Graphing Model of Prediction Data for Occupational Incidents in Chemical And Gas Industries
Ganapathy Subramaniam Balasubramanian1, Ramaprabha Thangamani2
1Ganapathy Subramaniam Balasubramanian*, Research Scholar, PG and Research Department of Computer Science and Applications, Vivekanandha College of Arts and Science, TN, India.
2Dr. T. Ramaprabha, PG and Research Department of Computer Sciences and Applications, Vivekanandha College of Arts and Science, Tiruchengode, TN, India.
Manuscript received on January 18, 2020. | Revised Manuscript received on January 26, 2020. | Manuscript published on February 10, 2020. | PP: 3112-3116 | Volume-9 Issue-4, February 2020. | Retrieval Number: D2208029420/2020©BEIESP | DOI: 10.35940/ijitee.D2208.029420
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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: Constant streaming of data for any instances at such high volumes provides insight in various organizations. Analyzing and identifying the pattern from the huge volumes of data has become difficult with its raw form of data. Visualization of information and visual data mining helps to deal with the flood of information. Constant streaming of data for any instances at such high volumes provides insight in various organizations. Analyzing and identifying the pattern from the huge volumes of data has become difficult with its raw form of data. Visualization of information and visual data mining helps to deal with the flood of information. Visual data representation takes the data and its results to all the stakeholders in a meaningful manner which comes out of the data mining process. Recent developments have brought a large number of information visualization techniques to explore the large data sets which can be converted into useful information and knowledge. Observations and inspection data gathered from chemical and gas industries are being piled up on a daily basis as raw data. Continuous analysis is a new term evolving in the industry which continuously performs on the streaming data to have real-time analysis and prediction on-live. In this paper, usage of the various graphing model as per the respective information obtained from the organization have been discussed and justified. It also describes the value addition in making the decisions by representations through graphs and charts for better understanding. Heatmap, Scattergram and customized Radar plots the analyzed data as in the required format to visualize the prediction done for the occupational incidents in chemical and gas industries. As a result of the graphing model, representation provides a higher level of confidence in the findings of the analysis. This fact takes a better visual representation technique and transforms them to provide better results with faster processing and understanding.
Keywords: Occupational Incidents, Graphing Model, Charts and Graphs, Continuous Analysis, Real-time processing.
Scope of the Article: Measurement & Performance Analysis