Skip to main content

Usage Analytics: A Process to Extract and Analyse Usage Data to Understand User Behaviour in Cloud

  • Conference paper
  • First Online:
Computer-Human Interaction Research and Applications (CHIRA 2017)

Abstract

Usage in the software field deals with knowledge about how end-users use the application and how the application responds to the users’ action. Understanding usage data can help developers optimise the application development process by prioritising the resources such as time, cost and man power on features of the application which are critical for the user. However, in a complex cloud computing environment, the process of extracting and analysing usage data is difficult since the usage data is spread across various front-end interfaces and back-end underlying infrastructural components of the cloud that host the application and are of different types and formats. In this paper, we propose usage analytics, a process to extract and analyse usage to understand the behavioural usage patterns of the user with the aim to identify features critical to user. We demonstrate how to identify the features in a cloud based application, how to extract and analyse the usage data to understand the user behaviour.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    https://www.forbes.com/sites/louiscolumbus/2017/04/29/roundup-of-cloud-computing-forecasts-2017/#7155e14e31e8.

  2. 2.

    https://apps.odoo.com/apps/online/note/.

  3. 3.

    https://colab.research.google.com.

  4. 4.

    http://jupyter.org.

References

  1. Al-Bayati, B., Clarke, N., Dowland, P.: Adaptive behavioral profiling for identity verification in cloud computing: a model and preliminary analysis. GSTF J. Comput. (JoC) 5(1), 21 (2016)

    Google Scholar 

  2. Bezemer, C.P., Zaidman, A., Platzbeecker, B., Hurkmans, T., Hart, A.: Enabling multi-tenancy: an industrial experience report. In: IEEE International Conference on Software Maintenance, pp. 1–8, September 2010. https://doi.org/10.1109/ICSM.2010.5609735

  3. Bucklin, R.E., Sismeiro, C.: Click here for internet insight: advances in clickstream data analysis in marketing. J. Interact. Mark. 23(1), 35–48 (2009)

    Article  Google Scholar 

  4. Castañeda, J.A., Rodríguez, M.A., Luque, T.: Attitudes’ hierarchy of effects in online user behaviour. Online Inf. Rev. 33(1), 7–21 (2009). https://doi.org/10.1108/14684520910944364. http://www.emeraldinsight.com/doi/10.1108/14684520910944364

    Article  Google Scholar 

  5. Corapi, D., Ray, O., Russo, A., Bandara, A.K., Lupu, E.C.: Learning rules from user behaviour. In: Iliadis, Tsoumakasis, Vlahavas, Bramer (eds.) Artificial Intelligence Applications and Innovations III, vol. 296, pp. 459–468. Springer, Boston (2009). https://doi.org/10.1007/978-1-4419-0221-4_54

    Google Scholar 

  6. Dang-Nguyen, D.T., Kesavulu, M., Helfert, M.: Usage analytics: research directions to discover insights from cloud-based applications. In: International Conference on Smart Cities and Green ICT Systems (SMARTGREENS) (2018, accepted)

    Google Scholar 

  7. Gasparetti, F.: Modeling user interests from web browsing activities. Data Min. Knowl. Disc. 31(2), 1–46 (2016). https://doi.org/10.1007/s10618-016-0482-x

    Article  MathSciNet  Google Scholar 

  8. Kesavulu, M., Dang-Nguyen, D.T., Helfert, M., Bezbradica, M.: An overview of user-level usage monitoring in cloud environment. In: The UK Academy for Information Systems (UKAIS) (2018)

    Google Scholar 

  9. Kesavulu, M., Helfert, M., Bezbradica, M.: A usage-based data extraction framework for cloud-based application - an human-computer interaction approach. In: International Conference on Computer-Human Interaction Research and Applications (CHIRA), Madeira, Portugal (2017)

    Google Scholar 

  10. Märtin, C., Herdin, C., Engel, J.: Model-based user-interface adaptation by exploiting situations, emotions and software patterns. In: International Conference on Computer-Human Interaction Research and Applications (2017)

    Google Scholar 

  11. Mulder, I., Ter Hofte, G.H., Kort, J.: SocioXensor: Measuring user behaviour and user eXperience in conteXt with mobile devices. In: Proceedings of Measuring Behavior, pp. 355–358, January 2005

    Google Scholar 

  12. Petruch, K., Tamm, G., Stantchev, V.: Deriving in-depth knowledge from IT-performance data simulations. Int. J. Knowl. Soc. Res. 3(2), 13–29 (2012). https://doi.org/10.4018/jksr.2012040102. http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/jksr.2012040102

    Article  Google Scholar 

  13. Xu, G., Zhang, Y., Yi, X.: Modelling user behaviour for Web recommendation using LDA model. In: Proceedings - 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Workshops, WI-IAT Workshops 2008, pp. 529–532 (2008). https://doi.org/10.1109/WIIAT.2008.313

  14. Yang, J., et al.: Multimedia recommendation and transmission system based on cloud platform. Future Gener. Comput. Syst. 70, 94–103 (2017)

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported with the financial support of the Science Foundation Ireland grant 13/RC/2094 and co-funded under the European Regional Development Fund through the Southern & Eastern Regional Operational Programme to Lero - the Irish Software Research Centre (www.lero.ie).

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Manoj Kesavulu , Duc-Tien Dang-Nguyen or Marija Bezbradica .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Kesavulu, M., Dang-Nguyen, DT., Bezbradica, M., Helfert, M. (2019). Usage Analytics: A Process to Extract and Analyse Usage Data to Understand User Behaviour in Cloud. In: Holzinger, A., Silva, H., Helfert, M. (eds) Computer-Human Interaction Research and Applications. CHIRA 2017. Communications in Computer and Information Science, vol 654. Springer, Cham. https://doi.org/10.1007/978-3-030-32965-5_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-32965-5_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-32964-8

  • Online ISBN: 978-3-030-32965-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics