Skip to main content
SpringerLink
Log in
Book cover

International Symposium on Business Modeling and Software Design

BMSD 2018: Business Modeling and Software Design pp 280–288Cite as

General Architectural Framework for Business Visual Analytics

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 319))

Abstract

An incredible amount of data has constantly been generated. There is a gap between the abundance of data and the needs of users. At the same time a particular user needs not only a relevant part of the available data but (s)he needs higher-level (inferred) data in order to use this data in support of decision-making. Analytics comes to bridge that gap, by means of tools. Those tools link what the user needs to what is available as data; generate higher-level (inferred) data based on the raw data; provide visualization that is useful especially to users that have limited knowledge in mathematics and data science. The abundance of definitions that concern analytics as well as the numerous terms and concepts confuse users. Often different concepts are inconsistent with regard to each other. Inspired by this problem, the current paper: provides a systematic analysis featuring analytics in general and business analytics, in particular, developing this also from the perspective of visualization-related needs. We propose a layered business analytics architecture named General Architectural Framework for Business Visual Analytics. The framework presents the main interrelationships between the elements for designing and modeling Business Visual Analytics. We propose a conceptual definition that combines the semantics of the multiple recurring and overlapping definitions of analytics, named Business Visual Analytics (BVA). This is a new conceptual viewpoint mainly focused on the innovative data visualization techniques, business analytics capabilities and achieving business goals and performance. The paper is reporting research in progress and for this reason, further architectural developments and related validations are planned for further research.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Davenport, T.H., Harris, J.G.: Competing on Analytics: the New Science of Winning. Harvard Business School Press Boston, Boston (2007)

    Google Scholar 

  2. Cooper, A.: What is analytics? definition and essential characteristics. CETIS Anal. Ser. 1(5), 1–10 (2012). ISSN 2059214

    Google Scholar 

  3. Laursen, G., Thorlund, J.: Business Analytics for Managers: Taking Business Intelligence Beyond Reporting, 2nd edn. Wiley, Hoboken (2017). ISBN 9781119298588

    Google Scholar 

  4. Vanthienen, J., Witte, K.: Data Analytics Applications in Education, 1st ed. CRC Press/Taylor Francis Group/Auerbach Publications, New York (2017). ISBN 9781498769273

    Google Scholar 

  5. Somani, A., Deka, G.: Big Data Analytics: Tools and Technology for Effective Planning. CRC Press/Taylor & Francis Group, New York (2018). ISBN 9781138032392

    Google Scholar 

  6. IBM Software Group: Descriptive, Predictive, Prescriptive: Transforming Asset and Facilities Management with Analytics. IBM Corporation Software Group, New York (2013)

    Google Scholar 

  7. Chowdhury, M., Apon, A., Dey, K.: Data Analytics for Intelligent Transportation Systems, 1st edn. Elsevier Science Publishers, Amsterdam (2017). ISBN 9780128097151

    Google Scholar 

  8. Sami, M.: The Evolution of Analytics. https://melsatar.blog/2017/07/30/the-evolution-of-analytics/. Accessed 3 May 2018

  9. Dankov, Y., Birov, D.: Extended conceptual framework for business analytics supporting innovations. In: Ketikidis, P., Solomon, A., Thessaloniki, G. (eds.) Proceedings of International Conference for Entrepreneurship, Innovation and Regional Development ICEIRD10, 31 August–1 September 2017 (2017). ISBN 9789609416115, ISSN 24115320

    Google Scholar 

  10. Peña-Ayala, A. (ed.): Learning Analytics: Fundaments, Applications, and Trends. SSDC, vol. 94. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-52977-6

    Book  Google Scholar 

  11. Dodd, J.: Healthcare It Transformation: Bridging Innovation, Integration, Interoperability, and Analytics, 1st ed. CRC Press/Taylor & Francis Group/Productivity Press, New York (2017). ISBN 9781498778442

    Google Scholar 

  12. Lowman, M.: A Practical Guide to Analytics for Governments: Using Big Data for Wiley and SAS Business Series. Wiley, Hoboken (2017). ISBN 9781119362821

    Book  Google Scholar 

  13. Yan, W.Q.: Introduction to Intelligent Surveillance: Surveillance Data Capture, Transmission, and Analytics, 2nd edn. Springer International Publishing AG, Switzerland (2017). https://doi.org/10.1007/978-3-319-60228-8. ISBN 9783319602271

    Book  Google Scholar 

  14. Thuraisingham, B., Parveen, P., Masud, M., Khan, L.: Big Data Analytics with Applications in Insider Threat Detection, 1st ed. CRC Press/Taylor & Francis Group/Auerbach Publications, Boston (2017). ISBN 9781498705479

    Google Scholar 

  15. Alsmadi, I., Karabatis, G., Aleroud, A.: Information Fusion for Cyber-Security Analytics. Studies in Computational Intelligence, vol. 691, 1st edn. Springer International Publishing, Switzerland (2017). https://doi.org/10.1007/978-3-319-44257-0. ISBN 9783319442563, 9783319442570

