Special Issue "Big Data and Cognitive Computing: Feature Papers 2020"

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

Deadline for manuscript submissions: closed (31 December 2020).

Special Issue Editors

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

Special Issue Information

Dear Colleagues,

Cognitive computing allows reducing the complexity of Big Data to help humans in decision making within the vast and growing seas of Internet of Things. This Special Issue will include 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 topics:

Big data, Clouds, Edge, 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
  • Edge computing and IoT data mining
  • 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. Giancarlo Fortino
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) for publication in this open access journal is 1400 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
  • cognitive computing
  • software agents
  • data analytics
  • smart internet of things applications
  • machine and deep learning

Published Papers (8 papers)

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Research

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Article
A Network-Based Analysis of a Worksite Canteen Dataset
Big Data Cogn. Comput. 2021, 5(1), 11; https://doi.org/10.3390/bdcc5010011 - 08 Mar 2021
Viewed by 2859
Abstract
The provision of wellness in workplaces gained interest in recent decades. A factor that contributes significantly to workers’ health is their diet, especially when provided by canteen services. The assessment of such a service involves questions as food cost, its sustainability, quality, nutritional [...] Read more.
The provision of wellness in workplaces gained interest in recent decades. A factor that contributes significantly to workers’ health is their diet, especially when provided by canteen services. The assessment of such a service involves questions as food cost, its sustainability, quality, nutritional facts and variety, as well as employees’ health and disease prevention, productivity increase, economic convenience vs. eating satisfaction when using canteen services. Even if food habits have already been studied using traditional statistical approaches, here we adopt an approach based on Network Science that allows us to deeply study, for instance, the interconnections among people, company and meals and that can be easily used for further analysis. In particular, this work concerns a multi-company dataset of workers and dishes they chose at a canteen worksite. We study eating habits and health consequences, also considering the presence of different companies and the corresponding contact network among workers. The macro-nutrient content and caloric values assessment is carried out both for dishes and for employees, in order to establish when food is balanced and healthy. Moreover, network analysis lets us discover hidden correlations among people and the environment, as communities that cannot be usually inferred with traditional or methods since they are not known a priori. Finally, we represent the dataset as a tripartite network to investigate relationships between companies, people, and dishes. In particular, the so-called network projections can be extracted, each one being a network among specific kind of nodes; further community analysis tools will provide hidden information about people and their food habits. In summary, the contribution of the paper is twofold: it provides a study of a real dataset spanning over several years that gives a new interesting point of view on food habits and healthcare, and it also proposes a new approach based on Network Science. Results prove that this kind of analysis can provide significant information that complements other traditional methodologies. Full article
(This article belongs to the Special Issue Big Data and Cognitive Computing: Feature Papers 2020)
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Article
Big Data and Personalisation for Non-Intrusive Smart Home Automation
Big Data Cogn. Comput. 2021, 5(1), 6; https://doi.org/10.3390/bdcc5010006 - 30 Jan 2021
Cited by 1 | Viewed by 2526
Abstract
With the advent of the Internet of Things (IoT), many different smart home technologies are commercially available. However, the adoption of such technologies is slow as many of them are not cost-effective and focus on specific functions such as energy efficiency. Recently, IoT [...] Read more.
With the advent of the Internet of Things (IoT), many different smart home technologies are commercially available. However, the adoption of such technologies is slow as many of them are not cost-effective and focus on specific functions such as energy efficiency. Recently, IoT devices and sensors have been designed to enhance the quality of personal life by having the capability to generate continuous data streams that can be used to monitor and make inferences by the user. While smart home devices connect to the home Wi-Fi network, there are still compatibility issues between devices from different manufacturers. Smart devices get even smarter when they can communicate with and control each other. The information collected by one device can be shared with others for achieving an enhanced automation of their operations. This paper proposes a non-intrusive approach of integrating and collecting data from open standard IoT devices for personalised smart home automation using big data analytics and machine learning. We demonstrate the implementation of our proposed novel technology instantiation approach for achieving non-intrusive IoT based big data analytics with a use case of a smart home environment. We employ open-source frameworks such as Apache Spark, Apache NiFi and FB-Prophet along with popular vendor tech-stacks such as Azure and DataBricks. Full article
(This article belongs to the Special Issue Big Data and Cognitive Computing: Feature Papers 2020)
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Article
An Exploratory Study of COVID-19 Information on Twitter in the Greater Region
Big Data Cogn. Comput. 2021, 5(1), 5; https://doi.org/10.3390/bdcc5010005 - 28 Jan 2021
Cited by 4 | Viewed by 2490
Abstract
The outbreak of the COVID-19 led to a burst of information in major online social networks (OSNs). Facing this constantly changing situation, OSNs have become an essential platform for people expressing opinions and seeking up-to-the-minute information. Thus, discussions on OSNs may become a [...] Read more.
The outbreak of the COVID-19 led to a burst of information in major online social networks (OSNs). Facing this constantly changing situation, OSNs have become an essential platform for people expressing opinions and seeking up-to-the-minute information. Thus, discussions on OSNs may become a reflection of reality. This paper aims to figure out how Twitter users in the Greater Region (GR) and related countries react differently over time through conducting a data-driven exploratory study of COVID-19 information using machine learning and representation learning methods. We find that tweet volume and COVID-19 cases in GR and related countries are correlated, but this correlation only exists in a particular period of the pandemic. Moreover, we plot the changing of topics in each country and region from 22 January 2020 to 5 June 2020, figuring out the main differences between GR and related countries. Full article
(This article belongs to the Special Issue Big Data and Cognitive Computing: Feature Papers 2020)
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Article
NLP-Based Customer Loyalty Improvement Recommender System (CLIRS2)
Big Data Cogn. Comput. 2021, 5(1), 4; https://doi.org/10.3390/bdcc5010004 - 19 Jan 2021
Viewed by 2241
Abstract
Structured data on customer feedback is becoming more costly and timely to collect and organize. On the other hand, unstructured opinionated data, e.g., in the form of free-text comments, is proliferating and available on public websites, such as social media websites, blogs, forums, [...] Read more.
Structured data on customer feedback is becoming more costly and timely to collect and organize. On the other hand, unstructured opinionated data, e.g., in the form of free-text comments, is proliferating and available on public websites, such as social media websites, blogs, forums, and websites that provide recommendations. This research proposes a novel method to develop a knowledge-based recommender system from unstructured (text) data. The method is based on applying an opinion mining algorithm, extracting aspect-based sentiment score per text item, and transforming text into a structured form. An action rule mining algorithm is applied to the data table constructed from sentiment mining. The proposed application of the method is the problem of improving customer satisfaction ratings. The results obtained from the dataset of customer comments related to the repair services were evaluated with accuracy and coverage. Further, the results were incorporated into the framework of a web-based user-friendly recommender system to advise the business on how to maximally increase their profits by introducing minimal sets of changes in their service. Experiments and evaluation results from comparing the structured data-based version of the system CLIRS (Customer Loyalty Improvement Recommender System) with the unstructured data-based version of the system (CLIRS2) are provided. Full article
(This article belongs to the Special Issue Big Data and Cognitive Computing: Feature Papers 2020)
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Article
eGAP: An Evolutionary Game Theoretic Approach to Random Forest Pruning
Big Data Cogn. Comput. 2020, 4(4), 37; https://doi.org/10.3390/bdcc4040037 - 28 Nov 2020
Viewed by 2163
Abstract
To make healthcare available and easily accessible, the Internet of Things (IoT), which paved the way to the construction of smart cities, marked the birth of many smart applications in numerous areas, including healthcare. As a result, smart healthcare applications have been and [...] Read more.
To make healthcare available and easily accessible, the Internet of Things (IoT), which paved the way to the construction of smart cities, marked the birth of many smart applications in numerous areas, including healthcare. As a result, smart healthcare applications have been and are being developed to provide, using mobile and electronic technology, higher diagnosis quality of the diseases, better treatment of the patients, and improved quality of lives. Since smart healthcare applications that are mainly concerned with the prediction of healthcare data (like diseases for example) rely on predictive healthcare data analytics, it is imperative for such predictive healthcare data analytics to be as accurate as possible. In this paper, we will exploit supervised machine learning methods in classification and regression to improve the performance of the traditional Random Forest on healthcare datasets, both in terms of accuracy and classification/regression speed, in order to produce an effective and efficient smart healthcare application, which we have termed eGAP. eGAP uses the evolutionary game theoretic approach replicator dynamics to evolve a Random Forest ensemble. Trees of high resemblance in an initial Random Forest are clustered, and then clusters grow and shrink by adding and removing trees using replicator dynamics, according to the predictive accuracy of each subforest represented by a cluster of trees. All clusters have an initial number of trees that is equal to the number of trees in the smallest cluster. Cluster growth is performed using trees that are not initially sampled. The speed and accuracy of the proposed method have been demonstrated by an experimental study on 10 classification and 10 regression medical datasets. Full article
(This article belongs to the Special Issue Big Data and Cognitive Computing: Feature Papers 2020)
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Article
Ticket Sales Prediction and Dynamic Pricing Strategies in Public Transport
Big Data Cogn. Comput. 2020, 4(4), 36; https://doi.org/10.3390/bdcc4040036 - 27 Nov 2020
Cited by 2 | Viewed by 2474
Abstract
In recent years, the demand for collective mobility services registered significant growth. In particular, the long-distance coach market underwent an important change in Europe, since FlixBus adopted a dynamic pricing strategy, providing low-cost transport services and an efficient and fast information system. This [...] Read more.
In recent years, the demand for collective mobility services registered significant growth. In particular, the long-distance coach market underwent an important change in Europe, since FlixBus adopted a dynamic pricing strategy, providing low-cost transport services and an efficient and fast information system. This paper presents a methodology, called DA4PT (Data Analytics for Public Transport), for discovering the factors that influence travelers in booking and purchasing bus tickets. Starting from a set of 3.23 million user-generated event logs of a bus ticketing platform, the methodology shows the correlation rules between booking factors and purchase of tickets. Such rules are then used to train machine learning models for predicting whether a user will buy or not a ticket. The rules are also used to define various dynamic pricing strategies with the purpose of increasing the number of tickets sales on the platform and the related amount of revenues. The methodology reaches an accuracy of 95% in forecasting the purchase of a ticket and a low variance in results. Exploiting a dynamic pricing strategy, DA4PT is able to increase the number of purchased tickets by 6% and the total revenue by 9% by showing the effectiveness of the proposed approach. Full article
(This article belongs to the Special Issue Big Data and Cognitive Computing: Feature Papers 2020)
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Article
Developing a Robust Defensive System against Adversarial Examples Using Generative Adversarial Networks
Big Data Cogn. Comput. 2020, 4(2), 11; https://doi.org/10.3390/bdcc4020011 - 22 May 2020
Cited by 1 | Viewed by 2577
Abstract
In this work, we propose a novel defense system against adversarial examples leveraging the unique power of Generative Adversarial Networks (GANs) to generate new adversarial examples for model retraining. To do so, we develop an automated pipeline using combination of pre-trained convolutional neural [...] Read more.
In this work, we propose a novel defense system against adversarial examples leveraging the unique power of Generative Adversarial Networks (GANs) to generate new adversarial examples for model retraining. To do so, we develop an automated pipeline using combination of pre-trained convolutional neural network and an external GAN, that is, Pix2Pix conditional GAN, to determine the transformations between adversarial examples and clean data, and to automatically synthesize new adversarial examples. These adversarial examples are employed to strengthen the model, attack, and defense in an iterative pipeline. Our simulation results demonstrate the success of the proposed method. Full article
(This article belongs to the Special Issue Big Data and Cognitive Computing: Feature Papers 2020)
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Review

