Abstract
The data science discipline can play a crucial role in developing effective data driven strategies for the valorization and promotion of the cultural heritage (CH) domain. Machine learning approaches can provide new perspectives, allowing knowledge extraction and insights generation from data since in the last decade CH domain has benefited from the applications of internet of things (IoT) solutions in order to improve visitors’ experience. Analyzing a great amount of data increasingly requires the use of advanced mathematical algorithms and therefore requires distribution, calculation and digital protection services. Data represent a great challenge for the CH domain, as well as a resource; this paper presents and discusses the application of a machine learning approach on IoT cultural data collected in the National Archaeological Museum of Naples. With the deployment of some Bluetooth sensing boards we collected the visit paths of the users in a non-invasive way. The research goal is to analyze and classify the collected visiting behavioural data in order to produce useful insights for cultural stakeholders. The knowledge of people behaviours can help museum organizations both in terms of medium-long term strategy and also in terms of strictly operational decisions.
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Bohnert F, Zukerman I, Berkovsky S, Baldwin T, Sonenberg L (2008) Using interest and transition models to predict visitor locations in museums. AI Commun 21(2–3):195–202
Boriah S, Chandola V, Kumar V (2008) Similarity measures for categorical data: a comparative evaluation, vol 1, pp 243–254
Budiaji W (2019) kmed: Distance-Based k-Medoids. R package version (2)
Cha SH (2007) Comprehensive survey on distance/similarity measures between probability density functions. Int J Math Models Methods Appl Sci 1(4):300–307
Charrad M, Ghazzali N, Boiteau V, Niknafs A (2014) NbClust: an R package for determining the relevant number of clusters in a data set. J Stat Softw 61(6):1–36
Ciabattoni L, Foresi G, Monteriù A, Pepa L, Pagnotta DP, Spalazzi L, Verdini F (2019) Real time indoor localization integrating a model based pedestrian dead reckoning on smartphone and ble beacons. J Ambient Intell Humaniz Comput 10(1):1–12
Cuomo S, De Michele P, Pragliola M (2017) A computational scheme to predict dynamics in iot systems by using particle filter. Concurr Comput Practi Exp 29(11):e4101
Cuomo S, De Michele P, Piccialli F, Sangaiah AK (2018) Reproducing dynamics related to an internet of things framework: a numerical and statistical approach. J Parallel Distrib Comput 118:359–368
Desgraupes B (2018) clusterCrit: clustering indices. R package version 1.2.8
Dice LR (1945) Measures of the amount of ecologic association between species. Ecology 26(3):297–302
dos Santos TR, Zárate LE (2015) Categorical data clustering: what similarity measure to recommend? Expert Syst Appl 42(3):1247–1260
Eklund P, Wray T, Goodall P, Lawson A (2012) Design, information organisation and the evaluation of the virtual museum of the Pacific digital ecosystem. J Ambient Intell Humaniz Comput 3(4):265–280
Engle S, Whalen S, Joshi A, Pollard KS (2017) Unboxing cluster heatmaps. BMC Bioinform 18(2):63
Hooper-Greenhill E (2007) Studying visitors, vol 22. Wiley, New York, pp 362–376
Kakhki FD, Freeman SA, Mosher GA (2019) Evaluating machine learning performance in predicting injury severity in agribusiness industries. Saf Sci 117:257–262
Kanda T, Shiomi M, Perrin L, Nomura T, Ishiguro H, Hagita N (2007) Analysis of people trajectories with ubiquitous sensors in a science museum. In: Proceedings 2007 IEEE international conference on robotics and automation, IEEE, pp 4846–4853
Kirchberg V, Tröndle M (2015) The museum experience: mapping the experience of fine art. Curator Museum J 58(2):169–193
