Reprint
Big Data Analytics for Cultural Heritage
Edited by
January 2023
210 pages
- ISBN978-3-0365-6326-8 (Hardback)
- ISBN978-3-0365-6327-5 (PDF)
This book is a reprint of the Special Issue Big Data Analytics for Cultural Heritage that was published in
Computer Science & Mathematics
Summary
In this edition, we focused on big data analytics methods and tools that have been specifically developed for the domain of cultural heritage, as well as on experiences from the adaptation and/or application of general-purpose solutions in the domain of cultural heritage. The aim was to gather solutions, but also to summarise the lessons learnt, methodologies, and good practices that researchers and practitioners can use as a basis for their own work in the domain.
Format
- Hardback
License and Copyright
© 2022 by the authors; CC BY-NC-ND license
Keywords
cultural analytics; cultural data; search engine optimization; SEO strategy; SEO factors; big data; websites visibility; predictive modeling; website security; website load speed; user behavior; intelligent interaction; semantics; usability; mixed reality; cultural experience; cultural heritage; big data management; data lake; data store; analytics and visualization; open source; cultural tourism; typology; personalization; user experience; recommender systems; collaborative filtering; hybrid matrix factorization; persona; user experience; user interface design; topic modelling; usability; personalization; cultural events; big data; open data; game-based learning; education; geography; board games; monopoly; gamification; cultural gaming; cultural heritage preservation; player annotations; data enrichment; Bartle taxonomy; Simon–Ando clustering; tensor algebra; multilinear distance; Julia; feminism; hashtivism; Twitter; social network analysis; Machine Learning; big data; culture; cultural informatics; museum informatics; mixed environment; social media; personalization; cultural heritage; mobile tourist guide; profile classification; user interface; cultural destinations; personalized suggestions; n/a