Showing papers by "Adrijit Goswami published in 2019"

The big data system, components, tools, and technologies: a survey

Abstract: The traditional databases are not capable of handling unstructured data and high volumes of real-time datasets Diverse datasets are unstructured lead to big data, and it is laborious to store, manage, process, analyze, visualize, and extract the useful insights from these datasets using traditional database approaches However, many technical aspects exist in refining large heterogeneous datasets in the trend of big data This paper aims to present a generalized view of complete big data system which includes several stages and key components of each stage in processing the big data In particular, we compare and contrast various distributed file systems and MapReduce-supported NoSQL databases concerning certain parameters in data management process Further, we present distinct distributed/cloud-based machine learning (ML) tools that play a key role to design, develop and deploy data models The paper investigates case studies on distributed ML tools such as Mahout, Spark MLlib, and FlinkML Further, we classify analytics based on the type of data, domain, and application We distinguish various visualization tools pertaining three parameters: functionality, analysis capabilities, and supported development environment Furthermore, we systematically investigate big data tools and technologies (Hadoop 30, Spark 23) including distributed/cloud-based stream processing tools in a comparative approach Moreover, we discuss functionalities of several SQL Query tools on Hadoop based on 10 parameters Finally, We present some critical points relevant to research directions and opportunities according to the current trend of big data Investigating infrastructure tools for big data with recent developments provides a better understanding that how different tools and technologies apply to solve real-life applications

An EPQ model for deteriorating items with imperfect production, inspection errors, rework and shortages : a type-2 fuzzy approach

Abstract: In this paper, a production inventory model is studied considering imperfect production and deterioration of item, simultaneously. Both the serviceable and reworkable items are assumed to deteriorate with time. A cost-minimizing model is developed incorporating both Type I and Type II inspection errors. Shortages are allowed that are completely backlogged. All the screened items are reworked at the end of the production process. To encounter a more practical situation, the deterioration rate is considered to be a type-2 fuzzy number. Such a situation arises when the vendor assigns, with similar priority, a number of experts to determine the rate of deterioration and the decision given by each expert is in linguistic term, which may be replaced by a fuzzy number. The aim of the proposed model is to calculate the maximum back-order quantity allowed and the optimal lot size that must be produced in order to minimize the overall inventory cost. The problem is solved for both the crisp and fuzzy models and a numerical example with practical application is also presented to exemplify the procedure. A novel method for solving a type-2 fuzzy optimization problem is developed which results in a set of Pareto optimal solutions for the proposed problem. It is followed by presenting a sensitivity analysis of various parameters involved on the decision variables and the cost function for a better illustration of the model.

Nonlinear Triangular Intuitionistic Fuzzy Number and Its Application in Linear Integral Equation

Abstract: In this paper we introduce the different arithmetic operations on nonlinear intuitionistic fuzzy number (NIFN). All the arithmetic operations are done by max-min principle method which is nothing but the application of interval analysis. We also define the nonlinear intuitionistic fuzzy function which is used for finding the values, ambiguities, and ranking of nonlinear intuitionistic fuzzy number. The de-i-fuzzification of the corresponding intuitionistic fuzzy solution is done by average of - cut method. Finally we solve integral equation with NIFN by the help of intuitionistic fuzzy Laplace transform method.