Museum (Big) Data Mining in Qatar: researching and developing methods, techniques, and a policy

Georgios Papaioannou, UCL Qatar, Qatar

This demonstration presents a new project on museum Big Data and data mining using data related to museums in Qatar as a case study. In Qatar museums, as elsewhere in the world, there is an emerging need to detect new and discover hidden and useful information, patterns, clusters and relationships among large sums of museum-related data. Addressing this need requires ethical considerations and processes, a thorough understanding of contexts in the real and the digital world, and cross-disciplinary Big Data methods, techniques and testing, all of which fall within this new project’s objectives and discussion points demonstrated here for the first time.
The aims of this Research Project at University College London in Qatar are to
• contribute to the development of Big Data and Data Mining methods and techniques on museum datasets,
• produce a policy document on Big Data and the museums in Qatar,
• initiate at University College London in Qatar a research team on Museum / Cultural Heritage Big Data,
• explore links / collaborations to information seeking research schemes related to Social Media cultural dataset-producing processes in Qatar.

Arvanitis, K., Gilmore, A., Franzi Florack, F. & Zuanni, C., “Data culture and organisational practice”. MW2016: Museums and the Web 2016.

Boucher Ferguson, R. (2013). “The Big Deal About a Big Data Culture (and Innovation)”. MIT Sloan Management Review 54 (2): 1–5.

Cunliffe, D., Gamble, B., Nicholls, J., Cawardine-Palmer, M. & Roche, F. (2014). “Torf. Exploring creative campaigns, using mobile and web technology, to capture audience data and establish ongoing engagement between the audience and the arts organisation”.

Dexibit, A.J., Stein, R. & Firth, M. (2017). “Big Data And Analytics: What We’ve Learned So Far”.

Knell, J., Bunting, C., Gilmore, A., Arvanitis, K., Florack, F. & Merriman, N. (2015). HOME: Quality Metrics. Research & Development Report. London: NESTA.

Lilley, A. (2015). “What can Big Data do for the cultural sector? Audience Finder”.

Lilley, A., & Moore, P. (2013). “Counting What Counts: What big data can do for the cultural sector”.

Mateos-Garcia, J. (2014). “The art of analytics: using bigger data to create value in the arts and cultural sector”. NESTA blogs.

Stein, R. & Wyman, B. (2014) “Seeing the Forest and the Trees: How Engagement Analytics can Help Museums Connect to Audiences at Scale”

Villaespesa, E., & Tasisch. T. (2012). “Making Sense of Numbers: A Journey of Spreading the Analytics Culture at Tate”. In: N. Proctor & R. Cherry (eds.). Museums and the Web 2012. Silver Spring, MD: Museums and the Web.

Walter, M. (2015). “The Data Won’t Save Us”.

Walter, M. (2016). “Data Warehousing and Building Analytics at Cooper Hewitt, Smithsonian Design Museum”.