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Big Data Analytics for Cultural Heritage

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A special issue of Big Data and Cognitive Computing (ISSN 2504-2289).

Deadline for manuscript submissions: closed (30 September 2022) | Viewed by 85561

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Special Issue Editors

Dr. Manolis Wallace

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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

Dr. Vassilis Poulopoulos

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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
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Dr. Angeliki Antoniou

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Guest Editor

Department of Informatics and Telecommunications, University of Peloponnese, Terma Karaiskaki, 22100 Tripolis, Greece
Interests: technologies and applications for cultural heritage; educational games (formal, non-formal and informal learning); augmented reality; user profiling; personalization; group adaptation; social networks; crowdsourcing
Special Issues, Collections and Topics in MDPI journals

Dr. Martín López-Nores

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
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Special Issue Information

Dear Colleagues,

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.

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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Big Data and Cognitive Computing is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

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:

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 (11 papers)

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Editorial

Jump to: Research

2 pages, 198 KiB

Open AccessEditorial

An Overview of Big Data Analytics for Cultural Heritage

by Manolis Wallace, Vassilis Poulopoulos, Angeliki Antoniou and Martín López-Nores

Big Data Cogn. Comput. 2023, 7(1), 14; https://doi.org/10.3390/bdcc7010014 - 13 Jan 2023

Cited by 3 | Viewed by 3073

Abstract

Cultural heritage is a domain that produces vast amounts of data, but it is also where the meaning of the data is crucially important, particularly to the extent that it refers to people’s opinions, perceptions, and interpretations of their past and their present, [...] Read more.

(This article belongs to the Special Issue Big Data Analytics for Cultural Heritage)

Research

Jump to: Editorial

11 pages, 1092 KiB

Open AccessArticle

ACUX Recommender: A Mobile Recommendation System for Multi-Profile Cultural Visitors Based on Visiting Preferences Classification

by Markos Konstantakis, Yannis Christodoulou, John Aliprantis and George Caridakis

Big Data Cogn. Comput. 2022, 6(4), 144; https://doi.org/10.3390/bdcc6040144 - 28 Nov 2022

Cited by 13 | Viewed by 3171

Abstract

In recent years, Recommendation Systems (RSs) have gained popularity in different scientific fields through the creation of (mostly mobile) applications that deliver personalized services. A mobile recommendation system (MRS) that classifies in situ visitors according to different visiting profiles could act as a mediator between their visiting preferences and cultural content. Drawing on the above, in this paper, we propose ACUX Recommender (ACUX-R), an MRS, for recommending personalized cultural POIs to visitors based on their visiting preferences. ACUX-R experimentally employs the ACUX typology for assigning profiles to cultural visitors. ACUX-R was evaluated through a user study and a questionnaire. The evaluation conducted showed that the proposed ACUX-R satisfies cultural visitors and is capable of capturing their nonverbal visiting preferences and needs. Full article

(This article belongs to the Special Issue Big Data Analytics for Cultural Heritage)

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19 pages, 491 KiB

Open AccessEditor’s ChoiceArticle

Digital Technologies and the Role of Data in Cultural Heritage: The Past, the Present, and the Future

by Vassilis Poulopoulos and Manolis Wallace

Big Data Cogn. Comput. 2022, 6(3), 73; https://doi.org/10.3390/bdcc6030073 - 4 Jul 2022

Cited by 30 | Viewed by 13379

Abstract

Is culture considered to be our past, our roots, ancient ruins, or an old piece of art? Culture is all the factors that define who we are, how we act and interact in our world, in our daily activities, in our personal and public relations, in our life. Culture is all the things we are not obliged to do. However, today, we live in a mixed environment, an environment that is a combination of “offline” and the online, digital world. In this mixed environment, it is technology that defines our behaviour, technology that unites people in a large world, that finally, defines a status of “monoculture”. In this article, we examine the role of technology, and especially big data, in relation to the culture. We present the advances that led to paradigm shifts in the research area of cultural informatics, and forecast the future of culture as will be defined in this mixed world. Full article

(This article belongs to the Special Issue Big Data Analytics for Cultural Heritage)

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17 pages, 1452 KiB

Open AccessArticle

Networks and Stories. Analyzing the Transmission of the Feminist Intangible Cultural Heritage on Twitter

by Jordi Morales-i-Gras, Julen Orbegozo-Terradillos, Ainara Larrondo-Ureta and Simón Peña-Fernández

