F.A., Yao, L., Guabtni, A.: ADAGE: a framework for supporting user-driven ad-hoc data analysis processes. Computing 94(6), 489–519 (2012). doi:10.1007/s00607-012-0193-0

    Article  Google Scholar 

  10. Yao, L., Rabhi, F.A.: Building architectures for data-intensive science using the adage framework. Concurrency Comput. Pract. Exp. 27(5), 1188–1206 (2015)

    Article  Google Scholar 

  11. Chen, J., Choudhary, A., Feldman, S., Hendrickson, B., Johnson, C., Mount, R., Sarkar, V., White, V., Williams, D.: Synergistic challenges in data-intensive science and exascale computing. DOE ASCAC Data Subcommittee Report, Department of Energy Office of Science (2013)

    Google Scholar 

  12. Yao, L., Rabhi, F., Peat, M.: Supporting data-intensive analysis processes: a review of enabling technologies and trends. In: Ramanathan, R., Raja, K. (eds.) Handbook of Research on Architectural Trends in Service-Driven Computing, vol. 2, pp. 481–508. IGI Global, Hershey (2014). doi:10.4018/978-1-4666-6178-3

  13. Bernstien, P.A., Wecker, D., Krishnamurthy, A., Manocha, D., Gardner, J., Kolker, N., Reschke, C., Stombaugh, J., Vagata, P., Stewart, E.: Technology and data-intensive science in the beginning of the 21st century. Omics: J. Integr. Biol. 15, 203–207 (2011)

    Article  Google Scholar 

  14. Yao, L.: ADAGE A Framework For Supporting User-Driven Ad Hoc Data Analysis Processes. Doctor of Philosophy, University of New South Wales (2013)

    Google Scholar 

  15. OASIS, OASIS Web Services Business Process Execution Language (WSBPEL) TC | OASIS. https://www.oasis-open.org/committees/wsbpel/. Accessed 9 Sep 2016

  16. TAVERNA 2009, Taverna - open source and domain independent Workflow Management System (2009). http://www.taverna.org.uk/. Accessed 9 Sep 2016

  17. Tao, J., Zhao, Y.: Scientific workflow management and the Kepler system. Concurrency Comput. Pract. Exp. 18, 1039–1065 (2006)

    Article  Google Scholar 

  18. Deelman, E., Moody, J., Kim, J., Ratnakar, V., Gil, Y., Gonzalez-Calero, P.A., Groth, P.: Wings: intelligent workflow-based design of computational experiments. IEEE Intell. Syst. 26(1), 62–72 (2011)

    Article  Google Scholar 

  19. Gnumeric.org., Gnumeric (2016). http://www.gnumeric.org/. Accessed 17 Sep 2016

  20. Apps.google.com. Google Sheets – Spreadsheets & Data Analysis for Business (2016). https://apps.google.com/intx/en_au/products/sheets/. Accessed 17 Sep 2016

  21. Evans, E.: Domain-Driven Design: Tackling Complexity in the Heart of Software. Addison-Wesley Professional, Reading (2004)

    Google Scholar 

  22. Völter, M., Stahl, T., Bettin, J., Haase, A., Helsen, S.: Model-Driven Software Development: Technology, Engineering, Management. John Wiley & Sons, Hoboken (2013)

    Google Scholar 

  23. W3.org. OWL Web Ontology Language Guide. (2016). https://www.w3.org/TR/owl-guide/. Accessed 17 Sep 2016

  24. W3.org. Financial Industry Business Ontology Community Group (2016). https://www.w3.org/community/fibo/. Accessed 17 Sep 2016

  25. James, G., Witten, D., Hastie, T., Tibshirani, R.: An Introduction to Statistical Learning with Applications in R. Springer, New York (2013)

    Book  MATH  Google Scholar 

  26. Milosevic, Z., Chen, W., Berry, A., Rabhi, F.A.: An open architecture for event-based analytics. Accepted in Int. J. Data Sci. Anal. (2016)

    Google Scholar 

  27. Natarajan, A.: Aventis, An architecture for event data analysis. Doctor of Philosophy, University of New South Wales (2016)

    Google Scholar 

  28. Behnaz, A., Rabhi, F., Peat, M.: A Software Architecture for Enabling Statistical Learning on Big Data. Springer Series on Statistics (2016)

    Google Scholar 

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Acknowledgements

We are grateful to ANZ Bank Agribusiness unit, especially Richard Schroder and Felipe Flores, Thomson Reuters and IBM for sponsoring the Hackathon which provided the data for the case study of this paper. We are also grateful to Terry Roach and Max Gillmore for helping on different aspects of this work.

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Authors and Affiliations

  1. School of Computer Science and Engineering, University of New South Wales, Sydney, New South Wales, 2052, Australia

    Ali Behnaz, Aarthi Natarajan & Fethi A. Rabhi

  2. The University of Sydney Business School, Sydney, New South Wales, 2006, Australia

    Maurice Peat

Authors
  1. Ali Behnaz
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  2. Aarthi Natarajan
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  3. Fethi A. Rabhi
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  4. Maurice Peat
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Corresponding author

Correspondence to Ali Behnaz .

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Editors and Affiliations

  1. University of Freiburg, Freiburg, Germany

    Stefan Feuerriegel

  2. University of Freiburg, Freiburg, Germany

    Dirk Neumann

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Behnaz, A., Natarajan, A., Rabhi, F.A., Peat, M. (2017). A Semantic-Based Analytics Architecture and Its Application to Commodity Pricing. In: Feuerriegel, S., Neumann, D. (eds) Enterprise Applications, Markets and Services in the Finance Industry. FinanceCom 2016. Lecture Notes in Business Information Processing, vol 276. Springer, Cham. https://doi.org/10.1007/978-3-319-52764-2_2

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