Abstract
The progress in Cultural and Tourism Informatics has substantially influenced the way people perceive, analyze, communicate and understand culture and cultural tourism. Emerging data sources, including social media, digitized cultural material, and Internet of Things devices, have enabled society to enhance and personalize the cultural experience. However, they have also generated a vast amount of new content, which must be stored and effectively managed to provide valuable assets. While data management is crucial for the advancement of both the Cultural Heritage and Cultural Tourism sectors, current solutions are fragmented, physically dispersed, requiring specialized IT expertise for their implementation, and considerable IT experience to operate, even for basic tasks. At the same time, as cultural and touristic information becomes increasingly accessible through a multitude of heterogeneous sources, such as social networks, official repositories, community platforms and open data in ...
The progress in Cultural and Tourism Informatics has substantially influenced the way people perceive, analyze, communicate and understand culture and cultural tourism. Emerging data sources, including social media, digitized cultural material, and Internet of Things devices, have enabled society to enhance and personalize the cultural experience. However, they have also generated a vast amount of new content, which must be stored and effectively managed to provide valuable assets. While data management is crucial for the advancement of both the Cultural Heritage and Cultural Tourism sectors, current solutions are fragmented, physically dispersed, requiring specialized IT expertise for their implementation, and considerable IT experience to operate, even for basic tasks. At the same time, as cultural and touristic information becomes increasingly accessible through a multitude of heterogeneous sources, such as social networks, official repositories, community platforms and open data initiatives, capturing all the available content can result in highly noisy datasets. For example, although well-known landmarks generally appear across these sources, lesser-known attractions are also recorded, yet with different levels of detail, leading to content that may be incomplete, overlapping, or complementary. To facilitate comprehensive access to this extensive information, it is essential to implant harvesting and consolidation methodologies that can gather, reconcile, and unify distributed content related to the same entities. In this thesis we study the problem of both human-assisted and automated data collection, through digital questionnaire-like and web scraping procedures respectively, in the fields of cultural tourism and transportation. We thoroughly explore various data storage, management and analysis architectures, we examine different levels of user collaboration to increase their engagement in cultural tourism research, while paying special attention to user access control for content security reasons and to user-friendly interfaces to target users with limited or no IT knowledge. In addition to the above, we focus on data resolution approaches to harmonize duplicate records referring to the same entity and consolidate complementary ones, while we utilize Machine Learning models to extract high-value insights from the captured content. Initially, we present Hydria, an online data lake designed for users who lack an IT background, enabling them to harvest, store, organize, analyze, and distribute heterogeneous and complex Cultural Heritage and Tourism data. Hydria offers a zero-administration, zero-cost, integrated framework that allows researchers, museum curators, and other stakeholders in the cultural tourism sector to effortlessly: (i) implement data acquisition services (such as social media scrapers, focused web crawlers, dataset imports, and questionnaire forms), (ii) create and manage adaptable customizable data repositories leveraging traditional databases, (iii) share entire datasets or specific horizontal/vertical data segments with other stakeholders via appropriate publish/subscribe mechanisms, (iv) search, filter, and analyze recorded content using an intuitive yet powerful graphical query engine and visualization tools, and (v) conduct user management and access control tasks on the stored data. Subsequently, we propose EnQuest, an expansion of Hydria equipped with a schema-less, document-oriented NoSQL database. In this architectural design, massive datasets can be ingested without affecting the operation of the data lake. In addition to the features provided by Hydria, EnQuest was developed in a way that allows access to unauthorized users (i.e., those without data lake credentials) for data acquisition and analysis tasks, while its data visualization service was further extended to enable its users to create more sophisticated visualizations by combining multiple data fields simultaneously. The proposed system is entirely open-source, designed upon big data tools and principles for data storage, analytics production, and knowledge sharing, targeting both performance and usability. The results of extensive performance evaluations compared to its predecessor (i.e., Hydria) highlighted EnQuest’s robustness and stability by at least an order of magnitude across all operations, as the workload on both data lake alternatives steadily increased. Finally, we put forward a machine learning-driven framework for harvesting, homogenizing, and augmenting cultural and tourism data across multilingual, diverse sources and apply it to both data lake approaches. Our approach addresses entity resolution, duplicate detection, and complementary content harmonization by combining both pre-recorded geographic information and various toponym string similarity metrics, establishing a basis for an enriched and unified representation of attractions and points of interest. Furthermore, the captured content is enhanced by leveraging Natural Language Processing methods, including automated text translation libraries, named entity recognition language models, and sentiment analysis pipelines, to yield actionable insights, such as cultural tourism trajectories. The framework is designed to facilitate scalable integration pipelines and is suitable for deployment in applications focused on tourism promotion, digital heritage, smart travel services, and transportation. It is worth noting that all aforementioned systems were implemented, deployed and used in real-life scenarios including European and national research projects bringing user feedback and tangible results.
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