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.
CrossCult is an EU-funded research project aiming to spur a change in the way European citizens appraise History, fostering the re-interpretation of what they may have learnt in the light of cross-border interconnections among pieces of cultural heritage, other citizens' viewpoints and physical venues. Exploiting the expressive power, reasoning and interoperability capabilities of semantic technologies, the CrossCult Knowledge Base models and semantically links desperate pieces of Cultural Heritage information, contributing significantly to the aims of the project. This paper presents the structure, design rationale and development of the CrossCult Knowledge Base, aiming to inform researchers in Digital Heritage about the challenges and opportunities of semantically modelling Cultural Heritage data.
Purpose-This paper aims to present real time user searches in a Greek academic library OPAC (University of Macedonia Library) in relation to user profile. Design/methodology/approach-Using as a test bed a Greek academic library and its OPAC's transaction logs along with a system implanted questionnaire, data were gathered, processed and analyzed using multivariate statistical analysis techniques. Findings-In making a synthesis of the analyzed data, a series of questions related to everyday library work were answered, giving libraries a tool to apply the gained knowledge in order to make decisions regarding their OPAC, their user education programs and their reference services. Research limitations/implications-The present paper focuses on the analysis of those variables that were considered to be the most representative for constructing a user profile. Originality/value-This paper builds upon the techniques of data collection and presents a new tool for analyzing them statistically. Data derived from libraries were processed and analyzed statistically using the classical descriptive statistics. The suggested multivariate statistical method is designed to become a tool for analyzing qualitative data and to be used in a variety of library applications. It is also particularly helpful in analyzing cross-tabular data in the form of numerical frequencies and allows all associations amongst pairs of variables to be analyzed as well as each association between a variable and itself.
The present study examines the impact of austerity measures on the academic community. The Technological Educational Institute of Athens served as our case study. It was selected because it is the second largest higher education institution in Greece, and has students of a diverse socio-economic background. Data were obtained through an analysis of institution financial statements and other documents depicting budgets, human resources, infrastructure, in an attempt to study their impact on the resulting quality of education. In addition, through the use of questionnaires we evaluated the impact of austerity measures and the economic crisis on both academic staff and students in relation to their performance, the quality of education, and their plans for educational and professional trajectories. Finally, and given the pending merging or even closure of some departments, faculties and/or institutions throughout the country, we examined the effects on people's attitudes towards educational and research activities.
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