Special Issue "Big Data and Cognitive Computing: Feature Papers 2018"

A special issue of Big Data and Cognitive Computing (ISSN 2504-2289).

Deadline for manuscript submissions: 10 December 2018

Special Issue Editors

Guest Editor
Prof. Dr. Min Chen

Embedded and Pervasive Computing (EPIC) Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan, 430074, China
Website | E-Mail
Interests: big data analytics; cognitive systems; mobile cloud computing; IoT Sensing; cyber-physical systems; 5G networks; SDN; healthcare big data; emotion communications and robotics
Co-Guest Editor
Prof. Dr. Kai Hwang

Department of Electrical Engineering, EEB-212, University of Southern California, Los Angeles, 90089-2562 CA, USA
Website | E-Mail
Phone: 1-(213)-740-4470
Fax: 1-(213)-740-4418
Interests: big data analytics; deep learning; cognitive computing; cloud computing; supercomputing

Special Issue Information

Dear Colleagues,

This is a Special Issue of high quality papers (original research articles or comprehensive review papers) in open access format, by the Editorial Board Members, or those invited by the Editorial Board Members and the Editorial Office. Papers will be published, free of charge, after peer review. The scope of “Big Data and Cognitive Computing” includes, but is not limited to, the following items:

Big data, Clouds and Internet of Things (IoT)

  • data storage and management
  • data search and mining
  • big data infrastructure and systems
  • big data processing and analytics
  • big data applications in science, Internet, finance, telecommunications, business, medicine, healthcare, government, transportation, industry, manufacture, etc.
  • big data integrity and privacy
  • big data models, algorithms, and architectures
  • cloud computing and big data platform
  • cloud services and big data applications
  • IoT technologies for big data collections
  • IoT sensing and cognitive IoT
  • data-driven IoT intelligent applications
  • 5G network and wireless big data

Cognitive Computing

  • machine learning and its applications in medicine, biology, industry, manufacturing, security, education, etc.
  • deep learning
  • artificial intelligence
  • affect/emotion/personality/mind computing
  • cognitive modeling
  • cognitive informatics
  • cognitive sensor-networks
  • cognitive robots
  • application of cognitive computing in health monitoring, intelligent control systems, bioinformatics, smart manufacturing, smart grids, image/video and signal processing, etc.
  • robots and control systems
  • natural language processing
  • human–machine/robot interaction

Prof. Dr. Min Chen
Prof. Dr. Kai Hwang
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. Big Data and Cognitive Computing 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) is waived for well-prepared manuscripts submitted to this issue. 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.

Published Papers (7 papers)

