Designing a Framework for Communal Software: Based on the Assessment Using Relation Modelling

Predicting and Identifying behavioral analysis in social media using big data analytics is exceptionally repetitive. Since data in motion are difficult to capture and process with existing innovation. In spite of the fact that there are numerous frameworks that have executed for user behavior analys...

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Vydáno v:International journal of parallel programming Ročník 48; číslo 2; s. 329 - 343
Hlavní autoři: BalaAnand, M, Karthikeyan, N, Karthik, S
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer Nature B.V 01.04.2020
Témata:
Analytics
Assessments
Big Data
Data management
Digital media
Innovations
Mathematical models
Predictions
Semantic web
Semantics
Social networks
Software
User behavior
ISSN:0885-7458, 1573-7640
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Shrnutí:Predicting and Identifying behavioral analysis in social media using big data analytics is exceptionally repetitive. Since data in motion are difficult to capture and process with existing innovation. In spite of the fact that there are numerous frameworks that have executed for user behavior analysis, it’s as yet an upcoming and unexplored market that has more prominent potential for better advancements. So with the help of Hadoop framework, a new approach of designing a communal framework is proposed and it’s used for predicting and identifying user’s behavioral analysis in a community. The proposed work can be applied in the community-based environment where the prediction and identification of user behavior analysis has to be made with the semantic web approach. In addition to that, a suitable model for the communally accountable software objects where these objects would observe online communal network information is designed. Further, these objects in online communal network, assess them from the viewpoint of communally accountable performance based on the relation modeling conceptions. The location of communally accountable mediators is grounded in diverse methods. Diverse illustrations are grabbed from the analysis with microblog assessments, community semantic web, higher relations for communal web and social network sourced big information assessments. In the proposed framework, the analysis is based on the assessment/observation of the communally accountable performance of the communal media big data and design of higher-level relations as the model for the above mentioned assessments.
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ISSN:0885-7458
1573-7640
DOI:10.1007/s10766-018-0598-2