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Big Data and Cognitive Computing: 5th Anniversary Feature Papers
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Big Data and Cognitive Computing: 5th Anniversary Feature Papers

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

Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 108157

Special Issue Editor

Prof. Dr. Min Chen

grade E-Mail Website
Guest Editor
School of Computer Science and Engineering, South China University of Technology, Guangzhou 510641, China
Interests: cognitive computing; 5G Networks; wearable computing; big data analytics; robotics; machine learning; deep learning; emotion detection; mobile edge computing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The year 2021 marks the 5th Anniversary of Big Data and Cognitive Computing, and we would be happy if you would join us in celebrating this wonderful occasion.

The first volume of Big Data and Cognitive Computing was launched in 2017. Over the years that have passed since the first issue, the journal has achieved remarkable growth in both the number of paper submissions received and number of papers published. Importantly for authors and publishers, this growth has been accompanied by a significant increase in citations.

BDCC was indexed by Scopus in 2020 and has been accepted into the Emerging Sources Citation Index in Web of Science in 2021 (ESCI). To date, BDCC has published more than 150 papers from more than 400 authors. More than 300 reviewers have submitted at least one review report. Our sincerest thanks go to our readers, authors, anonymous peer reviewers, editors, and all the people working for the journal who have contributed their efforts over the years. Without your help, we would never have achieved this milestone.

To mark this significant milestone, a Special Issue entitled “Big Data and Cognitive Computing: 5th Anniversary Feature Papers” is being launched. This Special Issue will collect original papers, high-quality review papers and a smaller number of surveying contributions that fall under the broad scope of the BDCC journal’s remit.

At the same time, we are pleased to announce that a “Best Paper Award” will be offered for entries submitted to the Special Issue. Two papers will receive this award, following a thorough evaluation by the Award Evaluation Committee.

Selection criteria:

- Originality and significance;
- Citations, downloads and views in 2021–2022;
- Data source: Web of Science, Scopus and the MDPI database.

Prizes: The winners will receive a certificate and an offer to publish a paper free of charge within one year in BDCC after the peer review. Additionally, each winner will receive a prize based on the award class;

- First Place (one paper): CHF 500;
- Second Place (one paper): CHF 300.

The winners will be announced before 31 March 2023.

Prof. Dr. Min Chen
Guest Editor

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 submissions that pass pre-check are 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 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 1800 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

  • cognitive computing
  • big data analytics
  • IoT
  • cognitive robotics
  • machine learning
  • deep learning
  • artificial intelligence
  • 5G/6G networks
  • edge intelligence

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  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
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Further information on MDPI's Special Issue polices can be found here.

Published Papers (7 papers)

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Research

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15 pages, 347 KiB  
Article
LFA: A Lévy Walk and Firefly-Based Search Algorithm: Application to Multi-Target Search and Multi-Robot Foraging
by Ouarda Zedadra, Antonio Guerrieri and Hamid Seridi
Big Data Cogn. Comput. 2022, 6(1), 22; https://doi.org/10.3390/bdcc6010022 - 21 Feb 2022
Cited by 7 | Viewed by 3766
Abstract
In the literature, several exploration algorithms have been proposed so far. Among these, Lévy walk is commonly used since it is proved to be more efficient than the simple random-walk exploration. It is beneficial when targets are sparsely distributed in the search space. [...] Read more.
In the literature, several exploration algorithms have been proposed so far. Among these, Lévy walk is commonly used since it is proved to be more efficient than the simple random-walk exploration. It is beneficial when targets are sparsely distributed in the search space. However, due to its super-diffusive behavior, some tuning is needed to improve its performance, specifically when targets are clustered. Firefly algorithm is a swarm intelligence-based algorithm useful for intensive search, but its exploration rate is very limited. An efficient and reliable search could be attained by combining the two algorithms since the first one allows exploration space, and the second one encourages its exploitation. In this paper, we propose a swarm intelligence-based search algorithm called Lévy walk and Firefly-based Algorithm (LFA), which is a hybridization of the two aforementioned algorithms. The algorithm is applied to Multi-Target Search and Multi-Robot Foraging. Numerical experiments to test the performances are conducted on the robotic simulator ARGoS. A comparison with the original firefly algorithm proves the goodness of our contribution. Full article
(This article belongs to the Special Issue Big Data and Cognitive Computing: 5th Anniversary Feature Papers)
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16 pages, 1393 KiB  
Article
Analyzing Political Polarization on Social Media by Deleting Bot Spamming
by Riccardo Cantini, Fabrizio Marozzo, Domenico Talia and Paolo Trunfio
Big Data Cogn. Comput. 2022, 6(1), 3; https://doi.org/10.3390/bdcc6010003 - 4 Jan 2022
Cited by 14 | Viewed by 7474
Abstract
Social media platforms are part of everyday life, allowing the interconnection of people around the world in large discussion groups relating to every topic, including important social or political issues. Therefore, social media have become a valuable source of information-rich data, commonly referred [...] Read more.
Social media platforms are part of everyday life, allowing the interconnection of people around the world in large discussion groups relating to every topic, including important social or political issues. Therefore, social media have become a valuable source of information-rich data, commonly referred to as Social Big Data, effectively exploitable to study the behavior of people, their opinions, moods, interests and activities. However, these powerful communication platforms can be also used to manipulate conversation, polluting online content and altering the popularity of users, through spamming activities and misinformation spreading. Recent studies have shown the use on social media of automatic entities, defined as social bots, that appear as legitimate users by imitating human behavior aimed at influencing discussions of any kind, including political issues. In this paper we present a new methodology, namely TIMBRE (Time-aware opInion Mining via Bot REmoval), aimed at discovering the polarity of social media users during election campaigns characterized by the rivalry of political factions. This methodology is temporally aware and relies on a keyword-based classification of posts and users. Moreover, it recognizes and filters out data produced by social media bots, which aim to alter public opinion about political candidates, thus avoiding heavily biased information. The proposed methodology has been applied to a case study that analyzes the polarization of a large number of Twitter users during the 2016 US presidential election. The achieved results show the benefits brought by both removing bots and taking into account temporal aspects in the forecasting process, revealing the high accuracy and effectiveness of the proposed approach. Finally, we investigated how the presence of social bots may affect political discussion by studying the 2016 US presidential election. Specifically, we analyzed the main differences between human and artificial political support, estimating also the influence of social bots on legitimate users. Full article
(This article belongs to the Special Issue Big Data and Cognitive Computing: 5th Anniversary Feature Papers)
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54 pages, 6458 KiB  
Article
6G Cognitive Information Theory: A Mailbox Perspective
by Yixue Hao, Yiming Miao, Min Chen, Hamid Gharavi and Victor C. M. Leung
Big Data Cogn. Comput. 2021, 5(4), 56; https://doi.org/10.3390/bdcc5040056 - 16 Oct 2021
Cited by 29 | Viewed by 28051
Abstract
With the rapid development of 5G communications, enhanced mobile broadband, massive machine type communications and ultra-reliable low latency communications are widely supported. However, a 5G communication system is still based on Shannon’s information theory, while the meaning and value of information itself are [...] Read more.
With the rapid development of 5G communications, enhanced mobile broadband, massive machine type communications and ultra-reliable low latency communications are widely supported. However, a 5G communication system is still based on Shannon’s information theory, while the meaning and value of information itself are not taken into account in the process of transmission. Therefore, it is difficult to meet the requirements of intelligence, customization, and value transmission of 6G networks. In order to solve the above challenges, we propose a 6G mailbox theory, namely a cognitive information carrier to enable distributed algorithm embedding for intelligence networking. Based on Mailbox, a 6G network will form an intelligent agent with self-organization, self-learning, self-adaptation, and continuous evolution capabilities. With the intelligent agent, redundant transmission of data can be reduced while the value transmission of information can be improved. Then, the features of mailbox principle are introduced, including polarity, traceability, dynamics, convergence, figurability, and dependence. Furthermore, key technologies with which value transmission of information can be realized are introduced, including knowledge graph, distributed learning, and blockchain. Finally, we establish a cognitive communication system assisted by deep learning. The experimental results show that, compared with a traditional communication system, our communication system performs less data transmission quantity and error. Full article
(This article belongs to the Special Issue Big Data and Cognitive Computing: 5th Anniversary Feature Papers)
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14 pages, 303 KiB  
Article
Traceability for Trustworthy AI: A Review of Models and Tools
by Marçal Mora-Cantallops, Salvador Sánchez-Alonso, Elena García-Barriocanal and Miguel-Angel Sicilia
Big Data Cogn. Comput. 2021, 5(2), 20; https://doi.org/10.3390/bdcc5020020 - 4 May 2021
Cited by 39 | Viewed by 14813
Abstract
Traceability is considered a key requirement for trustworthy artificial intelligence (AI), related to the need to maintain a complete account of the provenance of data, processes, and artifacts involved in the production of an AI model. Traceability in AI shares part of its [...] Read more.
Traceability is considered a key requirement for trustworthy artificial intelligence (AI), related to the need to maintain a complete account of the provenance of data, processes, and artifacts involved in the production of an AI model. Traceability in AI shares part of its scope with general purpose recommendations for provenance as W3C PROV, and it is also supported to different extents by specific tools used by practitioners as part of their efforts in making data analytic processes reproducible or repeatable. Here, we review relevant tools, practices, and data models for traceability in their connection to building AI models and systems. We also propose some minimal requirements to consider a model traceable according to the assessment list of the High-Level Expert Group on AI. Our review shows how, although a good number of reproducibility tools are available, a common approach is currently lacking, together with the need for shared semantics. Besides, we have detected that some tools have either not achieved full maturity, or are already falling into obsolescence or in a state of near abandonment by its developers, which might compromise the reproducibility of the research trusted to them. Full article
(This article belongs to the Special Issue Big Data and Cognitive Computing: 5th Anniversary Feature Papers)
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16 pages, 2188 KiB  
Article
Wine Ontology Influence in a Recommendation System
by Luís Oliveira, Rodrigo Rocha Silva and Jorge Bernardino
Big Data Cogn. Comput. 2021, 5(2), 16; https://doi.org/10.3390/bdcc5020016 - 15 Apr 2021
Cited by 1 | Viewed by 5857
Abstract
Wine is the second most popular alcoholic drink in the world behind beer. With the rise of e-commerce, recommendation systems have become a very important factor in the success of business. Recommendation systems analyze metadata to predict if, for example, a user will [...] Read more.
Wine is the second most popular alcoholic drink in the world behind beer. With the rise of e-commerce, recommendation systems have become a very important factor in the success of business. Recommendation systems analyze metadata to predict if, for example, a user will recommend a product. The metadata consist mostly of former reviews or web traffic from the same user. For this reason, we investigate what would happen if the information analyzed by a recommendation system was insufficient. In this paper, we explore the effects of a new wine ontology in a recommendation system. We created our own wine ontology and then made two sets of tests for each dataset. In both sets of tests, we applied four machine learning clustering algorithms that had the objective of predicting if a user recommends a wine product. The only difference between each set of tests is the attributes contained in the dataset. In the first set of tests, the datasets were influenced by the ontology, and in the second set, the only information about a wine product is its name. We compared the two test sets’ results and observed that there was a significant increase in classification accuracy when using a dataset with the proposed ontology. We demonstrate the general applicability of the methodology to other cases, applying our proposal to an Amazon product review dataset. Full article
(This article belongs to the Special Issue Big Data and Cognitive Computing: 5th Anniversary Feature Papers)
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Review

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29 pages, 1272 KiB  
Review
Big Data Analytics in Supply Chain Management: A Systematic Literature Review and Research Directions
by In Lee and George Mangalaraj
Big Data Cogn. Comput. 2022, 6(1), 17; https://doi.org/10.3390/bdcc6010017 - 1 Feb 2022
Cited by 68 | Viewed by 32002
Abstract
Big data analytics has been successfully used for various business functions, such as accounting, marketing, supply chain, and operations. Currently, along with the recent development in machine learning and computing infrastructure, big data analytics in the supply chain are surging in importance. In [...] Read more.
Big data analytics has been successfully used for various business functions, such as accounting, marketing, supply chain, and operations. Currently, along with the recent development in machine learning and computing infrastructure, big data analytics in the supply chain are surging in importance. In light of the great interest and evolving nature of big data analytics in supply chains, this study conducts a systematic review of existing studies in big data analytics. This study presents a framework of a systematic literature review from interdisciplinary perspectives. From the organizational perspective, this study examines the theoretical foundations and research models that explain the sustainability and performances achieved through the use of big data analytics. Then, from the technical perspective, this study analyzes types of big data analytics, techniques, algorithms, and features developed for enhanced supply chain functions. Finally, this study identifies the research gap and suggests future research directions. Full article
(This article belongs to the Special Issue Big Data and Cognitive Computing: 5th Anniversary Feature Papers)
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21 pages, 476 KiB  
Review
A Review of Artificial Intelligence, Big Data, and Blockchain Technology Applications in Medicine and Global Health
by Supriya M. and Vijay Kumar Chattu
Big Data Cogn. Comput. 2021, 5(3), 41; https://doi.org/10.3390/bdcc5030041 - 6 Sep 2021
Cited by 64 | Viewed by 14498
Abstract
Artificial intelligence (AI) programs are applied to methods such as diagnostic procedures, treatment protocol development, patient monitoring, drug development, personalized medicine in healthcare, and outbreak predictions in global health, as in the case of the current COVID-19 pandemic. Machine learning (ML) is a [...] Read more.
Artificial intelligence (AI) programs are applied to methods such as diagnostic procedures, treatment protocol development, patient monitoring, drug development, personalized medicine in healthcare, and outbreak predictions in global health, as in the case of the current COVID-19 pandemic. Machine learning (ML) is a field of AI that allows computers to learn and improve without being explicitly programmed. ML algorithms can also analyze large amounts of data called Big data through electronic health records for disease prevention and diagnosis. Wearable medical devices are used to continuously monitor an individual’s health status and store it in cloud computing. In the context of a newly published study, the potential benefits of sophisticated data analytics and machine learning are discussed in this review. We have conducted a literature search in all the popular databases such as Web of Science, Scopus, MEDLINE/PubMed and Google Scholar search engines. This paper describes the utilization of concepts underlying ML, big data, blockchain technology and their importance in medicine, healthcare, public health surveillance, case estimations in COVID-19 pandemic and other epidemics. The review also goes through the possible consequences and difficulties for medical practitioners and health technologists in designing futuristic models to improve the quality and well-being of human lives. Full article
(This article belongs to the Special Issue Big Data and Cognitive Computing: 5th Anniversary Feature Papers)
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