Big Data Cogn. Comput. 2021, 5(4), 69; https://doi.org/10.3390/bdcc5040069 - 24 Nov 2021

Cited by 7 | Viewed by 5199

Abstract

Internet social media is a key space in which the memorial resources of social movements, including the stories and knowledge of previous generations, are organised, disseminated, and reinterpreted. This is especially important for movements such as feminism, which places great emphasis on the transmission of an intangible cultural legacy between its different generations or waves, which are conformed through these cultural transmissions. In this sense, several authors have highlighted the importance of social media and hashtivism in shaping the fourth wave of feminism that has been taking place in recent years (e.g., #metoo). The aim of this article is to present to the scientific community a hybrid methodological proposal for the network and content analysis of audiences and their interactions on Twitter: we will do so by describing and evaluating the results of different research we have carried out in the field of feminist hashtivism. Structural analysis methods such as social network analysis have demonstrated their capacity to be applied to the analysis of social media interactions as a mixed methodology, that is, both quantitative and qualitative. This article shows the potential of a specific methodological process that combines inductive and inferential reasoning with hypothetico-deductive approaches. By applying the methodology developed in the case studies included in the article, it is shown that these two modes of reasoning work best when they are used together. Full article

(This article belongs to the Special Issue Big Data Analytics for Cultural Heritage)

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23 pages, 431 KiB

Open AccessArticle

Annotation-Assisted Clustering of Player Profiles in Cultural Games: A Case for Tensor Analytics in Julia

by Georgios Drakopoulos, Yorghos Voutos and Phivos Mylonas

Big Data Cogn. Comput. 2020, 4(4), 39; https://doi.org/10.3390/bdcc4040039 - 12 Dec 2020

Cited by 10 | Viewed by 6031

Abstract

Computer games play an increasingly important role in cultural heritage preservation. They keep tradition alive in the digital domain, reflect public perception about historical events, and make history, and even legends, vivid, through means such as advanced storytelling and alternative timelines. In this context, understanding the respective underlying player base is a major success factor as different game elements elicit various emotional responses across players. To this end, player profiles are often built from a combination of low- and high-level attributes. The former pertain to ordinary activity, such as collecting points or badges, whereas the latter to the outcome of strategic decisions, such as participation in in-game events such as tournaments and auctions. When available, annotations about in-game items or player activity supplement these profiles. In this article, we describe how such annotations may be integrated into different player profile clustering schemes derived from a template Simon–Ando iterative process. As a concrete example, the proposed methodology was applied to a custom benchmark dataset comprising the player base of a cultural game. The findings are interpreted in the light of Bartle taxonomy, one of the most prominent player categorization. Moreover, the clustering quality is based on intra-cluster distance and cluster compactness. Based on these results, recommendations in an affective context for maximizing engagement are proposed for the particular game player base composition. Full article

(This article belongs to the Special Issue Big Data Analytics for Cultural Heritage)

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20 pages, 2980 KiB

Open AccessEditor’s ChoiceArticle

Using Big and Open Data to Generate Content for an Educational Game to Increase Student Performance and Interest

by Irene Vargianniti and Kostas Karpouzis

Big Data Cogn. Comput. 2020, 4(4), 30; https://doi.org/10.3390/bdcc4040030 - 22 Oct 2020

Cited by 12 | Viewed by 5617

Abstract

The goal of this paper is to utilize available big and open data sets to create content for a board and a digital game and implement an educational environment to improve students’ familiarity with concepts and relations in the data and, in the process, academic performance and engagement. To this end, we used Wikipedia data to generate content for a Monopoly clone called Geopoly and designed a game-based learning experiment. Our research examines whether this game had any impact on the students’ performance, which is related to identifying implied ranking and grouping mechanisms in the game, whether performance is correlated with interest and whether performance differs across genders. Student performance and knowledge about the relationships contained in the data improved significantly after playing the game, while the positive correlation between student interest and performance illustrated the relationship between them. This was also verified by a digital version of the game, evaluated by the students during the COVID-19 pandemic; initial results revealed that students found the game more attractive and rewarding than a traditional geography lesson. Full article

(This article belongs to the Special Issue Big Data Analytics for Cultural Heritage)

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14 pages, 4711 KiB

Open AccessArticle

Data-Assisted Persona Construction Using Social Media Data

by Dimitris Spiliotopoulos, Dionisis Margaris and Costas Vassilakis

Big Data Cogn. Comput. 2020, 4(3), 21; https://doi.org/10.3390/bdcc4030021 - 19 Aug 2020

