A Survey on Big Data Applicability in Prediction Using Absence Information for Workforce Management
R. Varalakshmi1, R.S. Dhivya2
1R. Varalakshmi, Professor, Department of Computer Applications, Vels Institue of Science, Technology & Advanced Studies, Chennai (Tamil Nadu), India.
2R.S. Dhivya, Assistant Professor, Department of Computer Applications, J.H.A Agarsen College. Research Scholar, VISTAS, Chennai (Tamil Nadu), India.
Manuscript received on 07 February 2019 | Revised Manuscript received on 29 March 2019 | Manuscript Published on 28 April 2019 | PP: 97-100 | Volume-7 Issue-5C February 2019 | Retrieval Number: E10240275C19/19©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Prediction is a method of finding new insights from large data sets, it’s a reliable way to use big data for more accuracy. The overall goal of the process is to predict useful outcomes from dataset and transform into a meaningful insight for decision. Absence data has thousands of leave type information; by analysing the data, new insights can be predicted, since the data is growing exponentially, traditional method of prediction will not be effective. In this paper we discuss about prediction techniques and a survey of works done in the field of absenteeism is performed, This paper concentrates on machine learning algorithms and also presents the applicability of absence data in big data for workforce planning.
Keywords: Big Data, Data Science, Predictive Analytics, Workforce Planning, Random Forest.
Scope of the Article: Big Data Application Quality Services