Labatut V, Cherifi H (2012) Accuracy measures for the comparison of classifiers. arXiv:1207.3790
Lamirel J, Dugué N, Cuxac P (2016) New efficient clustering quality indexes. In: 2016 international joint conference on neural networks, IJCNN 2016, Vancouver, BC, Canada, July 24–29, 2016, pp 3649–3657
Manning CD, Raghavan P, Schütze H (2008) Introduction to information retrieval. Cambridge University Press, New York
Martella C, Miraglia A, Frost J, Cattani M, van Steen M (2017) Visualizing, clustering, and predicting the behavior of museum visitors. Pervas Mob Comput 38:430–443
Melton AW (1935) Problems of installation in museums of art. Parnassus 7(6):29–30
Murtagh F, Legendre P (2014) Ward’s hierarchical agglomerative clustering method: which algorithms implement ward’s criterion? J Classif 31(3):274–295
Mygind L, Bentsen P (2017) Reviewing automated sensor-based visitor tracking studies: beyond traditional observational methods? Visit Stud 20(2):202–217
Orgaz GB, Jung JJ, Camacho D (2016) Social big data: recent achievements and new challenges. Inf Fusion 28:45–59
Piccialli F, Yoshimura Y, Benedusi P, Ratti C, Cuomo S (2019) Lessons learned from longitudinal modeling of mobile-equipped visitors in a complex museum. Neural Comput Appl 2019:1–17
R Core Team (2018) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna
Sarkar D (2008) Lattice: multivariate data visualization with R. Springer, New York
Seok MG, Park D (2018) A novel multi-level evaluation approach for human-coupled IOT applications. J Ambient Intell Hum Comput 2018:1–4
Sookhanaphibarn K, Thawonmas R (2009) A movement data analysis and synthesis tool for museum visitors’ behaviors. In: Muneesawang P, Wu F, Kumazawa I, Roeksabutr A, Liao M, Tang X (eds) Advances in multimedia information processing—PCM 2009. Springer, Heidelberg, pp 144–154
Sørensen T (1948) A Method of establishing groups of equal amplitude in plant sociology based on similarity of species content and its application to analyses of the vegetation on Danish Commons, Biologiske skrifter, vol 4-5. I kommission hos E. Munksgaard
Sparacino F (2002) The museum wearable: real-time sensor-driven understanding of visitors’ interests for personalized visually-augmented museum experiences. In: In: Proceedings of museums and the web (MW2002), pp 17–20
van der Loo M (2014) The stringdist package for approximate string matching. R J 6:111–122
Vendramin L, Campello RJGB, Hruschka ER (2010) Relative clustering validity criteria: a comparative overview. Stat Anal Data Min ASA Data Sci J 3(4):209–235
Yoshimura Y, Sobolevsky S, Ratti C, Girardin F, Carrascal JP, Blat J, Sinatra R (2014) An analysis of visitors’ behavior in the louvre museum: a study using bluetooth data. Environ Plan 41(6):1113–1131
Yoshimura Y, Krebs A, Ratti C (2017) Noninvasive bluetooth monitoring of visitors’ length of stay at the louvre. IEEE Pervas Comput 16(2):26–34
Zancanaro M, Kuflik T, Boger Z, Goren-Bar D, Goldwasser D (2007) Analyzing museum visitors’ behavior patterns. In: Conati C, McCoy K, Paliouras G (eds) User modeling 2007. Springer, Heidelberg, pp 238–246
Acknowledgements
We thank the National Archaeological Museum of Naples (MANN—https://www.museoarcheologiconapoli.it), its staff and its director Dr. Paolo Giulierini for the availability and the support for the data collection task. This work have been supported by the C.E.T.R.A—Cultural Equipment with Transmedial Recommendation Analytics research project, CUP: B63D18000390007.
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Piccialli, F., Cuomo, S., Cola, V.S.d. et al. A machine learning approach for IoT cultural data. J Ambient Intell Human Comput 15, 1715–1726 (2024). https://doi.org/10.1007/s12652-019-01452-6
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DOI: https://doi.org/10.1007/s12652-019-01452-6