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Analysis of Transaction Logs from National Museums Liverpool

  • David WalshEmail author
  • Paul Clough
  • Mark M. Hall
  • Frank Hopfgartner
  • Jonathan Foster
  • Georgios Kontonatsios
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11799)

Abstract

The websites of Cultural Heritage institutions attract the full range of users, from professionals to novices, for a variety of tasks. However, many institutions are reporting high bounce rates and therefore seeking ways to better engage users. The analysis of transaction logs can provide insights into users’ searching and navigational behaviours and support engagement strategies. In this paper we present the results from a transaction log analysis of web server logs representing user-system interactions from the seven websites of National Museums Liverpool (NML). In addition, we undertake an exploratory cluster analysis of users to identify potential user groups that emerge from the data. We compare this with previous studies of NML website users.

Keywords

Digital cultural heritage Museum website Users Survey Transaction log analysis Cluster analysis 

Notes

Acknowledgements

We would like to thank National Museums Liverpool for providing access to the web server transaction logs.

References

  1. 1.
    Jones, S., Cunningham, S.J., McNab, R., Boddie, S.: A transaction log analysis of a digital library. Int. J. Digit. Libr. 3(2), 152–169 (2000)CrossRefGoogle Scholar
  2. 2.
    McKay, D., Buchanan, G., Chang, S.: It ain’t what you do, it’s the way that you do it: design guidelines to better support online browsing. Proc. Assoc. Inf. Sci. Technol. 55(1), 347–356 (2018)CrossRefGoogle Scholar
  3. 3.
    Peters, T.A.: The history and development of transaction log analysis. Library Hi Tech 11(2), 41–66 (1993)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Jansen, B.J., Spink, A., Saracevic, T.: Real life, real users, and real needs: a study and analysis of user queries on the web. Inf. Process. Manag. 36(2), 207–227 (2000)CrossRefGoogle Scholar
  5. 5.
    Ciber: Europeana 2012–2013: usage and performance update. Technical report, CIBER Research, July 2013Google Scholar
  6. 6.
    Walsh, D., Hall, M.M., Clough, P., Foster, J.: Characterising online museum users: a study of the National Museums Liverpool museum website. Int. J. Digit. Libr. (2018).  https://doi.org/10.1007/s00799-018-0248-8
  7. 7.
    Walsh, D., Hall, M., Clough, P., Foster, J.: The ghost in the museum website: investigating the general public’s interactions with museum websites. In: Kamps, J., Tsakonas, G., Manolopoulos, Y., Iliadis, L., Karydis, I. (eds.) TPDL 2017. LNCS, vol. 10450, pp. 434–445. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-67008-9_34CrossRefGoogle Scholar
  8. 8.
    Farrell, S.: Search-log analysis: the most overlooked opportunity in web UX research, July 2017. https://www.nngroup.com/articles/search-log-analysis/. Accessed 14 Mar 2019
  9. 9.
    Eirinaki, M., Vazirgiannis, M.: Web mining for web personalization. ACM Trans. Internet Technol. (TOIT) 3(1), 1–27 (2003)CrossRefGoogle Scholar
  10. 10.
    Falk, J.H.: Identity and the Museum Visitor Experience. Left Coast Press, Walnut Creek (2009)Google Scholar
  11. 11.
    Templeton, C.A.: Museum visitor engagement through resonant, rich and interactive experiences (2011)Google Scholar
  12. 12.
    Spellerberg, M., Granata, E., Wambold, S.: Visitor-first, mobile-first: designing a visitor-centric mobile experience. In: Museums and the Web (2016)Google Scholar
  13. 13.
    Vilar, P., Šauperl, A.: Archival literacy: different users, different information needs, behaviour and skills. In: Kurbanoğlu, S., Špiranec, S., Grassian, E., Mizrachi, D., Catts, R. (eds.) ECIL 2014. CCIS, vol. 492, pp. 149–159. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-14136-7_16CrossRefGoogle Scholar
  14. 14.
    Pantano, E.: Virtual cultural heritage consumption: a 3D learning experience. Int. J. Technol. Enhanc. Learn. 3(5), 482–495 (2011)CrossRefGoogle Scholar
