Cloud Computing-Based Socially Important Locations Discovery on Social Media Big Datasets

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dc.authoridDokuz, Ahmet Sakir/0000-0002-1775-0954
dc.contributor.authorDokuz, Ahmet Sakir
dc.contributor.authorCelik, Mete
dc.date.accessioned2024-11-07T13:31:34Z
dc.date.available2024-11-07T13:31:34Z
dc.date.issued2020
dc.departmentNiğde Ömer Halisdemir Üniversitesi
dc.description.abstractSocially important locations are places which are frequently visited by social media users in their social media lifetime. Discovering socially important locations provides valuable information, such as which locations are frequently visited by a social media user, which locations are common for a social media user group, and which locations are socially important for a group of urban area residents. However, discovering socially important locations is challenging due to huge volume, velocity, and variety of social media datasets, inefficiency of current interest measures and algorithms on social media big datasets, and the need of massive spatial and temporal calculations for spatial social media analyses. In contrast, cloud computing provides infrastructure and platforms to scale compute-intensive jobs. In the literature, limited number of studies related to socially important locations discovery takes into account cloud computing systetns to scale increasing dataset size and to handle massive calculations. This study proposes a cloud-based socially important locations discovery algorithm of Cloud SS-ILM to handle volume and variety of social media big datasets. In particular, in this study, we used Apache Hadoop framework and Hadoop MapReduce programming model to scale dataset size and handle massive spatial and temporal calculations. The performance evaluation of the proposed algorithm is conducted on a cloud computing environment using Turkey Twitter social media big dataset. The experimental results show that using cloud computing systems for socially important locations discovery provide much faster discovery of results than classical algorithms. Moreover, the results show that it is necessary to use cloud computing systems for analyzing social media big datasets that could not be handled with traditional stand-alone computer systems. The proposed Cloud SS-ILM algorithm could be applied on many application areas, such as targeted advertisement of businesses, social media utilization of cities for city planners and local governments, and handling emergency situations.
dc.description.sponsorshipResearch Fund of Erciyes University [FDK-2017-7233]; Microsoft Azure for Research Award Program [0518113]
dc.description.sponsorshipThis research was supported by Research Fund of Erciyes University (Project Number: FDK-2017-7233) and Microsoft Azure for Research Award Program (CRM Number: 0518113). This study is a part of PhD thesis of Dr. Ahmet Sakir Dokuz72 under supervision of Dr. Mete Celik.
dc.identifier.doi10.1142/S0219622020500091
dc.identifier.endpage497
dc.identifier.issn0219-6220
dc.identifier.issn1793-6845
dc.identifier.issue2
dc.identifier.scopus2-s2.0-85082316887
dc.identifier.scopusqualityQ1
dc.identifier.startpage469
dc.identifier.urihttps://doi.org/10.1142/S0219622020500091
dc.identifier.urihttps://hdl.handle.net/11480/14930
dc.identifier.volume19
dc.identifier.wosWOS:000537569300006
dc.identifier.wosqualityQ3
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherWorld Scientific Publ Co Pte Ltd
dc.relation.ispartofInternational Journal of Information Technology & Decision Making
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241106
dc.subjectSocially important locations discovery
dc.subjectspatial social media mining
dc.subjectcloud computing
dc.subjectHadoop MapReduce
dc.subjectTwitter
dc.titleCloud Computing-Based Socially Important Locations Discovery on Social Media Big Datasets
dc.typeArticle

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