Critical analysis of Big Data challenges and analytical methods
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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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Smart data and business analytics: A theoretical framework for managing rework risks in mega-projects
TL;DR: In this article , an ontology for rework is proposed to understand patterns of occurrence and risks and provide a much-needed structure for decision-making in transport mega-projects.
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Evaluating medical travelers’ satisfaction through online review analysis
Ali Ahani,Mehrbakhsh Nilashi,Waleed Abdu Zogaan,Sarminah Samad,Nojood O. Aljehane,Ashwaq Alhargan,Saidatulakmal Mohd,Hossein Ahmadi,Louis Sanzogni +8 more
TL;DR: In this article, a new method was developed to reveal travelers' choice preferences and satisfaction with medical tourism services through the analysis of the online review, where text mining and ontology approaches were used in the proposed method.
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Using artificial intelligence to overcome over-indebtedness and fight poverty.
Mário B. Ferreira,Diego Costa Pinto,Márcia Maurer Herter,Jerônimo C. Soro,Leonardo Vanneschi,Mauro Castelli,Fernando Peres +6 more
TL;DR: This research uses Automated Machine Learning in a field database of 1654 over-indebted households to identify distinguishable clusters and to predict its risk factors, adding both theoretically and methodologically to current models of scarcity with important practical implications for business research and society.
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Medical Big Data Classification Using a Combination of Random Forest Classifier and KMeans Clustering
R. Saravana kumar,P. Manikandan +1 more
TL;DR: The Random Forest Classification using K-means clustering algorithm is adapted to overcome the complexity and accuracy issue and the experimental results indicate that the proposed algorithm increases the data accuracy.
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