Open AccessAN EFFICIENT AND COST-EFFECTIVE MATHEMATICAL MODEL TO ANALYZE BIG DATA
Author(s) -
Ubaidullah Yashkun,
Waqas Akram,
Irfan Ali Memon
Publication year - 2017
Publication title -
journal of mountain area research
Language(s) - English
Resource type - Journals
eISSN - 2518-850X
pISSN - 2518-8496
DOI - 10.53874/jmar.v2i0.31
Subject(s) - piecewise , function (biology) , reliability (semiconductor) , revenue , big data , computer science , mathematical optimization , econometrics , mathematics , operations research , economics , data mining , finance , mathematical analysis , power (physics) , physics , quantum mechanics , evolutionary biology , biology
An efficient and cost-effective piecewise mathematical model is presented to represent a descriptive huge data mathematically. The techniques of function lines as decision boundaries are applied to incorporate the big data of the organization into slope intercept form. Which may be very helpful for a better understanding of discrete data to obtain sustainable and accurate results. Based on the boundaries limitation results of the collected data of the Federal Board of Revenue, the income tax against the income is studied. And finally the reliability of piecewise function to optimize the role of strategic management in any organization is investigated. The results showed that, the slope rate measured in the boundaries of income in percentage or increased slope rate is in good agreement with that predicted by the organization in descriptive form.