Critical analysis of Big Data challenges and analytical methods
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TLDR
In this article, the authors present a state-of-the-art review that presents a holistic view of the BD challenges and BDA methods theorized/proposed/employed by organizations to help others understand this landscape with the objective of making robust investment decisions.About:
This article is published in Journal of Business Research.The article was published on 2017-01-01 and is currently open access. It has received 1267 citations till now. The article focuses on the topics: Analytics & Big data.read more
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Journal ArticleDOI
Low-overhead compressibility prediction for high-performance lossless data compression
Youngil Kim,Jinwoo Jeong,Wang Kexin,Yong Ho Song,Seungdo Choi,Daeyong Lee,Joonyong Jeong,Jaewook Kwak,Jungkeol Lee,Gyeongyong Lee,Sangjin Lee,Kibin Park +11 more
TL;DR: The proposed compressibility prediction method provides more fine-grained selectivity for combinational compression, and reduces the amount of resources consumed by the compressibility predictor, enabling selective compression at a low cost.
Book ChapterDOI
Performance Analysis of Machine Learning Techniques on Big Data Using Apache Spark
TL;DR: This paper compares various classification based machine learning algorithms namely, Decision Tree Learning, Naive Bayes, Random Forest and Support Vector Machines on big data using Apache Spark to find out which classification based algorithm gives fast and better result.
Journal ArticleDOI
Customer-centered data power: Sensing and responding capability in big data analytics
TL;DR: In this article , the authors collected top managers' opinions from different companies and applied a quantitative method to empirically examine the proposed model to enhance operations and found that using big data analysis tools effectively enhances customer sensing and response capabilities.
Book ChapterDOI
Approximate Partitional Clustering Through Systematic Sampling in Big Data Mining
TL;DR: The experimental evaluation of the SYK-means algorithm achieved better effectiveness and efficiency through R squares, root-mean-square standard deviation, Davies Bouldin, Calinski Harabasz, Silhouette coefficient, CPU time, and convergence validation indices.
Journal ArticleDOI
Iinterdisciplinarity in Data Science over Big Data: findings for mining industry
Vitor Afonso Pinto,Ana Maria Pereira Cardoso,Marta Macedo Kerr Pinheiro,Fernando Silva Parreiras +3 more
TL;DR: It is concluded that achieving results with Data Science initiative over big data is not related to a single knowledge area, especially in mining industries.
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