A methodology for reducing error rate using fuzzy logic in big data environment
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A methodology for reducing error rate using fuzzy logic in big data environment

Project period

08/17/2019 - 09/15/2019




Project Category

Computer Science

A methodology for reducing error rate using fuzzy logic in big data environment
A methodology for reducing error rate using fuzzy logic in big data environment

A methodology for reducing error rate using fuzzy logic in the bigdata environment. Fuzzy geospatial models or the missing data results in the spread or even the increase of errors. To deal with the quality storage of fuzzy geospatial big data, we found the methodology that allows us to store consistent (correct) fuzzy geospatial data compliant with multi-database systems. The geographical map data are stored in a database. Sometimes, the data may be reduced and destroyed. So, we are using fuzzy logic to reduce the error rate/confusion matrix in the bigdata environment. Currently, we are witnessing a growing trend in the study and application of problems in the framework of Big Data. This is mainly due to the great advantages which come from the knowledge extraction from a high volume of information. For this reason, we observe a migration of the standard Data Mining systems towards a new functional paradigm that allows at working with Big Data. Employing the MapReduce model and its different extensions, scalability can be successfully addressed, while maintaining a good fault tolerance during the execution of the algorithms. Among the different approaches used in Data Mining, those models based on fuzzy systems stand out for many applications. Among their advantages, we must stress the use of a representation close to the natural language. Additionally, they use an inference model that allows a good adaptation to different scenarios, especially those with a given degree of uncertainty. Despite the success of this type of systems, their migration to the Big Data environment in the different learning areas is at a preliminary stage yet. In this project, we will carry out an overview of the main existing proposals on the topic, analyzing the design of these models. Additionally, we will discuss those problems related to the data distribution and parallelization of the current algorithms, and also its relationship with the fuzzy representation of the information. Finally, we will provide our view on the expectations for the future in this framework according to the design of those methods based on fuzzy sets, as well as the open challenges on the topic.

Why: Problem statement

The data gets duplicated while storing a large amount of data in the database. In a normal data storage technique, they do not have enough memory for deploying more memory tasks. So, some geographical map data get destroyed in a big data environment.

How: Solution description

We found the solution by adding flexibility and scalability of the NoSQL system using map reduced programming model and fuzzy logic to reduced confusion matrix in the bigdata environment. In this project, we will first introduce some concepts on Big Data. Then, we present an overview of the MapReduce programming model that supports scalability in data processing. Finally, we will describe two novel programming frameworks developed as an alternative to the standard MapReduce, under the premise of solving the shortcomings of this model in certain scenarios.

How is it different from competition

Existing system: Fuzzy geospatial models or the missing data results in the spread, or even the increase of errors. 

Proposed system: The flexibility and scalability of the NoSQL system. Using map reduced programming model and fuzzy logic, we reduced the calculation simulink in the bigdata environment.

Who are your customers

Common people use geographical map data. It is useful to find the path and reach the destination.

Project Phases and Schedule

Project Phase 1: Find the project of existing system problems and solutions.

Project Phase 2: Implement the project and solved the problem and developed.

Resources Required

Hardware Requirements: Intel Core i3 processor,  500GB (hard disk), monitor, mouse, keyboard.

Software Requirements:  Ubuntu OS, Java language

Tools: Hadoop, Pig, Hive

Project Code Code copy
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