Department of Computer Science and Information Systems

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Author: search.filters.author.K., Pradheep KumarSubject: search.filters.subject.Big Data

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    Catur Approach to Assess the Quality of Big Data Using Decision Tree and Multidimensional Model
    (AENSI Publisher, 2015) K., Pradheep Kumar
    This paper is intended to design and develop multidimensional and decision tree based frameworks, for assessing the quality of a big data. Since the datasets represented in a big data environment is both complex and multidimensional, the quality of big data can be better viewed through multiple dimensions. Most enterprises face number of challenges in managing the quality of the big data during their initial setup or migration from traditional database or after building the big data. This paper uses multidimensional model proposed for Knowledge Management System for designing critical quality dimensions for big data. Based on the extensive literature review, this work proposes a classification of big data quality into many quality factors such as accessibility, consistency, integrity, usability, relevance, completeness, compatibility, conformity and accuracy. Since there are very few appropriate data stewards or frameworks available for confirmation of quality dimensions, this paper aims to develop some hybrid approaches using multi-dimensional model and decision tree based methods for automatic quality checks. Using decision tree, multiple if-then rules can be formed to decide on the quality of data based on the specific constraints developed for big data. The paper also aims to provide the quality framework and measures which can serve as a data quality firewall just like an internet firewall to proactively find the quality issues and apply the rules based on the decision tree algorithms to prevent bad or inconsistent or invalid data or access entering in to the big data environment.
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    Fuzzy-Based Querying Approach for Multidimensional Big Data Quality Assessment
    (2017) K., Pradheep Kumar
    This paper is intended to design a fuzzy based approach to assess standards and quality of big data. It also serves as a platform to organizations that intend to migrate their existing database environment to big data environment. Data is assessed using a multidimensional approach based on quality factors like accuracy, completeness, reliability, usability, etc. These factors are analysed by constructing decision trees to identify the quality aspects which need to be improved. In this work fuzzy queries have been designed. The queries are grouped as sets namely Excellent, Optimal, Fair and Hybrid. Based on the fuzzy data sets formed and the query compatibility index, a query set is chosen. A data set that has a very high degree of membership is assigned a fair query set. A data set with a medium degree of membership is assigned a optimal query set. A data set that has a lesser degree of membership is assigned a Excellent query set. A data set which needs a combination of queries of all the above is assigned a hybrid query set. The fuzzy query based approach reduces the query compatibility index by 36%, compared to a normal query set approach.