Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Navigation is a fundamental part of our day to day life. It is difficult for normal people to find different locations on their own and for visually impaired people it becomes more difficult. The environment around us is well known by the normal people but, visually disabled or partially abled people are not used to with the environment around them. And a more challenging task for these people will be navigating independently in new space, buildings or environments. The areas and environments around us are marked with signs, boards, and signals that can be viewed by a normal person. The proposed system shows us a way to make visually challenged people roam in the spaces around them without any external guidance or boards. In this paper, a system is proposed. It is an intelligent authentication and navigation system to help visually challenged people to navigate within any environment (e.g. shopping centers, public parks, office buildings). Based on an android application developed exclusively for visually challenged people and using wireless Bluetooth sensors deployed in the environment according to their respective range and locations. The proposed system is an information tracking system that will be able to guide visually challenged people in real-time to their destination and allow them to have all the information about the location. This system offers authentication, navigation through audio commands and provides detailed information about places, locality, and services that are available in the environment around the user. This initiative will change the way of day to day indoor navigation of visually challenged people. © 2020 SERSC.

World University root node

Computer Science

School of Computer Science and Engineering

Chennai Campus

International Journal of Advanced Science and Technology

Intelligent authentication and indoor mobile navigation system for visually disabled

Big data is a new technology, which is defined by large amount of data, so it is possible to extract value from the capturing and analysis process. Large data faced many challenges due to various features such as volume, speed, variation, value, complexity and performance. Many organizations face challenges while facing test strategies for structured and unstructured data validation, establishing a proper testing environment, working with non relational databases and maintaining functional testing. These challenges have low quality data in production, delay in execution and increase in cost. Reduce the map for data intensive business and scientific applications Provides parallel and scalable programming model. To get the performance of big data applications, defined as response time, maximum online user data capacity size, and a certain maximum processing capacity. In proposed, to test the health care big data . In health care data contains text file, image file, audio file and video file. To test the big data document, by using two concepts such as big data preprocessing testing and post processing testing. To classify the data from unstructured format to structured format using SVM algorithm. In preprocessing testing test all the data, for the purpose data accuracy. In preprocessing testing such as file size testing, file extension testing and de-duplication testing. In Post Processing to implement the map reduce concept for the use of easily to fetch the data.

International Journal of Recent Technology and Engineering 2

A Big Data Framework for Quality Assurance and Validation

Today, the usage of data volume has increased manifold. The domain of Big Data utilizes an attribute referred to as ‘Predictability’. This extraordinary feature basically draws prediction and based on user’s need, exhibits data to the user. The database systems prevailing today are quiet complex in nature. The software verification involves an essential phase of testing. The quality of database system depends on what is the quality of testing. Because of the high level of database complexity, testing too has become complicated which in turn has led to intensive workload. The evolution of Big Data has led many enterprises to make a shift towards big data technique such as ‘Hadoop’. The proposed work recommends the technique of big data for carrying out processing of heterogeneous data items. On such technique is the MapReduce which being a well-known programming model build to achieve the proposed issues. The research utilizes machine learning techniques and recommends the approach of big data validation and testing via MMSV (Multi Model Structure Validation) in context to data handling. The MMSV model performs data analysis and comprehends it for making future forecast. Researchers have built methodologies keeping the automated testing components centralized for enhancing overall quality and efficiency of testing methods. Testing involves checking of massive data volume gathered from various sources into primary and central data warehouse. It’s revealed that the technique of MMSV and big data yields improvised output compared to the prevailing system. Moreover the recommended system consumes low execution time. These techniques assure to attend all the issues thereby providing possible remedies and tactics. © BEIESP.

International Journal of Engineering and Advanced Technology

Automated testing for big data environment using multi model structure validation

Big data testing services are to deliver end to end testing methodologies which address our big data challenges. Methods: The testing module includes two types of functionalities. One is functional testing and second is non-functional testing. The functional testing should be accomplished at every stage of big data processing. Functional testing is nothing but the big data sources extraction testing, data migration testing and big data ecosystem. Testing which completes ETL test strategy, Map job reduce validation, multicore Data integration validation and data duplication check. On the other side the non-functional testing is to ensure that there are no quality defeat in data and no performance related issues. Applications: It covers the area for security testing, performance testing which solve the problem of monitoring and identify bottlenecks. © Serials Publications Pvt. Ltd.

International Journal of Economic Research

Towards an automated testing framework for big data

Big data, a platform in which everybody wants to gain knowledge on processing and analyzing the vast amount of data with ease. Various application are available today for processing vast amount of data and get the intended result from it, And various application testing tools are available to test the Big Data application. But no application or tool explain how data is validated before processing and after processing of data. Various existing functional and non-functional testing can be performed to assure the quality of the data so that the cost and time can be saved for the processing party. Here in this paper we are going to propose a set of testing strategies at various stages on Big Data analysis process so that one can validate data before retrieving the vast amount of data from Hadoop. © IAEME Publication.

International Journal of Mechanical Engineering and Technology

Pre hadoop and post hadoop validations for big data

Big Data is a big topic in software development today. When it comes to practice, software testers may not yet fully understand what Big Data is exactly. What testers do know is that you need a plan for testing it. The problem here is the lack of a clear understanding about what to test and how deep inside a tester should go. There are some key questions that must be answered before going down this path. Since most Big Data lacks a traditional structure, what does Big Data quality look like? And what are the most appropriate software testing tools? Many of us improperly believe that Big Data is just a large amount of information. This is a completely incorrect approach. For example, a 2 petabyte Oracle database alone doesn’t constitute a Big Data situation – just a high load one. To be very precise, Big Data is a series of approaches, tools and methods for processing of high volumes of structured and (most importantly) of unstructured data. © 2016, International Journal of Pharmacy and Technology. All rights reserved.

International Journal of Pharmacy and Technology

Big data validation needs and challenges

Cloud computing has emerged as a new technology across organization and cooperates that impacts several different research fields, including software testing. To provide a cloud service and sharing resources successfully, the cloud must be tested before it comes into offering services. Testing the applications has their own testing tools and testing methodologies. In this paper we provide an overview regarding cloud computing trends, types, challenges, tools and the comparison of tools for cloud testing. © 2015 The Authors. Published by Elsevier B.V.

Fulltext

Procedia Computer Science

Cloud Testing Tools and its Challenges: A Comparative Study

Big Data is already there and developers and analytics are working with it using several support upcoming frameworks and technologies. The storage and retrieval systems, the access layers and processes for Big Data are evolving day by day. Test Architects and Testing teams are not excluded in this Big scenario. This literature focuses on some of the challenges test teams would be facing in the near future. Two specialized testings are considered to learn the intricacies, and thoughts on Big Test Data Management are also presented. © 2013 IEEE.

2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT)

Getting ready for BigData testing: A practitioner's perception

As the software is modified and new test cases are added to the test-suite, the size of the test-suite grows and the cost of regression testing increases. In order to decrease the cost of regression testing, researchers have focused on the use of test-suite reduction techniques, which identify a subset of test cases that provides the same coverage of the software, according to some criterion, as the original test-suite. This paper investigates the use of an evolutionary approach, called genetic algorithms, for test-suite reduction. The proposed model builds the initial population based on test history, calculates the fitness value using coverage and run time of test case, and then selectively breeds the successive generations using genetic operations and allows only the fit tests to the reduced suite. This generational process is repeated until an optimized test-suite is found. © 2010 Kongu Engineering College.