TY - GEN
T1 - A proposed contextual model for big data analysis using advanced analytics
AU - Ramannavar, Manjula
AU - Sidnal, Nandini S.
N1 - Publisher Copyright:
© 2018, Springer Nature Singapore Pte Ltd.
PY - 2018
Y1 - 2018
N2 - Big Data has numerous issues related to its primary defining characteristics of the three V’s: Variety, Volume and Velocity. A greater segment of Big Data is attributed to semi-structured or unstructured text that emanates from social interactions on the web, emails, tweets, blogs, etc. Conventional approaches are overwhelmed by the data deluge and fall short to perform. These challenges consequently create scope for research in developing models to analyze data and extract actionable insights to realize the fourth V, i.e., Value. The purpose of this paper is to propose a contextual model for Resume Analytics that utilizes Semantic technologies and Analytic (Descriptive, Predictive and Prescriptive) procedures to find a befitting match between a job and candidate(s). The related work, issues and challenges and design requirements are presented along with a discussion of the analytical framework for the opted use case.
AB - Big Data has numerous issues related to its primary defining characteristics of the three V’s: Variety, Volume and Velocity. A greater segment of Big Data is attributed to semi-structured or unstructured text that emanates from social interactions on the web, emails, tweets, blogs, etc. Conventional approaches are overwhelmed by the data deluge and fall short to perform. These challenges consequently create scope for research in developing models to analyze data and extract actionable insights to realize the fourth V, i.e., Value. The purpose of this paper is to propose a contextual model for Resume Analytics that utilizes Semantic technologies and Analytic (Descriptive, Predictive and Prescriptive) procedures to find a befitting match between a job and candidate(s). The related work, issues and challenges and design requirements are presented along with a discussion of the analytical framework for the opted use case.
KW - Big data
KW - Descriptive analytics
KW - Predictive analytics
KW - Prescriptive analytics
KW - Semantic technologies
UR - http://www.scopus.com/inward/record.url?scp=85031419478&partnerID=8YFLogxK
U2 - 10.1007/978-981-10-6620-7_32
DO - 10.1007/978-981-10-6620-7_32
M3 - Conference contribution
AN - SCOPUS:85031419478
SN - 9789811066191
T3 - Advances in Intelligent Systems and Computing
SP - 329
EP - 339
BT - Big Data Analytics - Proceedings of CSI 2015
A2 - Aggarwal, V.B.
A2 - Bhatnagar, Vasudha
A2 - Mishra, Durgesh Kumar
PB - Springer Verlag
T2 - 50th Annual Convention of Computer Society of India : Big Data Analytics, CSI 2015
Y2 - 2 December 2015 through 5 December 2015
ER -