Big Data Analytics for Cultural Heritage 2nd Edition

Special Issue Editors


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Guest Editor
ΓΑΒ LAB—Knowledge and Uncertainty Research Laboratory, Campus of the University of Peloponnese, 22132 Trípoli, Greece
Interests: cultural informatics; semantics; uncertainty
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
ΓΑΒ LAB—Knowledge and Uncertainty Research Laboratory, Campus of the University of Peloponnese, 22132 Trípoli, Greece
Interests: data mining; big data; social media analytics
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
atlanTTic Research Center, Department of Telematics Engineering, University of Vigo, 36310 Vigo, Spain
Interests: applied artificial intelligence; knowledge modeling; semantic reasoning; interactive storytelling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Following the success of the Special Issue of Big Data and Cognitive Computing on “Big Data Analytics for Cultural Heritage”, we are delighted to announce a new Special Issue entitled “Big Data Analytics for Cultural Heritage 2nd Edition”.

Although big data was initially coined as a term to represent our inability to manage and process the volumes of data that we record, recent advances in both the technological and algorithmic frontier have led to the development of the field of big data analytics. Big data analytics, i.e., methods and applications designed specifically to operate with vast data sets, have become widely accepted as general-purpose tools that can be applied to any domain.

As such, we have seen the same, or very similar, big data analytics tools applied to fields such as social media, economics, biomedicine, smart cities, and so on. The caveat here is that the meaning of the data is not being considered in the process, such as in the case of deep learning, even if some data structures, such as word embeddings, do reflect structures of meaning.

Cultural heritage, on the other hand, is a domain that produces vast amounts of data but also where the meaning of the data is crucially important in its handling; particularly to the extent that it refers to people’s opinions, perceptions, and interpretations of their past and their present, or to people’s feelings, preferences, and attitudes.

In this Special Issue, we focus 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 to the domain of cultural heritage. The aim is to gather solutions, but also lessons learnt, methodologies, and good practices, that researchers and practitioners can use as a basis for their own work in the domain.

Relevant topics include any aspect of big data analytics, as long as it is applied or aimed at the cultural heritage domain. Indicative topics include (but are not restricted to) the following keywords.

Dr. Manolis Wallace
Dr. Vassilis Poulopoulos
Dr. Angeliki Antoniou
Dr. Martín López-Nores
Guest Editors

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Keywords

  • data analytics
  • big data visualization
  • social media analytics
  • pattern detection in archives
  • handling of heterogeneous cultural resources
  • integration with linked data resources
  • analytics on sensor-generated and person-generated data
  • named entity recognition in textual and non-textual sources
  • identification of semantic relations
  • sentiment analysis
  • visitor type classification
  • user/visitor profiling
  • adaptation and personalization of cultural heritage experiences
  • context awareness in cultural heritage data
  • big data analytics underpinning (semi-)automated content generation (e.g., interactive storytelling)
  • big data analytics and computational creativity
  • gamification
  • trajectories in the physical space
  • ethical concerns
  • case studies

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Published Papers (3 papers)