    Book  Google Scholar 

  16. Schniederjans, M., Schniederjans, D., Christopher, J.: Business Analytics Principles, Concepts and Applications. Pearson Education Inc, USA (2014). ISBN 9780133552188

    Google Scholar 

  17. Stubbs, E.: The Value of Business Analytics. Wiley, Hoboken (2011)

    Book  Google Scholar 

  18. Bichler, M., Heinzl, A., van der Aalst, W.M.P.: Business analytics and data science: once again? Bus. Inf. Syst. Eng. 59(2), 77–79 (2017). https://doi.org/10.1007/s12599-016-0461-1

    Article  Google Scholar 

  19. Shmueli, G., Bruce, P.: Data Mining for Business Analytics: Concepts, Techniques, and Applications with XLMiner, 3rd edn. Wiley, Hoboken (2016). ISBN 9781118729274

    Google Scholar 

  20. Evans, J.: Business Analytics: Methods, Models, and Decisions, 2nd edn. Pearson Education, Boston (2017). ISBN 9781292095448

    Google Scholar 

  21. Bayrak, T., A review of business analytics: a business enabler or another passing fad. In: World Conference on Technology, Innovation and Entrepreneurship, Procedia - Social and Behavioral Sciences vol. 195, pp 230 – 239, Elsevier Ltd (2015). ISSN: 18770428

    Article  Google Scholar 

  22. Sugumaran, V., Sangaiah, A., Thangavelu, A.: Computational Intelligence Applications in Business and Big Data Analytics, 1st edn. CRC Press/Taylor & Francis Group, FL (2017). ISBN 9781498761017

    Google Scholar 

  23. Keim, D.; Mansmann, F.; Stoffel, A., Ziegler, H.: Visual Analytics., Encyclopedia of Database Systems/M. Tamer Özsu et al. (ed.) - Heidelberg: Springer, (2008). http://nbn-resolving.de/urn:nbn:de:bsz:352-opus-68335. Accessed 2 Nov 2018

  24. Keim, D., Kohlhammer, J., Ellis, G., Mansmann, F.: Mastering the Information Age – Solving Problems with Visual Analytics. Eurographics Association, Goslar (2010). ISBN 9783905673777

    Google Scholar 

  25. Thomas, J., Cook, K.: Illuminating the path: the research and development agenda for visual analytics. In: National Visualization and Analytics Center (2005). ISBN:0769523234

    Google Scholar 

  26. Keim, D., Andrienko, G., Fekete, J.-D., Görg, C., Kohlhammer, J., Melançon, G.: Visual Analytics: Definition, Process, and Challenges. In: Kerren, A., Stasko, John T., Fekete, J.-D., North, C. (eds.) Information Visualization. LNCS, vol. 4950, pp. 154–175. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-70956-5_7

    Chapter  Google Scholar 

  27. Telea, A., Ersoy, O., Voinea, L.: Visual analytics in software maintenance: challenges and opportunities. In: International Symposium on Visual Analytics Science and Technology, Kohlhammer, J., Keim, D. (eds.), the Eurographics Association (2010)

    Google Scholar 

  28. Dill, J., Earnshaw, R., Kasik, D., Vince, J., Chung Wong, P.: Expanding the Frontiers of Visual Analytics and Visualization. Springer, London Dordrecht Heidelberg New York (2012). https://doi.org/10.1007/978-1-4471-2804-5. ISBN 9781447128038

    Book  Google Scholar 

  29. Heer, J., Agrawala, M.: Design considerations for collaborative visual analytics, information visualization. In: Proceedings of IEEE Symposium on Visual Analytics Science and Technology 2007, Sacramento, CA, USA, vol. 7 (2008). https://doi.org/10.1057/palgrave.ivs.9500167

    Article  Google Scholar 

  30. Suh, S., Anthony, T.: Big Data and Visual Analytics, 1st edn. Springer International Publishing AG, Switzerland (2017). ISBN 9783319639154

    Book  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Mathematics and Informatics, Sofia University “St. Kliment Ohridski”, Sofia, Bulgaria

    Yavor Dankov & Dimitar Birov

Authors
  1. Yavor Dankov
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dimitar Birov
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yavor Dankov .

Editor information

Editors and Affiliations

  1. Bulgarian Academy of Sciences, Institute of Mathematics and Informatics (IMI)/Interdisciplinary Institute for Collaboration and Research on Enterprise Systems and Technology (IICREST), Sofia, Bulgaria

    Boris Shishkov

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Dankov, Y., Birov, D. (2018). General Architectural Framework for Business Visual Analytics. In: Shishkov, B. (eds) Business Modeling and Software Design. BMSD 2018. Lecture Notes in Business Information Processing, vol 319. Springer, Cham. https://doi.org/10.1007/978-3-319-94214-8_19

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-94214-8_19

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-94213-1

  • Online ISBN: 978-3-319-94214-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation