NoCS2: Topic-Based Clustering of Big Data Text Corpus in the Cloud

Zobaed, SM; Haque, Md. Enamul; Kaiser, Shahidullah; Hussain, Razin Farhan

doi:10.1109/iccitechn.2018.8631951

Cited by 5 publications

(2 citation statements)

References 23 publications

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“…The cluster analysis results were interpreted using the Myer-Briggs theory and Kolb’s learning styles theory (Altan, 2018) based on the cluster theory. The final Step 4 of the analysis was the identification and description of the learning styles in each cluster and the generation of word clouds for each cluster (Zobaed et al , 2018; Crișan-Mitra et al , 2020) (Appendix 2).…”

Section: Methodsmentioning

confidence: 99%

Community of practice: converting IT graduate students into specialists via professional knowledge sharing

Stanca

Dabija

Păcurar

2021

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Purpose The paper aims to highlight how an applied learning framework or “community of practice” (CoP) combined with a traditional theoretical course of study enables the identification of teaching-learning processes which facilitate knowledge transfer from practitioners to graduate information technology (IT) students for quicker integration in the labour market. Design/methodology/approach CoPs are identified based on cluster analysis according to Kolb’s Learning Style Inventory (1984), with data obtained through a survey. Empirical research is applied to the CoP developed within a non-formal learning framework, principal new actors being IT specialists linked to graduate IT students and teachers on a traditional university course. Graduate IT students can gain knowledge of the ideal employee and the social and emotional skills needed to integrate with the IT labour market. Findings The K-Means algorithm helps to identify clusters of graduate IT students displaying necessary knowledge acceptance behaviour to convert them into specialists. The results of the cluster analysis show different learning styles of the labour force, providing an overview of candidate selection methods and the knowledge, skills and attitudes expected by users. Research limitations/implications Although the research adds value to the existing literature on learning styles and the knowledge and core skills needed by IT specialists, it was limited to an emerging market. Originality/value The study provides a preliminary overview of graduate IT students’ attitudes from an emerging market to the re-engineering of academic learning contexts to facilitate professional knowledge transfer, converting them into IT practitioners and integrating them in the labour market of an emerging economy.

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Section: Methodsmentioning

confidence: 99%

Community of practice: converting IT graduate students into specialists via professional knowledge sharing

Stanca

Dabija

Păcurar

2021

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“…Although numerous data clustering methods exist, they are not appropriate for encrypted big data because of the following challenges: First, in the encrypted domain the original data is not available. Therefore, prior works (e.g., [12], [36], [37]) suggest making use of statistical characteristics as the clustering metric for encrypted data. For instance, in S3BD [21], which is a search system for encrypted big data, keywords' co-occurrences in a document set is used to cluster keywords.…”

Section: Introductionmentioning

confidence: 99%

ClustCrypt: Privacy-Preserving Clustering of Unstructured Big Data in the Cloud

Zobaed

Ahmad

Gottumukkala

et al. 2019

2019 IEEE 21st International Conference on High Performance Computing and Communications; IEEE 17th International Conference On

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we present an approach named ClustCrypt for efficient topic-based clustering of encrypted unstructured big data in the cloud. ClustCrypt dynamically estimates the optimal number of clusters based on the statistical characteristics of encrypted data. It also provides clustering approach for encrypted data. We deploy ClustCrypt within the context of a secure cloud-based semantic search system (S3BD). Experimental results obtained from evaluating ClustCrypt on three datasets demonstrate on average 60% improvement on clusters' coherency. ClustCrypt also decreases the search-time overhead by up to 78% and increases the accuracy of search results by up to 35%.

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