Cited by 29 | Viewed by 7173

Abstract

User experience design and subsequent usability evaluation can benefit from knowledge about user interaction, types, deployment settings and situations. Most of the time, the user type and generic requirements are given or can be obtained and used to model interaction during the design phase. The deployment settings and situations can be collected through the needfinding phase, either via user feedback or via the automatic analysis of existing data. Personas may be defined using the aforementioned information through user research analysis or data analysis. This work utilizes an approach to activate an accurate persona definition early in the design cycle, using topic detection to semantically enrich the data that are used to derive the persona details. This work uses Twitter data from a music event to extract information that can be used to assist persona creation. A user study in persona construction compares the topic modelling metadata to a traditional user collected data analysis for persona construction. The results show that the topic information-driven constructed personas are perceived as having better clarity, completeness and credibility. Additionally, the human users feel more attracted and similar to such personas. This work may be used to model personas and recommend suitable ones to designers of other products, such as advertisers, game designers and moviegoers. Full article

(This article belongs to the Special Issue Big Data Analytics for Cultural Heritage)

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19 pages, 450 KiB

Open AccessArticle

A Personalized Heritage-Oriented Recommender System Based on Extended Cultural Tourist Typologies

by Markos Konstantakis, Georgios Alexandridis and George Caridakis

Big Data Cogn. Comput. 2020, 4(2), 12; https://doi.org/10.3390/bdcc4020012 - 4 Jun 2020

Cited by 18 | Viewed by 7609

Abstract

Recent developments in digital technologies regarding the cultural heritage domain have driven technological trends in comfortable and convenient traveling, by offering interactive and personalized user experiences. The emergence of big data analytics, recommendation systems and personalization techniques have created a smart research field, augmenting cultural heritage visitor’s experience. In this work, a novel, hybrid recommender system for cultural places is proposed, that combines user preference with cultural tourist typologies. Starting with the McKercher typology as a user classification research base, which extracts five categories of heritage tourists out of two variables (cultural centrality and depth of user experience) and using a questionnaire, an enriched cultural tourist typology is developed, where three additional variables governing cultural visitor types are also proposed (frequency of visits, visiting knowledge and duration of the visit). The extracted categories per user are fused in a robust collaborative filtering, matrix factorization-based recommendation algorithm as extra user features. The obtained results on reference data collected from eight cities exhibit an improvement in system performance, thereby indicating the robustness of the presented approach. Full article

(This article belongs to the Special Issue Big Data Analytics for Cultural Heritage)

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28 pages, 10611 KiB

Open AccessEditor’s ChoiceArticle

Hydria: An Online Data Lake for Multi-Faceted Analytics in the Cultural Heritage Domain

by Kimon Deligiannis, Paraskevi Raftopoulou, Christos Tryfonopoulos, Nikos Platis and Costas Vassilakis

Big Data Cogn. Comput. 2020, 4(2), 7; https://doi.org/10.3390/bdcc4020007 - 23 Apr 2020

Cited by 15 | Viewed by 6521

Abstract

Advancements in cultural informatics have significantly influenced the way we perceive, analyze, communicate and understand culture. New data sources, such as social media, digitized cultural content, and Internet of Things (IoT) devices, have allowed us to enrich and customize the cultural experience, but at the same time have created an avalanche of new data that needs to be stored and appropriately managed in order to be of value. Although data management plays a central role in driving forward the cultural heritage domain, the solutions applied so far are fragmented, physically distributed, require specialized IT knowledge to deploy, and entail significant IT experience to operate even for trivial tasks. In this work, we present Hydria, an online data lake that allows users without any IT background to harvest, store, organize, analyze and share heterogeneous, multi-faceted cultural heritage data. Hydria provides a zero-administration, zero-cost, integrated framework that enables researchers, museum curators and other stakeholders within the cultural heritage domain to easily (i) deploy data acquisition services (like social media scrapers, focused web crawlers, dataset imports, questionnaire forms), (ii) design and manage versatile customizable data stores, (iii) share whole datasets or horizontal/vertical data shards with other stakeholders, (iv) search, filter and analyze data via an expressive yet simple-to-use graphical query engine and visualization tools, and (v) perform user management and access control operations on the stored data. To the best of our knowledge, this is the first solution in the literature that focuses on collecting, managing, analyzing, and sharing diverse, multi-faceted data in the cultural heritage domain and targets users without an IT background. Full article

