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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Remote sensing big data computing
TL;DR: A brief overview on the Big Data and data-intensive problems, including the analysis of RS Big Data, Big Data challenges, current techniques and works for processing RS Big data is given.
Journal Article
Sensing as a Service and Big Data
TL;DR: Emerging Internet of Things architecture, large scale sensor network applications, federating sensor networks, sensor data and related context capturing techniques, challenges in cloud-based management, storing, archiving and processing of sensor data are discussed.
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Challenges in 5G: how to empower SON with big data for enabling 5G
Ali Imran,Ahmed Zoha +1 more
TL;DR: A comprehensive framework for empowering SONs with big data to address the requirements of 5G is proposed and the resultant dynamicity of a big data empowered SON (BSON) makes it more agile and can act as a key enabler for 5G's extremely low latency requirements.
Proceedings ArticleDOI
Addressing big data issues in Scientific Data Infrastructure
TL;DR: The paper proposes the SDI generic architecture model that provides a basis for building interoperable data or project centric SDI using modern technologies and best practices and introduces the Scientific Data Lifecycle Management (SDLM) model.
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Geospatial Big Data
Jae-Gil Lee,Minseo Kang +1 more
TL;DR: Several case studies are introduced to show the importance and benefits of the analytics of geospatial big data, including fuel and time saving, revenue increase, urban planning, and health care, and new emerging platforms for sharing the collected data and for tracking human mobility via mobile devices.