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Research

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Open AccessArticle Topological Signature of 19th Century Novelists: Persistent Homology in Text Mining
Big Data Cogn. Comput. 2018, 2(4), 33; https://doi.org/10.3390/bdcc2040033
Received: 23 September 2018 / Revised: 8 October 2018 / Accepted: 15 October 2018 / Published: 18 October 2018
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Abstract
Topological Data Analysis (TDA) refers to a collection of methods that find the structure of shapes in data. Although recently, TDA methods have been used in many areas of data mining, it has not been widely applied to text mining tasks. In most
[...] Read more.
Topological Data Analysis (TDA) refers to a collection of methods that find the structure of shapes in data. Although recently, TDA methods have been used in many areas of data mining, it has not been widely applied to text mining tasks. In most text processing algorithms, the order in which different entities appear or co-appear is being lost. Assuming these lost orders are informative features of the data, TDA may play a significant role in the resulted gap on text processing state of the art. Once provided, the topology of different entities through a textual document may reveal some additive information regarding the document that is not reflected in any other features from conventional text processing methods. In this paper, we introduce a novel approach that hires TDA in text processing in order to capture and use the topology of different same-type entities in textual documents. First, we will show how to extract some topological signatures in the text using persistent homology-i.e., a TDA tool that captures topological signature of data cloud. Then we will show how to utilize these signatures for text classification. Full article
(This article belongs to the Special Issue Big Data and Cognitive Computing: Feature Papers 2018)
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Open AccessArticle Edge Machine Learning: Enabling Smart Internet of Things Applications
Big Data Cogn. Comput. 2018, 2(3), 26; https://doi.org/10.3390/bdcc2030026
Received: 11 July 2018 / Revised: 14 August 2018 / Accepted: 17 August 2018 / Published: 3 September 2018
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Abstract
Machine learning has traditionally been solely performed on servers and high-performance machines. However, advances in chip technology have given us miniature libraries that fit in our pockets and mobile processors have vastly increased in capability narrowing the vast gap between the simple processors
[...] Read more.
Machine learning has traditionally been solely performed on servers and high-performance machines. However, advances in chip technology have given us miniature libraries that fit in our pockets and mobile processors have vastly increased in capability narrowing the vast gap between the simple processors embedded in such things and their more complex cousins in personal computers. Thus, with the current advancement in these devices, in terms of processing power, energy storage and memory capacity, the opportunity has arisen to extract great value in having on-device machine learning for Internet of Things (IoT) devices. Implementing machine learning inference on edge devices has huge potential and is still in its early stages. However, it is already more powerful than most realise. In this paper, a step forward has been taken to understand the feasibility of running machine learning algorithms, both training and inference, on a Raspberry Pi, an embedded version of the Android operating system designed for IoT device development. Three different algorithms: Random Forests, Support Vector Machine (SVM) and Multi-Layer Perceptron, respectively, have been tested using ten diverse data sets on the Raspberry Pi to profile their performance in terms of speed (training and inference), accuracy, and power consumption. As a result of the conducted tests, the SVM algorithm proved to be slightly faster in inference and more efficient in power consumption, but the Random Forest algorithm exhibited the highest accuracy. In addition to the performance results, we will discuss their usability scenarios and the idea of implementing more complex and taxing algorithms such as Deep Learning on these small devices in more details. Full article
(This article belongs to the Special Issue Big Data and Cognitive Computing: Feature Papers 2018)
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Open AccessArticle The Rise of Big Data Science: A Survey of Techniques, Methods and Approaches in the Field of Natural Language Processing and Network Theory
Big Data Cogn. Comput. 2018, 2(3), 22; https://doi.org/10.3390/bdcc2030022
Received: 30 May 2018 / Revised: 29 July 2018 / Accepted: 31 July 2018 / Published: 2 August 2018
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Abstract
The continuous creation of data has posed new research challenges due to its complexity, diversity and volume. Consequently, Big Data has increasingly become a fully recognised scientific field. This article provides an overview of the current research efforts in Big Data science, with
[...] Read more.
The continuous creation of data has posed new research challenges due to its complexity, diversity and volume. Consequently, Big Data has increasingly become a fully recognised scientific field. This article provides an overview of the current research efforts in Big Data science, with particular emphasis on its applications, as well as theoretical foundation. Full article
(This article belongs to the Special Issue Big Data and Cognitive Computing: Feature Papers 2018)
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Open AccessArticle Traffic Sign Recognition based on Synthesised Training Data
Big Data Cogn. Comput. 2018, 2(3), 19; https://doi.org/10.3390/bdcc2030019
Received: 29 May 2018 / Revised: 18 July 2018 / Accepted: 24 July 2018 / Published: 27 July 2018
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Abstract
To deal with the richness in visual appearance variation found in real-world data, we propose to synthesise training data capturing these differences for traffic sign recognition. The use of synthetic training data, created from road traffic sign templates, allows overcoming the problems of
[...] Read more.
To deal with the richness in visual appearance variation found in real-world data, we propose to synthesise training data capturing these differences for traffic sign recognition. The use of synthetic training data, created from road traffic sign templates, allows overcoming the problems of existing traffic sing recognition databases, which are only subject to specific sets of road signs found explicitly in countries or regions. This approach is used for generating a database of synthesised images depicting traffic signs under different view-light conditions and rotations, in order to simulate the complexity of real-world scenarios. With our synthesised data and a robust end-to-end Convolutional Neural Network (CNN), we propose a data-driven, traffic sign recognition system that can achieve not only high recognition accuracy, but also high computational efficiency in both training and recognition processes. Full article
(This article belongs to the Special Issue Big Data and Cognitive Computing: Feature Papers 2018)
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Open AccessArticle Adaptive Provisioning of Heterogeneous Cloud Resources for Big Data Processing
Big Data Cogn. Comput. 2018, 2(3), 15; https://doi.org/10.3390/bdcc2030015
Received: 31 May 2018 / Revised: 5 July 2018 / Accepted: 9 July 2018 / Published: 12 July 2018
Cited by 1 | PDF Full-text (640 KB) | HTML Full-text | XML Full-text
Abstract
Efficient utilization of resources plays an important role in the performance of large scale task processing. In cases where heterogeneous types of resources are used within the same application, it is hard to achieve good utilization of all of the different types of
[...] Read more.
Efficient utilization of resources plays an important role in the performance of large scale task processing. In cases where heterogeneous types of resources are used within the same application, it is hard to achieve good utilization of all of the different types of resources. By taking advantage of recent developments in cloud infrastructure that enable the use of dynamic clusters of resources, and by dynamically altering the size of the available resources for all the different resource types, the overall utilization of resources, however, can be improved. Starting from this premise, this paper discusses a solution that aims to provide a generic algorithm to estimate the desired ratios of instance processing tasks as well as ratios of the resources that are used by these instances, without the necessity for trial runs or a priori knowledge of the execution steps. These ratios are then used as part of an adaptive system that is able to reconfigure itself to maximize utilization. To verify the solution, a reference framework which adaptively manages clusters of functionally different VMs to host a calculation scenario is implemented. Experiments are conducted based on a compute-heavy use case in which the probability of underground pipeline failures is determined based on the settlement of soils. These experiments show that the solution is capable of eliminating large amounts of under-utilization, resulting in increased throughput and lower lead times. Full article
(This article belongs to the Special Issue Big Data and Cognitive Computing: Feature Papers 2018)
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Open AccessFeature PaperArticle The Development of Data Science: Implications for Education, Employment, Research, and the Data Revolution for Sustainable Development
Big Data Cogn. Comput. 2018, 2(2), 14; https://doi.org/10.3390/bdcc2020014
Received: 28 May 2018 / Revised: 16 June 2018 / Accepted: 16 June 2018 / Published: 19 June 2018
PDF Full-text (631 KB) | HTML Full-text | XML Full-text
Abstract
In Data Science, we are concerned with the integration of relevant sciences in observed and empirical contexts. This results in the unification of analytical methodologies, and of observed and empirical data contexts. Given the dynamic nature of convergence, the origins and many evolutions
[...] Read more.
In Data Science, we are concerned with the integration of relevant sciences in observed and empirical contexts. This results in the unification of analytical methodologies, and of observed and empirical data contexts. Given the dynamic nature of convergence, the origins and many evolutions of the Data Science theme are described. The following are covered in this article: the rapidly growing post-graduate university course provisioning for Data Science; a preliminary study of employability requirements, and how past eminent work in the social sciences and other areas, certainly mathematics, can be of immediate and direct relevance and benefit for innovative methodology, and for facing and addressing the ethical aspect of Big Data analytics, relating to data aggregation and scale effects. Associated also with Data Science is how direct and indirect outcomes and consequences of Data Science include decision support and policy making, and both qualitative as well as quantitative outcomes. For such reasons, the importance is noted of how Data Science builds collaboratively on other domains, potentially with innovative methodologies and practice. Further sections point towards some of the most major current research issues. Full article
(This article belongs to the Special Issue Big Data and Cognitive Computing: Feature Papers 2018)
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Review

