Special Issue "Emerging Approaches and Advances in Big Data"

A special issue of Symmetry (ISSN 2073-8994).

Deadline for manuscript submissions: 30 September 2017

Special Issue Editors

Guest Editor
Prof. Dr. Ka Lok Man

Department of Computer Science and Software Engineering, Xi’an Jiaotong Liverpool University, Suzhou Dushu Lake Higher Education Town, Suzhou Industrial Park, Jiangsu Province, China
E-Mail
Guest Editor
Dr. Kevin Lee

School of Science and Technology, Nottingham Trent University, Clifton Campus, Nottingham, NG11 8NS, UK
E-Mail

Special Issue Information

Dear Colleagues,

The growth of big data presents challenges, as well as opportunities, for industries and academia. Accumulated data can be extracted, processed, analyzed, and reported in time to deliver better data insights, complex patterns and valuable predictions to the design and analysis of various systems/platforms, including complex business models, highly scalable system and reconfigurable hardware and software systems, as well as wireless sensor and actuator networks. The main building blocks of big data analytics include:

  • big data thinking
  • computational tools
  • data modelling
  • analytical algorithms
  • data governance

Big data thinking is an exciting area that, not only involves business organizational data-related culture, but also big data projects initiation, team formation and best practices. Computational platforms and tools offer adaptive mechanisms that enable the understanding of data in complex and changing environments. Algorithms and analysis methods are the foundations for many solutions to real problems. Data and information governance and social responsibility directly affect data usage and social acceptance of business solutions.

This Special Issue on “Emerging Approaches and Advances in Big Data” will focus on emerging approaches and recent advances on architectures, design techniques, modeling and prototyping solutions for the design of complex business models, highly scalable system and reconfigurable hardware and software systems, and computing networks in the era of big data.

Prof. Dr. Ka Lok Man
Dr. Kevin Lee
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. Symmetry is an international peer-reviewed open access monthly 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 1000 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 architecture, modelling and toolkits
  • big data for business model and intelligence
  • big data challenges for small, medium and large enterprises
  • big data analytics and innovations
  • big data systems/analytics on emerging hardware/software architectures and computing networks

Published Papers (3 papers)

View options order results:
result details:
Displaying articles 1-3
Export citation of selected articles as:

Research

Jump to: Other

Open AccessArticle Using Knowledge Transfer and Rough Set to Predict the Severity of Android Test Reports via Text Mining
Symmetry 2017, 9(8), 161; doi:10.3390/sym9080161 (registering DOI)
Received: 5 June 2017 / Revised: 10 August 2017 / Accepted: 16 August 2017 / Published: 17 August 2017
PDF Full-text (2685 KB) | HTML Full-text | XML Full-text
Abstract
Crowdsourcing is an appealing and economic solution to software application testing because of its ability to reach a large international audience. Meanwhile, crowdsourced testing could have brought a lot of bug reports. Thus, in crowdsourced software testing, the inspection of a large number
[...] Read more.
Crowdsourcing is an appealing and economic solution to software application testing because of its ability to reach a large international audience. Meanwhile, crowdsourced testing could have brought a lot of bug reports. Thus, in crowdsourced software testing, the inspection of a large number of test reports is an enormous but essential software maintenance task. Therefore, automatic prediction of the severity of crowdsourced test reports is important because of their high numbers and large proportion of noise. Most existing approaches to this problem utilize supervised machine learning techniques, which often require users to manually label a large number of training data. However, Android test reports are not labeled with their severity level, and manual labeling is time-consuming and labor-intensive. To address the above problems, we propose a Knowledge Transfer Classification (KTC) approach based on text mining and machine learning methods to predict the severity of test reports. Our approach obtains training data from bug repositories and uses knowledge transfer to predict the severity of Android test reports. In addition, our approach uses an Importance Degree Reduction (IDR) strategy based on rough set to extract characteristic keywords to obtain more accurate reduction results. The results of several experiments indicate that our approach is beneficial for predicting the severity of android test reports. Full article
(This article belongs to the Special Issue Emerging Approaches and Advances in Big Data)
Figures

