Skip to main content

Analysis of Transaction Logs from National Museums Liverpool

Part of the Lecture Notes in Computer Science book series (LNISA,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

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-30760-8_7
  • Chapter length: 15 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   69.99
Price excludes VAT (USA)
  • ISBN: 978-3-030-30760-8
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   89.99
Price excludes VAT (USA)
Fig. 1.
Fig. 2.

Notes

  1. 1.

    http://www.liverpoolmuseums.org.uk/.

  2. 2.

    https://www.europeana.eu/portal/en.

  3. 3.

    https://lite.ip2location.com/ip-address-ranges-by-country.

  4. 4.

    Alternative algorithms such as k-modes (k-prototypes) and DBScan were also tested, but no stable clusters emerged.

  5. 5.

    Based on the IP2Location IP4 allocated IP address ranges; however, it is noted that the United Nations only identifies 195.

References

  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)

    CrossRef  Google Scholar 

  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)

    CrossRef  Google Scholar 

  3. Peters, T.A.: The history and development of transaction log analysis. Library Hi Tech 11(2), 41–66 (1993)

    CrossRef  MathSciNet  Google Scholar 

  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)

    CrossRef  Google Scholar 

  5. Ciber: Europeana 2012–2013: usage and performance update. Technical report, CIBER Research, July 2013

    Google Scholar 

  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. 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_34

    CrossRef  Google Scholar 

  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. Eirinaki, M., Vazirgiannis, M.: Web mining for web personalization. ACM Trans. Internet Technol. (TOIT) 3(1), 1–27 (2003)

    CrossRef  Google Scholar 

  10. Falk, J.H.: Identity and the Museum Visitor Experience. Left Coast Press, Walnut Creek (2009)

    Google Scholar 

  11. Templeton, C.A.: Museum visitor engagement through resonant, rich and interactive experiences (2011)

    Google Scholar 

  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. 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_16

    CrossRef  Google Scholar 

  14. Pantano, E.: Virtual cultural heritage consumption: a 3D learning experience. Int. J. Technol. Enhanc. Learn. 3(5), 482–495 (2011)

    CrossRef  Google Scholar 

  15. Booth, B.: Understanding the information needs of visitors to museums. Mus. Manag. Curatorship 17(2), 139–157 (1998)

    CrossRef  Google Scholar 

  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. 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_17

    CrossRef  Google Scholar 

  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. 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. 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_12

    CrossRef  Google Scholar 

  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)

    CrossRef  Google Scholar 

  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. 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. Stenmark, D.: Identifying clusters of user behavior in intranet search engine log files. J. Am. Soc. Inf. Sci. Technol. 59(14), 2232–2243 (2008)

    CrossRef  Google Scholar 

  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. 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. 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. 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. 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. 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 

Download references

Acknowledgements

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

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to David Walsh .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Walsh, D., Clough, P., Hall, M.M., Hopfgartner, F., Foster, J., Kontonatsios, G. (2019). Analysis of Transaction Logs from National Museums Liverpool. In: Doucet, A., Isaac, A., Golub, K., Aalberg, T., Jatowt, A. (eds) Digital Libraries for Open Knowledge. TPDL 2019. Lecture Notes in Computer Science(), vol 11799. Springer, Cham. https://doi.org/10.1007/978-3-030-30760-8_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-30760-8_7

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-30759-2

  • Online ISBN: 978-3-030-30760-8

  • eBook Packages: Computer ScienceComputer Science (R0)