LITERATURE SURVEY

To manage the growing demands, there is a need to increase the capacity and performance of tools and methods employed for analysis of data. Chen et al. (2014), in their work “Big data: A survey” focused on big data and reviewed related technologies and examined the application of big data in various fiels. Al-Jarrah et al. (2015), in their work “Efficient Machine Learning for Big Data: A Review” reviewed the data modeling in large scale data intensive field relating to model efficiency and new algorithm approaches. Hoffmann and Birnbrich (2012) to protect their customer from third party fraud proposed a conceptual link between retail bank activities in “The impact of fraud prevention on bank-customer relationships: An empirical investigation in retail banking”. Srivastava and Gopalkrishnan (2015) revealed some of the best techniques which are used by the banks across the globe and can be used by the Indian banks to enhance their services offerings to the customers in “Impact of Big Data Analytics on Banking Sector: Learning for Indian Banks”. Azar and Hassanien (2014) for dimensionality reduction presented a linguistic hedges neurofuzzy classifier with selected features (LHNFCSF). In this paper author compared the new classifier with the other classifiers for various classification problems in “Dimensionality reduction of medical big data using neural-fuzzy classifier”. Hassanien et al. (2015) focused on application, challenges and opportunities of big data in “Big Data in Complex Systems: Challenges and Opportunities”. Wahi et al. (2014) proposed a social media and its implication on customer relationship management in “Social Media: The core of enterprise 2.0.”. Shabeera and Madhu Kumar (2015), in their work “Optimizing virtual machine allocation in MapReduce cloud for improved data locality” focused on improving data locality by allocating virtual machines for executing map reduce jobs. Aloui and Touzi (2015) proposed a methodology for designing ontology on a new platform called “FO-FQ Tab plug-in” and then querying them smartly based on conceptual clustering and fuzzy logic in “A Fuzzy Ontology-Based Platform for Flexible Querying”. Ghallab et al. (2014), in their work “Strictness petroleum prediction system based on fussy model” predicted the status of crude oil and then compared it with other petroleum values. Huang et al. (2015) summarized the latest application of big data in health science. The authors also reviewed the latest technologies of big data and discussed the future perspective of health sciences in “Promises and challenges of big data computing in health science”. Jagadish (2015) in “Big Data and Science: Myths and Reality” explored myths about big data and exposed the underlying truth. Jin et al. (2015) introduced the concept of big data and described the challenges as well as solution to these challenges in “Significance and challenges of big data research”. Ryan and Lee (2015) presented a Multi-tier resource allocation as resource management technique for distributed systems in “Multi-tier resource allocation for data-intensive computing”. Tiwari and Joshi (2015) in “Data security for software as a service” discussed security vulnerabilities of software as a service (SaaS) and its solution. Wahi et al. (2015) focused on whether the organization could able to address challenges posed by big data successfully or not. It also focused on the reasons why it is necessary to transit from the enterprise 1.0 stage to enterprise 2.0 stage in “Big Data: Enabler or Challenge for Enterprise 2.0. Deepak and John (2016) illustrated that information system is one of the most significant problem in fuzzy domain. Authors illustrated a case where hesitant membership value arrived from attribute value whose membership values are a family of set and also discusses the homomorphism between hesitant information systems in “Information Systems on Hesitant Fuzzy Sets”. Bhanu and Tripathy (2016) in “Rough Set Based Similarity Measures for Data Analytics in Spatial Epidemiology” carried out epidemiological studies to understand a pattern and transmission of disease instances.

 
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