Figure 1

Open AccessArticle A Case Study on Iteratively Assessing and Enhancing Wearable User Interface Prototypes
Symmetry 2017, 9(7), 114; doi:10.3390/sym9070114
Received: 18 May 2017 / Revised: 4 July 2017 / Accepted: 6 July 2017 / Published: 10 July 2017
PDF Full-text (19024 KB) | HTML Full-text | XML Full-text
Abstract
Wearable devices are being explored and investigated as a promising computing platform as well as a source of personal big data for the post smartphone era. To deal with a series of rapidly developed wearable prototypes, a well-structured strategy is required to assess
[...] Read more.
Wearable devices are being explored and investigated as a promising computing platform as well as a source of personal big data for the post smartphone era. To deal with a series of rapidly developed wearable prototypes, a well-structured strategy is required to assess the prototypes at various development stages. In this paper, we first design and develop variants of advanced wearable user interface prototypes, including joystick-embedded, potentiometer-embedded, motion-gesture and contactless infrared user interfaces for rapidly assessing hands-on user experience of potential futuristic user interfaces. To achieve this goal systematically, we propose a conceptual test framework and present a case study of using the proposed framework in an iterative cyclic process to prototype, test, analyze, and refine the wearable user interface prototypes. We attempt to improve the usability of the user interface prototypes by integrating initial user feedback into the leading phase of the test framework. In the following phase of the test framework, we track signs of improvements through the overall results of usability assessments, task workload assessments and user experience evaluation of the prototypes. The presented comprehensive and in-depth case study demonstrates that the iterative approach employed by the test framework was effective in assessing and enhancing the prototypes, as well as gaining insights on potential applications and establishing practical guidelines for effective and usable wearable user interface development. Full article
(This article belongs to the Special Issue Emerging Approaches and Advances in Big Data)
Figures

Figure 1

Other

Jump to: Research

Open AccessFeature PaperProject Report A Study on Big Data Thinking of the Internet of Things-Based Smart-Connected Car in Conjunction with Controller Area Network Bus and 4G-Long Term Evolution
Symmetry 2017, 9(8), 152; doi:10.3390/sym9080152
Received: 19 May 2017 / Revised: 2 August 2017 / Accepted: 2 August 2017 / Published: 9 August 2017
PDF Full-text (5818 KB) | HTML Full-text | XML Full-text
Abstract
A smart connected car in conjunction with the Internet of Things (IoT) is an emerging topic. The fundamental concept of the smart connected car is connectivity, and such connectivity can be provided by three aspects, such as Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I), and Vehicle-to-Everything
[...] Read more.
A smart connected car in conjunction with the Internet of Things (IoT) is an emerging topic. The fundamental concept of the smart connected car is connectivity, and such connectivity can be provided by three aspects, such as Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I), and Vehicle-to-Everything (V2X). To meet the aspects of V2V and V2I connectivity, we developed modules in accordance with international standards with respect to On-Board Diagnostics II (OBDII) and 4G Long Term Evolution (4G-LTE) to obtain and transmit vehicle information. We also developed software to visually check information provided by our modules. Information related to a user’s driving, which is transmitted to a cloud-based Distributed File System (DFS), was then analyzed for the purpose of big data analysis to provide information on driving habits to users. Yet, since this work is an ongoing research project, we focus on proposing an idea of system architecture and design in terms of big data analysis. Therefore, our contributions through this work are as follows: (1) Develop modules based on Controller Area Network (CAN) bus, OBDII, and 4G-LTE; (2) Develop software to check vehicle information on a PC; (3) Implement a database related to vehicle diagnostic codes; (4) Propose system architecture and design for big data analysis. Full article
(This article belongs to the Special Issue Emerging Approaches and Advances in Big Data)
Figures

Figure 1

Journal Contact

MDPI AG
Symmetry Editorial Office
St. Alban-Anlage 66, 4052 Basel, Switzerland
E-Mail: 
Tel. +41 61 683 77 34
Fax: +41 61 302 89 18
Editorial Board
Contact Details Submit to Special Issue Edit a special issue Review for Symmetry
logo
loading...
Back to Top