Beyond the hype
Amir H. Gandomi,Murtaza Haider +1 more
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TLDR
The need to develop appropriate and efficient analytical methods to leverage massive volumes of heterogeneous data in unstructured text, audio, and video formats is highlighted and the need to devise new tools for predictive analytics for structured big data is reinforced.About:
This article is published in International Journal of Information Management.The article was published on 2015-04-01 and is currently open access. It has received 2962 citations till now. The article focuses on the topics: Analytics & Big data.read more
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Critical analysis of Big Data challenges and analytical methods
TL;DR: 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.
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A New Convolutional Neural Network-Based Data-Driven Fault Diagnosis Method
TL;DR: A new CNN based on LeNet-5 is proposed for fault diagnosis which can extract the features of the converted 2-D images and eliminate the effect of handcrafted features and has achieved significant improvements.
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Big data
Ibrar Yaqoob,Ibrahim Abaker Targio Hashem,Abdullah Gani,Salimah Binti Mokhtar,Ejaz Ahmed,Nor Badrul Anuar,Athanasios V. Vasilakos +6 more
TL;DR: This paper presents a comprehensive discussion on state-of-the-art big data technologies based on batch and stream data processing based on structuralism and functionalism paradigms and strengths and weaknesses of these technologies are analyzed.
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Data-driven smart manufacturing
TL;DR: The role of big data in supporting smart manufacturing is discussed, a historical perspective to data lifecycle in manufacturing is overviewed, and a conceptual framework proposed in the paper is proposed.
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Digital Twin and Big Data Towards Smart Manufacturing and Industry 4.0: 360 Degree Comparison
Qinglin Qi,Fei Tao +1 more
TL;DR: The similarities and differences between big data and digital twin are compared from the general and data perspectives and how they can be integrated to promote smart manufacturing are discussed.
References
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Book
Opinion Mining and Sentiment Analysis
Bo Pang,Lillian Lee +1 more
TL;DR: This survey covers techniques and approaches that promise to directly enable opinion-oriented information-seeking systems and focuses on methods that seek to address the new challenges raised by sentiment-aware applications, as compared to those that are already present in more traditional fact-based analysis.
Book
Big data: The next frontier for innovation, competition, and productivity
TL;DR: The amount of data in the authors' world has been exploding, and analyzing large data sets will become a key basis of competition, underpinning new waves of productivity growth, innovation, and consumer surplus, according to research by MGI and McKinsey.
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Business intelligence and analytics: from big data to big impact
TL;DR: This introduction to the MIS Quarterly Special Issue on Business Intelligence Research first provides a framework that identifies the evolution, applications, and emerging research areas of BI&A, and introduces and characterized the six articles that comprise this special issue in terms of the proposed BI &A research framework.
Book
Sentiment Analysis and Opinion Mining
TL;DR: Sentiment analysis and opinion mining is the field of study that analyzes people's opinions, sentiments, evaluations, attitudes, and emotions from written language as discussed by the authors and is one of the most active research areas in natural language processing and is also widely studied in data mining, Web mining, and text mining.
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Sure independence screening for ultrahigh dimensional feature space
Jianqing Fan,Jinchi Lv +1 more
TL;DR: In this article, the authors introduce the concept of sure screening and propose a sure screening method that is based on correlation learning, called sure independence screening, to reduce dimensionality from high to a moderate scale that is below the sample size.