Big Data Analytics for Cultural Heritage / / by Manolis Wallace, Vassilis Poulopoulos, Angeliki Antoniou (editors)

• Pubblicazione: [Place of publication not identified] : , : MDPI - Multidisciplinary Digital Publishing Institute, , 2023
• Descrizione fisica: 1 online resource (210 pages)
• Disciplina: 658/.05631
• Soggetto topico: Data mining; Management - Data processing; Big data
• Persona (resp. second.): WallaceManolis; PoulopoulosVassilis; AntoniouAngeliki
• Nota di contenuto: About the Editors vii -- An Overview of Big Data Analytics for Cultural Heritage 1 -- A Semantic Mixed Reality Framework for Shared Cultural Experiences Ecosystems 3 -- Hydria: An Online Data Lake for Multi-Faceted Analytics in the Cultural Heritage Domain 25 -- Data-Assisted Persona Construction Using Social Media Data 53 -- A Personalized Heritage-Oriented Recommender System Based on Extended Cultural Tourist Typologies 67 -- ACUX Recommender: A Mobile Recommendation System for Multi-Profile Cultural Visitors Based on Visiting Preferences Classification 85 -- Big Data Analytics for Search Engine Optimization 97 -- Using Big and Open Data to Generate Content for an Educational Game to Increase Student Performance and Interest 119 -- Annotation-Assisted Clustering of Player Profiles in Cultural Games: A Case for Tensor Analytics in Julia 139 -- Networks and Stories. Analyzing the Transmission of the Feminist Intangible Cultural Heritage on Twitter 163 -- Digital Technologies and the Role of Data in Cultural Heritage: The Past, the Present, and the Future 181.
• Sommario/riassunto: 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.
• Titolo autorizzato: Big Data Analytics for Cultural Heritage
• ISBN: 3-0365-6327-X
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910647230103321
• Lo trovi qui: Univ. Federico II