Special Issue "Emerging Approaches and Advances in Big Data"

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

Deadline for manuscript submissions: 31 December 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
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Guest Editor
Dr. Kevin Lee

School of Science and Technology, Nottingham Trent University, Clifton Campus, Nottingham, NG11 8NS, UK
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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 (7 papers)

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Research

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Open AccessArticle Self-Adaptive Pre-Processing Methodology for Big Data Stream Mining in Internet of Things Environmental Sensor Monitoring
Symmetry 2017, 9(10), 244; doi:10.3390/sym9100244
Received: 30 September 2017 / Revised: 11 October 2017 / Accepted: 15 October 2017 / Published: 21 October 2017
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Abstract
Over the years, advanced IT technologies have facilitated the emergence of new ways of generating and gathering data rapidly, continuously, and largely and are associated with a new research and application branch, namely, data stream mining (DSM). Among those multiple scenarios of DSM,
[...] Read more.
Over the years, advanced IT technologies have facilitated the emergence of new ways of generating and gathering data rapidly, continuously, and largely and are associated with a new research and application branch, namely, data stream mining (DSM). Among those multiple scenarios of DSM, the Internet of Things (IoT) plays a significant role, with a typical meaning of a tough and challenging computational case of big data. In this paper, we describe a self-adaptive approach to the pre-processing step of data stream classification. The proposed algorithm allows different divisions with both variable numbers and lengths of sub-windows under a whole sliding window on an input stream, and clustering-based particle swarm optimization (CPSO) is adopted as the main metaheuristic search method to guarantee that its stream segmentations are effective and adaptive to itself. In order to create a more abundant search space, statistical feature extraction (SFX) is applied after variable partitions of the entire sliding window. We validate and test the effort of our algorithm with other temporal methods according to several IoT environmental sensor monitoring datasets. The experiments yield encouraging outcomes, supporting the reality that picking significant appropriate variant sub-window segmentations heuristically with an incorporated clustering technique merit would allow these to perform better than others. Full article
(This article belongs to the Special Issue Emerging Approaches and Advances in Big Data)
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Open AccessArticle Toward Bulk Synchronous Parallel-Based Machine Learning Techniques for Anomaly Detection in High-Speed Big Data Networks
Symmetry 2017, 9(9), 197; doi:10.3390/sym9090197
Received: 28 August 2017 / Revised: 15 September 2017 / Accepted: 15 September 2017 / Published: 19 September 2017
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Abstract
Anomaly detection systems, also known as intrusion detection systems (IDSs), continuously monitor network traffic aiming to identify malicious actions. Extensive research has been conducted to build efficient IDSs emphasizing two essential characteristics. The first is concerned with finding optimal feature selection, while another
[...] Read more.
Anomaly detection systems, also known as intrusion detection systems (IDSs), continuously monitor network traffic aiming to identify malicious actions. Extensive research has been conducted to build efficient IDSs emphasizing two essential characteristics. The first is concerned with finding optimal feature selection, while another deals with employing robust classification schemes. However, the advent of big data concepts in anomaly detection domain and the appearance of sophisticated network attacks in the modern era require some fundamental methodological revisions to develop IDSs. Therefore, we first identify two more significant characteristics in addition to the ones mentioned above. These refer to the need for employing specialized big data processing frameworks and utilizing appropriate datasets for validating system’s performance, which is largely overlooked in existing studies. Afterwards, we set out to develop an anomaly detection system that comprehensively follows these four identified characteristics, i.e., the proposed system (i) performs feature ranking and selection using information gain and automated branch-and-bound algorithms respectively; (ii) employs logistic regression and extreme gradient boosting techniques for classification; (iii) introduces bulk synchronous parallel processing to cater computational requirements of high-speed big data networks; and; (iv) uses the Infromation Security Centre of Excellence, of the University of Brunswick real-time contemporary dataset for performance evaluation. We present experimental results that verify the efficacy of the proposed system. Full article
(This article belongs to the Special Issue Emerging Approaches and Advances in Big Data)
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Open AccessArticle A Robust Method for Finding the Automated Best Matched Genes Based on Grouping Similar Fragments of Large-Scale References for Genome Assembly
Symmetry 2017, 9(9), 192; doi:10.3390/sym9090192
Received: 9 August 2017 / Revised: 8 September 2017 / Accepted: 11 September 2017 / Published: 13 September 2017
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Abstract
Big data research on genomic sequence analysis has accelerated considerably with the development of next-generation sequencing. Currently, research on genomic sequencing has been conducted using various methods, ranging from the assembly of reads consisting of fragments to the annotation of genetic information using
[...] Read more.
Big data research on genomic sequence analysis has accelerated considerably with the development of next-generation sequencing. Currently, research on genomic sequencing has been conducted using various methods, ranging from the assembly of reads consisting of fragments to the annotation of genetic information using a database that contains known genome information. According to the development, most tools to analyze the new organelles’ genetic information requires different input formats such as FASTA, GeneBank (GB) and tab separated files. The various data formats should be modified to satisfy the requirements of the gene annotation system after genome assembly. In addition, the currently available tools for the analysis of organelles are usually developed only for specific organisms, thus the need for gene prediction tools, which are useful for any organism, has been increased. The proposed method—termed the genome_search_plotter—is designed for the easy analysis of genome information from the related references without any file format modification. Anyone who is interested in intracellular organelles such as the nucleus, chloroplast, and mitochondria can analyze the genetic information using the assembled contig of an unknown genome and a reference model without any modification of the data from the assembled contig. Full article
(This article belongs to the Special Issue Emerging Approaches and Advances in Big Data)
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Open AccessArticle An Efficient and Energy-Aware Cloud Consolidation Algorithm for Multimedia Big Data Applications
Symmetry 2017, 9(9), 184; doi:10.3390/sym9090184
Received: 14 August 2017 / Revised: 30 August 2017 / Accepted: 1 September 2017 / Published: 6 September 2017
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Abstract
It is well known that cloud computing has many potential advantages over traditional distributed systems. Many enterprises can build their own private cloud with open source infrastructure as a service (IaaS) frameworks. Since enterprise applications and data are migrating to private cloud, the
[...] Read more.
It is well known that cloud computing has many potential advantages over traditional distributed systems. Many enterprises can build their own private cloud with open source infrastructure as a service (IaaS) frameworks. Since enterprise applications and data are migrating to private cloud, the performance of cloud computing environments is of utmost importance for both cloud providers and users. To improve the performance, previous studies on cloud consolidation have been focused on live migration of virtual machines based on resource utilization. However, the approaches are not suitable for multimedia big data applications. In this paper, we reveal the performance bottleneck of multimedia big data applications in cloud computing environments and propose a cloud consolidation algorithm that considers application types. We show that our consolidation algorithm outperforms previous approaches. Full article
(This article belongs to the Special Issue Emerging Approaches and Advances in Big Data)
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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
Received: 5 June 2017 / Revised: 10 August 2017 / Accepted: 16 August 2017 / Published: 17 August 2017
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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)
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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
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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)
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Other

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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
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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)
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