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Big Data Analytics for Search Engine Optimization

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Department of Archival, Library and Information Studies, Lab of Information Management, University of West Attica, Ag. Spyridonos, 12243 Egaleo, Greece
2
School of Applied Economics and Social Sciences, Agricultural University of Athens, Iera Odos 75, 11855 Athens, Greece
*
Author to whom correspondence should be addressed.
Big Data Cogn. Comput. 2020, 4(2), 5; https://doi.org/10.3390/bdcc4020005
Received: 8 March 2020 / Revised: 28 March 2020 / Accepted: 30 March 2020 / Published: 2 April 2020
(This article belongs to the Special Issue Big Data Analytics for Cultural Heritage)
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. View Full-Text
Keywords: cultural analytics; cultural data; search engine optimization; SEO strategy; SEO factors; big data; websites visibility; predictive modeling; website security; website load speed; user behavior cultural analytics; cultural data; search engine optimization; SEO strategy; SEO factors; big data; websites visibility; predictive modeling; website security; website load speed; user behavior
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MDPI and ACS Style

Drivas, I.C.; Sakas, D.P.; Giannakopoulos, G.A.; Kyriaki-Manessi, D. Big Data Analytics for Search Engine Optimization. Big Data Cogn. Comput. 2020, 4, 5. https://doi.org/10.3390/bdcc4020005

AMA Style

Drivas IC, Sakas DP, Giannakopoulos GA, Kyriaki-Manessi D. Big Data Analytics for Search Engine Optimization. Big Data and Cognitive Computing. 2020; 4(2):5. https://doi.org/10.3390/bdcc4020005

Chicago/Turabian Style

Drivas, Ioannis C.; Sakas, Damianos P.; Giannakopoulos, Georgios A.; Kyriaki-Manessi, Daphne. 2020. "Big Data Analytics for Search Engine Optimization" Big Data Cogn. Comput. 4, no. 2: 5. https://doi.org/10.3390/bdcc4020005

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