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Research

13 pages, 9470 KiB  
Article
The Distribution and Accessibility of Elements of Tourism in Historic and Cultural Cities
by Wei-Ling Hsu, Yi-Jheng Chang, Lin Mou, Juan-Wen Huang and Hsin-Lung Liu
Big Data Cogn. Comput. 2024, 8(3), 29; https://doi.org/10.3390/bdcc8030029 - 11 Mar 2024
Cited by 1 | Viewed by 2298
Abstract
Historic urban areas are the foundations of urban development. Due to rapid urbanization, the sustainable development of historic urban areas has become challenging for many cities. Elements of tourism and tourism service facilities play an important role in the sustainable development of historic [...] Read more.
Historic urban areas are the foundations of urban development. Due to rapid urbanization, the sustainable development of historic urban areas has become challenging for many cities. Elements of tourism and tourism service facilities play an important role in the sustainable development of historic areas. This study analyzed policies related to tourism in Panguifang and Meixian districts in Meizhou, Guangdong, China. Kernel density estimation was used to study the clustering characteristics of tourism elements through point of interest (POI) data, while space syntax was used to study the accessibility of roads. In addition, the Pearson correlation coefficient and regression were used to analyze the correlation between the elements and accessibility. The results show the following: (1) the overall number of tourism elements was high on the western side of the districts and low on the eastern one, and the elements were predominantly distributed along the main transportation arteries; (2) according to the integration degree and depth value, the western side was easier to access than the eastern one; and (3) the depth value of the area negatively correlated with kernel density, while the degree of integration positively correlated with it. Based on the results, the study put forward measures for optimizing the elements of tourism in Meizhou’s historic urban area to improve cultural tourism and emphasize the importance of the elements. Full article
(This article belongs to the Special Issue Big Data Analytics for Cultural Heritage 2nd Edition)
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23 pages, 4549 KiB  
Article
An Ontology Development Methodology Based on Ontology-Driven Conceptual Modeling and Natural Language Processing: Tourism Case Study
by Shaimaa Haridy, Rasha M. Ismail, Nagwa Badr and Mohamed Hashem
Big Data Cogn. Comput. 2023, 7(2), 101; https://doi.org/10.3390/bdcc7020101 - 21 May 2023
Cited by 3 | Viewed by 4026
Abstract
Ontologies provide a powerful method for representing, reusing, and sharing domain knowledge. They are extensively used in a wide range of disciplines, including artificial intelligence, knowledge engineering, biomedical informatics, and many more. For several reasons, developing domain ontologies is a challenging task. One [...] Read more.
Ontologies provide a powerful method for representing, reusing, and sharing domain knowledge. They are extensively used in a wide range of disciplines, including artificial intelligence, knowledge engineering, biomedical informatics, and many more. For several reasons, developing domain ontologies is a challenging task. One of these reasons is that it is a complicated and time-consuming process. Multiple ontology development methodologies have already been proposed. However, there is room for improvement in terms of covering more activities during development (such as enrichment) and enhancing others (such as conceptualization). In this research, an enhanced ontology development methodology (ON-ODM) is proposed. Ontology-driven conceptual modeling (ODCM) and natural language processing (NLP) serve as the foundation of the proposed methodology. ODCM is defined as the utilization of ontological ideas from various areas to build engineering artifacts that improve conceptual modeling. NLP refers to the scientific discipline that employs computer techniques to analyze human language. The proposed ON-ODM is applied to build a tourism ontology that will be beneficial for a variety of applications, including e-tourism. The produced ontology is evaluated based on competency questions (CQs) and quality metrics. It is verified that the ontology answers SPARQL queries covering all CQ groups specified by domain experts. Quality metrics are used to compare the produced ontology with four existing tourism ontologies. For instance, according to the metrics related to conciseness, the produced ontology received a first place ranking when compared to the others, whereas it received a second place ranking regarding understandability. These results show that utilizing ODCM and NLP could facilitate and improve the development process, respectively. Full article
(This article belongs to the Special Issue Big Data Analytics for Cultural Heritage 2nd Edition)
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19 pages, 11218 KiB  
Article
Promoting Agritourism in Poland with Ready-Made Digital Components and Rustic Cyberfolklore
by Karol Król and Dariusz Zdonek
Big Data Cogn. Comput. 2023, 7(1), 23; https://doi.org/10.3390/bdcc7010023 - 28 Jan 2023
Cited by 3 | Viewed by 2560
Abstract
Online content can have unique cultural value. It is certainly the case for digital representations of folklore found on websites related to rural tourism, including agritourism. It is true for both archaic websites, copies of which are found in digital archives, and modern [...] Read more.
Online content can have unique cultural value. It is certainly the case for digital representations of folklore found on websites related to rural tourism, including agritourism. It is true for both archaic websites, copies of which are found in digital archives, and modern websites. The purpose of this paper is to assess the frequency of ready-made digital components and rustic folklore on agritourism farms’ websites. The exploration and comparative analysis involved 866 websites from two independent sets: (1) archaic websites, copies of which are available in the Internet Archive and (2) currently operational websites published in the Polish ccTLD (country code top-level domain). We employed HTML code exploration to verify the websites’ development technique and their selected characteristics, including content management systems (CMSs) and responsiveness. In the set of the ccTLD websites, we recorded such design attributes as the type of graphic layout, hero image, and parallax scrolling. The research demonstrated that ready-made folklore graphics were relatively rare among the investigated websites. Elements of rustic cyberfolklore were found only on 17 archaic websites (approx. 4%) and 52 ccTLD websites (approx. 12%). They were most often Kashubian patterns. The research suggests that rustic cyberfolklore is most often found on websites of agritourism farms in areas where local communities and ethnic groups are particularly active and strongly identify with regional traditions. Full article
(This article belongs to the Special Issue Big Data Analytics for Cultural Heritage 2nd Edition)
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