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Review
A Review of Blockchain in Internet of Things and AI
Big Data Cogn. Comput. 2020, 4(4), 28; https://doi.org/10.3390/bdcc4040028 - 14 Oct 2020
Cited by 7 | Viewed by 3850
Abstract
The Internet of Things (IoT) represents a new technology that enables both virtual and physical objects to be connected and communicate with each other, and produce new digitized services that improve our quality of life. The IoT system provides several advantages, however, the [...] Read more.
The Internet of Things (IoT) represents a new technology that enables both virtual and physical objects to be connected and communicate with each other, and produce new digitized services that improve our quality of life. The IoT system provides several advantages, however, the current centralized architecture introduces numerous issues involving a single point of failure, security, privacy, transparency, and data integrity. These challenges are an obstacle in the way of the future developments of IoT applications. Moving the IoT into one of the distributed ledger technologies may be the correct choice to resolve these issues. Among the common and popular types of distributed ledger technologies is the blockchain. Integrating the IoT with blockchain technology can bring countless benefits. Therefore, this paper provides a comprehensive discussion of integrating the IoT system with blockchain technology. After providing the basics of the IoT system and blockchain technology, a thorough review of integrating the blockchain with the IoT system is presented by highlighting benefits of the integration and how the blockchain can resolve the issues of the IoT system. Then, the blockchain as a service for the IoT is presented to show how various features of blockchain technology can be implemented as a service for various IoT applications. This is followed by discussing the impact of integrating artificial intelligence (AI) on both IoT and blockchain. In the end, future research directions of IoT with blockchain are presented. Full article
(This article belongs to the Special Issue Big Data and Cognitive Computing: Feature Papers 2020)
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