(This article belongs to the Special Issue Big Data Analytics for Cultural Heritage)

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22 pages, 4394 KiB

Open AccessArticle

A Semantic Mixed Reality Framework for Shared Cultural Experiences Ecosystems

by Costas Vassilakis, Konstantinos Kotis, Dimitris Spiliotopoulos, Dionisis Margaris, Vlasios Kasapakis, Christos-Nikolaos Anagnostopoulos, Georgios Santipantakis, George A. Vouros, Theodore Kotsilieris, Volha Petukhova, Andrei Malchanau, Ioanna Lykourentzou, Kaj Michael Helin, Artem Revenko, Nenad Gligoric and Boris Pokric

Big Data Cogn. Comput. 2020, 4(2), 6; https://doi.org/10.3390/bdcc4020006 - 20 Apr 2020

Cited by 6 | Viewed by 6552

Abstract

This paper presents SemMR, a semantic framework for modelling interactions between human and non-human entities and managing reusable and optimized cultural experiences, towards a shared cultural experience ecosystem that might seamlessly accommodate mixed reality experiences. The SemMR framework synthesizes and integrates interaction data into semantically rich reusable structures and facilitates the interaction between different types of entities in a symbiotic way, within a large, virtual, and fully experiential open world, promoting experience sharing at the user level, as well as data/application interoperability and low-effort implementation at the software engineering level. The proposed semantic framework introduces methods for low-effort implementation and the deployment of open and reusable cultural content, applications, and tools, around the concept of cultural experience as a semantic trajectory or simply, experience as a trajectory (eX-trajectory). The methods facilitate the collection and analysis of data regarding the behaviour of users and their interaction with other users and the environment, towards optimizing eX-trajectories via reconfiguration. The SemMR framework supports the synthesis, enhancement, and recommendation of highly complex reconfigurable eX-trajectories, while using semantically integrated disparate and heterogeneous related data. Overall, this work aims to semantically manage interactions and experiences through the eX-trajectory concept, towards delivering enriched cultural experiences. Full article

(This article belongs to the Special Issue Big Data Analytics for Cultural Heritage)

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22 pages, 3322 KiB

Open AccessArticle

Big Data Analytics for Search Engine Optimization

by Ioannis C. Drivas, Damianos P. Sakas, Georgios A. Giannakopoulos and Daphne Kyriaki-Manessi

Big Data Cogn. Comput. 2020, 4(2), 5; https://doi.org/10.3390/bdcc4020005 - 2 Apr 2020

Cited by 26 | Viewed by 15689

Abstract

In the Big Data era, search engine optimization deals with the encapsulation of datasets that are related to website performance in terms of architecture, content curation, and user behavior, with the purpose to convert them into actionable insights and improve visibility and findability on the Web. In this respect, big data analytics expands the opportunities for developing new methodological frameworks that are composed of valid, reliable, and consistent analytics that are practically useful to develop well-informed strategies for organic traffic optimization. In this paper, a novel methodology is implemented in order to increase organic search engine visits based on the impact of multiple SEO factors. In order to achieve this purpose, the authors examined 171 cultural heritage websites and their retrieved data analytics about their performance and user experience inside them. Massive amounts of Web-based collections are included and presented by cultural heritage organizations through their websites. Subsequently, users interact with these collections, producing behavioral analytics in a variety of different data types that come from multiple devices, with high velocity, in large volumes. Nevertheless, prior research efforts indicate that these massive cultural collections are difficult to browse while expressing low visibility and findability in the semantic Web era. Against this backdrop, this paper proposes the computational development of a search engine optimization (SEO) strategy that utilizes the generated big cultural data analytics and improves the visibility of cultural heritage websites. One step further, the statistical results of the study are integrated into a predictive model that is composed of two stages. First, a fuzzy cognitive mapping process is generated as an aggregated macro-level descriptive model. Secondly, a micro-level data-driven agent-based model follows up. The purpose of the model is to predict the most effective combinations of factors that achieve enhanced visibility and organic traffic on cultural heritage organizations’ websites. To this end, the study contributes to the knowledge expansion of researchers and practitioners in the big cultural analytics sector with the purpose to implement potential strategies for greater visibility and findability of cultural collections on the Web. Full article

(This article belongs to the Special Issue Big Data Analytics for Cultural Heritage)

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