  15. 15.
    Booth, B.: Understanding the information needs of visitors to museums. Mus. Manag. Curatorship 17(2), 139–157 (1998)CrossRefGoogle Scholar
  16. 16.
    Marchionini, G., Plaisant, C., Komlodi, A.: The people in digital libraries: multifaceted approaches to assessing needs and impact. In: Social Practice in Design and Evaluation, Digital Library Use, pp. 119–160 (2003)Google Scholar
  17. 17.
    Clough, P., Hill, T., Paramita, M.L., Goodale, P.: Europeana: what users search for and why. In: Kamps, J., Tsakonas, G., Manolopoulos, Y., Iliadis, L., Karydis, I. (eds.) TPDL 2017. LNCS, vol. 10450, pp. 207–219. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-67008-9_17CrossRefGoogle Scholar
  18. 18.
    Russell-Rose, T., Clough, P.: Mining search logs for usage patterns. In: Text Mining and Visualization: Case Studies using Open-Source Tools, vol. 40 (2016) Google Scholar
  19. 19.
    Kachhadiya, B.C., Patel, B.: A survey on sequential pattern mining algorithm for web log pattern data. In: 2018 2nd International Conference on Trends in Electronics and Informatics (ICOEI), pp. 1269–1273. IEEE (2018)Google Scholar
  20. 20.
    Lau, T., Horvitz, E.: Patterns of search: analyzing and modeling web query refinement. In: Kay, J. (ed.) UM99 User Modeling. CICMS, vol. 407, pp. 119–128. Springer, Vienna (1999).  https://doi.org/10.1007/978-3-7091-2490-1_12CrossRefGoogle Scholar
  21. 21.
    Chen, H.M., Cooper, M.D.: Using clustering techniques to detect usage patterns in a web-based information system. J. Am. Soc. Inf. Sci. Technol. 52(11), 888–904 (2001)CrossRefGoogle Scholar
  22. 22.
    Wang, G., Zhang, X., Tang, S., Zheng, H., Zhao, B.Y.: Unsupervised clickstream clustering for user behavior analysis. In: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems, pp. 225–236. ACM (2016)Google Scholar
  23. 23.
    Zhang, J., Kamps, J.: Search log analysis of user stereotypes, information seeking behavior, and contextual evaluation. In: Proceedings of the Third Symposium on Information Interaction in Context, pp. 245–254. ACM (2010)Google Scholar
  24. 24.
    Stenmark, D.: Identifying clusters of user behavior in intranet search engine log files. J. Am. Soc. Inf. Sci. Technol. 59(14), 2232–2243 (2008)CrossRefGoogle Scholar
  25. 25.
    He, D., Göker, A.: Detecting session boundaries from web user logs. In: Proceedings of the BCS-IRSG 22nd Annual Colloquium on Information Retrieval Research, pp. 57–66 (2000)Google Scholar
  26. 26.
    Bogaard, T., Hollink, L., Wielemaker, J., Hardman, L., van Ossenbruggen, J.: Searching for old news: user interests and behavior within a national collection. In: Proceedings of the 2019 Conference on Human Information Interaction and Retrieval, pp. 113–121. ACM (2019)Google Scholar
  27. 27.
    Bholowalia, P., Kumar, A.: EBK-means: a clustering technique based on elbow method and k-means in wsn. Int. J. Comput. Appl. 105(9), 17–24 (2014)Google Scholar
  28. 28.
    Skov, M., Ingwersen, P.: Exploring information seeking behaviour in a digital museum context. In: Proceedings of the Second International Symposium on Information Interaction in Context, IIiX 2008, pp. 110–115. ACM, New York (2008)Google Scholar
  29. 29.
    Skov, M.: The reinvented museum: exploring information seeking behaviour in a digital museum context. Ph.D. thesis, Københavns Universitet’Københavns Universitet’, Faculty of Humanities, School of Library and Information Science, Royal School of Library and Information Science (2009, unpublished thesis)Google Scholar
  30. 30.
    Elsweiler, D., Wilson, M.L., Lunn, B.K.: Chapter 9 understanding casual-leisure information behaviour. In: New Directions in Information Behaviour. Library and Information Science, vol. 1, pp. 211–241. Emerald Group Publishing Limited (2011)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Edge Hill UniversityOrmskirkUK
  2. 2.University of SheffieldSheffieldUK
  3. 3.Martin Luther University Halle-WittenbergHalleGermany
  4. 4.Peak IndicatorsChesterfieldUK

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