Jump to: Research

Open AccessReview EMG Pattern Recognition in the Era of Big Data and Deep Learning
Big Data Cogn. Comput. 2018, 2(3), 21; https://doi.org/10.3390/bdcc2030021
Received: 3 July 2018 / Revised: 20 July 2018 / Accepted: 20 July 2018 / Published: 1 August 2018
PDF Full-text (376 KB) | HTML Full-text | XML Full-text
Abstract
The increasing amount of data in electromyographic (EMG) signal research has greatly increased the importance of developing advanced data analysis and machine learning techniques which are better able to handle “big data”. Consequently, more advanced applications of EMG pattern recognition have been developed.
[...] Read more.
The increasing amount of data in electromyographic (EMG) signal research has greatly increased the importance of developing advanced data analysis and machine learning techniques which are better able to handle “big data”. Consequently, more advanced applications of EMG pattern recognition have been developed. This paper begins with a brief introduction to the main factors that expand EMG data resources into the era of big data, followed by the recent progress of existing shared EMG data sets. Next, we provide a review of recent research and development in EMG pattern recognition methods that can be applied to big data analytics. These modern EMG signal analysis methods can be divided into two main categories: (1) methods based on feature engineering involving a promising big data exploration tool called topological data analysis; and (2) methods based on feature learning with a special emphasis on “deep learning”. Finally, directions for future research in EMG pattern recognition are outlined and discussed. Full article
(This article belongs to the Special Issue Big Data and Cognitive Computing: Feature Papers 2018)

Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Edge Machine Learning: Enabling Smart Internet of Things Applications

Authors: Mahmut Taha Yazici, Shadi Basurra and Mohamed Medhat Gaber
Affiliation: School of Computing and Digital Technology, Birmingham City University, Birmingham B4 7BD, UK
Abstract:  Machine learning has traditionally been the solely performed on servers and high-performance machines. But advances in chip technology have given us miniature libraries that fit in our pockets and mobile processors have vastly increased in capability narrowing the vast gulf between the simple processors embedded in such things and their more complex cousins in personal computers. Thus, with the now available tools, the opportunity has arisen to extract great value in having on-device machine learning for IoT devices. Doing ML inference on edge devices has huge potential and is still in its early stages. However, it's already more powerful than most realize. In this paper, a step forward has been taken to understand the feasibility of running Machine Learning algorithms, both training and inference, on a Raspberry Pi, an excellent system to use as the processing core of IoT devices. Three different algorithms: Random Forests, Support Vector Machine and Multi-Layer Perceptron respectively, have been tested using ten diverse data sets on the Raspberry Pi to observe their performance in terms of speed, accuracy, and power consumption. As a result of the conducted tests, the SVM algorithm proved to be slightly faster in speed and more efficient in power consumption, but the Random Forest algorithm took over in accuracy. In addition to the performance results, we will discuss their usability scenarios and the idea of implementing more complex and taxing algorithms such as Deep Learning on these small devices in more details.

 

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