Beyond the hype
TL;DR: 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.
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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.
1,267 citations
Cites background or methods from "Beyond the hype"
...On the other hand, the challenges are significant such as data integration complexities (Gandomi & Haider, 2015), lack of skilled personal and sufficient resources (Kim, Trimi, & Chung, 2014), data security and privacy issues (Barnaghi, Sheth, & Henson, 2013), inadequate infrastructure and…...
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...…say 3Vs [volume, velocity and variety] of data (e.g. Shah, Rabhi, & Ray, 2015), others reported 4Vs [volume, velocity, variety, and variability] of data (e.g. Liao, Yin, Huang, & Sheng, 2014) and 6Vs [volume, velocity, variety, veracity, variability, and value] of data (Gandomi & Haider, 2015)....
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...Gandomi and Haider (2015) asserts the need to develop new solutions for predictive analytics for structured BD. Predictive analytics are principally based on statistical methods and seeks to uncover patterns and capture relationships in data....
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...Big Data analytical methods – related to Q2 To facilitate evidence-based decision-making, organizations need efficient methods to process large volumes of assorted data into meaningful comprehensions (Gandomi & Haider, 2015)....
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...Thus, the necessity to deal with inaccurate and ambiguous data is another facet of BD, which is addressed using tools and analytics developed for management and mining of unreliable data (Gandomi & Haider, 2015)....
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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.
Abstract: Fault diagnosis is vital in manufacturing system, since early detections on the emerging problem can save invaluable time and cost. With the development of smart manufacturing, the data-driven fault diagnosis becomes a hot topic. However, the traditional data-driven fault diagnosis methods rely on the features extracted by experts. The feature extraction process is an exhausted work and greatly impacts the final result. Deep learning (DL) provides an effective way to extract the features of raw data automatically. Convolutional neural network (CNN) is an effective DL method. In this study, a new CNN based on LeNet-5 is proposed for fault diagnosis. Through a conversion method converting signals into two-dimensional (2-D) images, the proposed method can extract the features of the converted 2-D images and eliminate the effect of handcrafted features. The proposed method which is tested on three famous datasets, including motor bearing dataset, self-priming centrifugal pump dataset, and axial piston hydraulic pump dataset, has achieved prediction accuracy of 99.79%, 99.481%, and 100%, respectively. The results have been compared with other DL and traditional methods, including adaptive deep CNN, sparse filter, deep belief network, and support vector machine. The comparisons show that the proposed CNN-based data-driven fault diagnosis method has achieved significant improvements.
1,240 citations
Cites methods from "Beyond the hype"
...This provides new opportunities for the data-driven fault diagnosis methods to make full use of the massive mechanical data [5], and has received more and more attentions from the researchers and engineers....
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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.
964 citations
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.
937 citations
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.
Abstract: With the advances in new-generation information technologies, especially big data and digital twin, smart manufacturing is becoming the focus of global manufacturing transformation and upgrading. Intelligence comes from data. Integrated analysis for the manufacturing big data is beneficial to all aspects of manufacturing. Besides, the digital twin paves a way for the cyber-physical integration of manufacturing, which is an important bottleneck to achieve smart manufacturing. In this paper, the big data and digital twin in manufacturing are reviewed, including their concept as well as their applications in product design, production planning, manufacturing, and predictive maintenance. On this basis, the similarities and differences between big data and digital twin are compared from the general and data perspectives. Since the big data and digital twin can be complementary, how they can be integrated to promote smart manufacturing are discussed.
856 citations
References
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TL;DR: Researchers are investigating summarization tools and methods that automatically extract or abstract content from a range of information sources, including multimedia, looking at approaches which roughly fall into two categories: knowledge-poor and knowledge-rich.
Abstract: Summarization, the art of abstracting key content from one or more information sources, has become an integral part of everyday life. Researchers are investigating summarization tools and methods that automatically extract or abstract content from a range of information sources, including multimedia. Researchers are looking at approaches which roughly fall into two categories. Knowledge-poor approaches rely on not having to add new rules for each new application domain or language. Knowledge-rich approaches assume that if you grasp the meaning of the text, you can reduce it more effectively, thus yielding a better summary. Some approaches use a hybrid. In both methods, the main constraint is the compression requirement. High reduction rates pose a challenge because they are hard to attain without a reasonable amount of background knowledge. Another challenge is how to evaluate summarizers. If you are to trust that the summary is indeed a reliable substitute for the source, you must be confident that it does in fact reflect what is relevant in that source. Hence, methods for creating and evaluating summaries must complement each other.
341 citations
IBM1
TL;DR: This book provides a data-centric view of online social networks; a topic which has been missing from much of the literature.
Abstract: The advent of online social networks has been one of the most exciting events in this decade. Many popular online social networks such as Twitter, LinkedIn, and Facebook have become increasingly popular. In addition, a number of multimedia networks such as Flickr have also seen an increasing level of popularity in recent years. Many such social networks are extremely rich in content, and they typically contain a tremendous amount of content and linkage data which can be leveraged for analysis. The linkage data is essentially the graph structure of the social network and the communications between entities; whereas the content data contains the text, images and other multimedia data in the network. The richness of this network provides unprecedented opportunities for data analytics in the context of social networks. This book provides a data-centric view of online social networks; a topic which has been missing from much of the literature. This chapter provides an overview of the key topics in this field, and their coverage in this book.
299 citations
"Beyond the hype" refers methods in this paper
...In this regard, community detection is similar to clustering (Aggarwal, 2011), a data mining technique used to partition a data set into disjoint subsets based on the similarity of data points....
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TL;DR: An overview of online social networks is provided to contribute to a better understanding of this worldwide phenomenon and addresses the following questions: What are the major functionalities and characteristics ofOnline social networks?
270 citations
01 Jan 2012
TL;DR: This tutorial illustrates the application of data mining to social media using examples, and describes some projects of mining social media for human- itarian assistance and disaster relief for real-world applications.
Abstract: The pervasive use of social media has generated unprecedented amounts of social data. Social media provides easily an accessible platform for users to share informa- tion. Mining social media has its potential to extract actionable patterns that can be benecial for business, users, and consumers. Social media data are vast, noisy, unstructured, and dynamic in nature, and thus novel challenges arise. This tutorial reviews the basics of data mining and social media, introduces representative research problems of mining social media, illustrates the application of data mining to social media using examples, and describes some projects of mining social media for human- itarian assistance and disaster relief for real-world applications.
222 citations
01 Jan 2011
TL;DR: This chapter surveys the research on social influence analysis with a focus on the computational aspects, and presents the researchOn social influence maximization which has many practical applications including marketing and advertisement.
Abstract: Social influence is the behavioral change of a person because of the perceived relationship with other people, organizations and society in general. Social influence has been a widely accepted phenomenon in social networks for decades. Many applications have been built based around the implicit notation of social influence between people, such as marketing, advertisement and recommendations. With the exponential growth of online social network services such as Facebook and Twitter, social influence can for the first time be measured over a large population. In this chapter, we survey the research on social influence analysis with a focus on the computational aspects. First, we present statistical measurements related to social influence. Second, we describe the literature on social similarity and influences. Third, we present the research on social influence maximization which has many practical applications including marketing and advertisement.
203 citations
"Beyond the hype" refers background in this paper
...The Linear Threshold Model LTM) and Independent Cascade Model (ICM) are two well-known xamples of such frameworks (Sun & Tang, 2011)....
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