Special Issue "Supporting Technologies and Enablers for Big Data"

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Systems".

Deadline for manuscript submissions: closed (30 September 2017)

Special Issue Editors

Guest Editor
Prof. Steven Guan

Research Institute of Big Data Analytics, Xi’an Jiaotong-Liverpool University, Suzhou, China
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Interests: machine learning; computational intelligence; security; networking; data mining; personalization; electronic commerce; mobile commerce
Guest Editor
Prof. Xizhao Wang

Big Data Institute, Shenzhen University, Shenzhen, China
Website | E-Mail
Interests: machine learning; computational intelligence; uncertainty modeling; data mining; big data analytics
Guest Editor
Prof. Hai Zhuge

Aston University, UK; Chinese Academy of Sciences, China
Website | E-Mail
Interests: semantic link network; multi-dimensional category space; data and knowledge management; cyber-physical society
Guest Editor
Prof. Jianhua Zhang

School of Information Science and Engineering, East China University of Science and Technology, Shanghai, China
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Interests: machine learning and deep learning; soft computing; intelligent systems and control; intelligent data modeling and analysis; large-scale (human) physiological data mining and pattern recognition
Guest Editor
Prof. Ching-Shih Tsou

Graduate Institute of Information and Decision Sciences, National Taipei University of Business, Taipei, Taiwan
Website | E-Mail
Interests: data science; multiobjective optimization; evolutionary computation; game-theoretic modeling

Special Issue Information

Dear Colleagues,

In recent years, “Big Data” has become a main theme in many aspects of computing. Big Data is transforming science, engineering, medicine, healthcare, finance, business, and, ultimately, society itself. The Special Issue on “Supporting Technologies and Enablers for Big Data” aims at providing an international forum for researchers and practitioners to exchange information regarding advancements in the state of art and practice of big data related technology development and business applications. It will contain expanded versions of selected papers presented at the 2017 IEEE 2nd International Conference on Big Data Analysis (ICBDA 2017), which is to take place held in Bejing, China on 10–12 March, 2017.

Prof. Steven Guan, Xi’an Jiaotong-Liverpool University
Prof. Xizhao Wang, Big Data Institute, Shenzhen University
Prof. Hai Zhuge, Aston University and Chinese Academy of Sciences
Prof. Jianhua Zhang, East China University of Science and Technology
Prof. Ching-Shih Tsou, National Taipei University of Business
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Information is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 350 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • big data analytics, applications, architecture, management, metrics, modeling, platforms, services, and toolkits;
  • data analytics;
  • data governance;
  • data modelling;
  • data protection,
  • integrity, and privacy;
  • data social responsibilities;
  • business intelligence;
  • marketing analytics

Published Papers (2 papers)

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Research

Open AccessArticle A New Anomaly Detection System for School Electricity Consumption Data
Information 2017, 8(4), 151; doi:10.3390/info8040151
Received: 29 September 2017 / Revised: 8 November 2017 / Accepted: 16 November 2017 / Published: 20 November 2017
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Abstract
Anomaly detection has been widely used in a variety of research and application domains, such as network intrusion detection, insurance/credit card fraud detection, health-care informatics, industrial damage detection, image processing and novel topic detection in text mining. In this paper, we focus on
[...] Read more.
Anomaly detection has been widely used in a variety of research and application domains, such as network intrusion detection, insurance/credit card fraud detection, health-care informatics, industrial damage detection, image processing and novel topic detection in text mining. In this paper, we focus on remote facilities management that identifies anomalous events in buildings by detecting anomalies in building electricity consumption data. We investigated five models within electricity consumption data from different schools to detect anomalies in the data. Furthermore, we proposed a hybrid model that combines polynomial regression and Gaussian distribution, which detects anomalies in the data with 0 false negative and an average precision higher than 91%. Based on the proposed model, we developed a data detection and visualization system for a facilities management company to detect and visualize anomalies in school electricity consumption data. The system is tested and evaluated by facilities managers. According to the evaluation, our system has improved the efficiency of facilities managers to identify anomalies in the data. Full article
(This article belongs to the Special Issue Supporting Technologies and Enablers for Big Data)
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Open AccessArticle Investigating the Statistical Distribution of Learning Coverage in MOOCs
Information 2017, 8(4), 150; doi:10.3390/info8040150
Received: 30 September 2017 / Revised: 17 November 2017 / Accepted: 17 November 2017 / Published: 20 November 2017
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Abstract
Learners participating in Massive Open Online Courses (MOOC) have a wide range of backgrounds and motivations. Many MOOC learners enroll in the courses to take a brief look; only a few go through the entire content, and even fewer are able to eventually
[...] Read more.
Learners participating in Massive Open Online Courses (MOOC) have a wide range of backgrounds and motivations. Many MOOC learners enroll in the courses to take a brief look; only a few go through the entire content, and even fewer are able to eventually obtain a certificate. We discovered this phenomenon after having examined 92 courses on both xuetangX and edX platforms. More specifically, we found that the learning coverage in many courses—one of the metrics used to estimate the learners’ active engagement with the online courses—observes a Zipf distribution. We apply the maximum likelihood estimation method to fit the Zipf’s law and test our hypothesis using a chi-square test. In the xuetangX dataset, the learning coverage in 53 of 76 courses fits Zipf’s law, but in all of 16 courses on the edX platform, the learning coverage rejects the Zipf’s law. The result from our study is expected to bring insight to the unique learning behavior on MOOC. Full article
(This article belongs to the Special Issue Supporting Technologies and Enablers for Big Data)
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