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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Green innovation and organizational performance: The influence of big data and the moderating role of management commitment and HR practices
TL;DR: In this article, the authors developed and tested a holistic model that depicts and examines the relationships among green innovation, its drivers, as well as factors that help overcome the technological challenges and influence the performance and competitive advantage of the firm.
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A survey towards an integration of big data analytics to big insights for value-creation
Mandeep Kaur Saggi,Sushma Jain +1 more
TL;DR: This article presents a comprehensive, well-informed examination, and realistic analysis of deploying big data analytics successfully in companies and presents a methodical analysis for the usage of Big Data Analytics in various applications such as agriculture, healthcare, cyber security, and smart city.
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Artificial intelligence and business models in the sustainable development goals perspective: A systematic literature review
TL;DR: In this article, the authors investigated the role of Artificial Intelligence (AI) in the construction of sustainable business models (SBMs) and provided a quantitative overview of the academic literature that constitutes the field.
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Big data analytics as an operational excellence approach to enhance sustainable supply chain performance
TL;DR: In this paper, the authors used dynamic capability theory as a foundation for evaluating the role of BDA capability as an operational excellence approach in improving sustainable supply chain performance, and identified two pathways that managers can use to improve sustainable supply-chain outcomes in the mining industry based on big data analytics capabilities.
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Role of institutional pressures and resources in the adoption of big data analytics powered artificial intelligence, sustainable manufacturing practices and circular economy capabilities
TL;DR: In this article, the authors employed institutional theory and resource-based view theory to elucidate the way in which automotive firms configure tangible resources and workforce skills to drive technological enablement and improve sustainable manufacturing practices and furthermore develop circular economy capabilities.
References
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Hazy: making it easier to build and maintain big-data analytics
TL;DR: Racing to unleash the full potential of big data with the latest statistical and machine-learning techniques.
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A distributed frequent itemset mining algorithm using Spark for Big Data analytics
TL;DR: An efficient distributed frequent itemset mining algorithm (DFIMA) which can significantly reduce the amount of candidate itemsets by applying a matrix-based pruning approach is proposed.
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A cloud-based framework for Home-diagnosis service over big medical data
TL;DR: A cloud-based framework to implement a Home-diagnosis service where similar historical medical records as well as a disease-symptom lattice are obtained to help users figure out which kind of disease they are probably infected with.
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Big privacy: protecting confidentiality in big data
TL;DR: Approaches from computer science and statistical science for assessing and protecting privacy in large, public data sets.
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Geographical information system parallelization for spatial big data processing: a review
TL;DR: The general evolution of the GIS architecture is presented which includes main two parallel GIS architectures based on high performance computing cluster and Hadoop cluster and the current spatial data partition strategies, key methods to realize Parallel GIS in the view of data decomposition and progress of the special parallel Gis algorithms are summarized.