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Advanced Data Mining Techniques for IoT and Big Data
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Advanced Data Mining Techniques for IoT and Big Data

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

Deadline for manuscript submissions: closed (20 May 2022) | Viewed by 20056

Special Issue Editors

Prof. Dr. Elisa Quintarelli

E-Mail Website
Guest Editor
Department of Computer Science, University of Verona, 37129 Verona, Italy
Interests: data management; big data and analytics; database exploration; data overload reduction; data mining
Special Issues, Collections and Topics in MDPI journals
Dr. Sara Migliorini

E-Mail Website
Guest Editor
Department of Computer Science, University of Verona, 37134 Verona, Italy
Interests: data management; spatiotemporal information systems; big data and analytics; collaborative and distributed architectures; blockchain technology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the big data era, effective cloud systems, web services, and data centers must be designed to discover, store, and process a massive amount of data.

Indeed, the availability of the Internet makes it possible to easily connect various devices that can communicate with each other and share data: The Internet of Things (IoT) is now a paradigm that allows users to connect various sensors and smart devices to collect real-time data from the environment. 

Once data have been collected, advanced learning techniques must be applied to learn, analyze, and predict implicit knowledge from previously stored data.

Data mining algorithms, and more in general machine learning methods, can be applied to retrieve hidden, valid, and potentially useful patterns in huge data sets and to discover unknown relationships amongst the data coming from IoT devices or from the Web (e.g., they can help to provide intelligent web services using knowledge about user behaviors and interests).

This Special Issue focuses on the design, implementation, and validation of advanced machine learning methods for big datasets or the IoT scenario.

The topics of interest include but are not limited to:

  • Big data, clouds, and Internet of Things (IoT);
  • Cloud services and applications;
  • Data mining for IoT;
  • Pattern mining;
  • Service discovery process;
  • Web service recommendations;
  • Web mining;
  • Predictive analysis;
  • Data analytics;
  • Machine learning.

Prof. Dr. Elisa Quintarelli
Dr. Sara Migliorini
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 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

  • Big data, clouds, and Internet of Things (IoT)
  • Cloud services and applications
  • Data mining for IoT Pattern mining
  • Service discovery process
  • Web service recommendations
  • Web mining
  • Predictive analysis
  • Data analytics
  • Machine learning

Published Papers (4 papers)

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Research

21 pages, 3495 KiB  
Article
Innovative Business Process Reengineering Adoption: Framework of Big Data Sentiment, Improving Customers’ Service Level Agreement
by Heru Susanto, Aida Sari and Fang-Yie Leu
Big Data Cogn. Comput. 2022, 6(4), 151; https://0-doi-org.brum.beds.ac.uk/10.3390/bdcc6040151 - 08 Dec 2022
Cited by 2 | Viewed by 2447
Abstract
Social media is now regarded as the most valuable source of data for trend analysis and innovative business process reengineering preferences. Data made accessible through social media can be utilized for a variety of purposes, such as by an entrepreneur who wants to [...] Read more.
Social media is now regarded as the most valuable source of data for trend analysis and innovative business process reengineering preferences. Data made accessible through social media can be utilized for a variety of purposes, such as by an entrepreneur who wants to learn more about the market they intend to enter and uncover their consumers’ requirements before launching their new products or services. Sentiment analysis and text mining of telecommunication businesses via social media posts and comments are the subject of this study. A proposed framework will be utilized as a guideline, and it will be tested for sentiment analysis. Lexicon-based sentiment categorization is used as a model training dataset for a supervised machine learning support vector machine. The result is very promising. The accuracy and the quantity of the true sentiments it can detect are compared. This result signifies the usefulness of text mining and sentiment analysis on social media data, while the use of machine learning classifiers for predicting sentiment orientation provides a useful tool for operations and marketing departments. The availability of large amounts of data in this digitally active society is advantageous for sectors such as the telecommunication industry. These companies can be two steps ahead with their strategy and develop a more cohesive company that can make customers happier and mitigate problems easily with the use of text mining and sentiment analysis for further adopting innovative business process reengineering for service improvements within the telecommunications industry. Full article
(This article belongs to the Special Issue Advanced Data Mining Techniques for IoT and Big Data)
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28 pages, 3163 KiB  
Article
Indoor Localization for Personalized Ambient Assisted Living of Multiple Users in Multi-Floor Smart Environments
by Nirmalya Thakur and Chia Y. Han
Big Data Cogn. Comput. 2021, 5(3), 42; https://0-doi-org.brum.beds.ac.uk/10.3390/bdcc5030042 - 08 Sep 2021
Cited by 21 | Viewed by 4153
Abstract
This paper presents a multifunctional interdisciplinary framework that makes four scientific contributions towards the development of personalized ambient assisted living (AAL), with a specific focus to address the different and dynamic needs of the diverse aging population in the future of smart living [...] Read more.
This paper presents a multifunctional interdisciplinary framework that makes four scientific contributions towards the development of personalized ambient assisted living (AAL), with a specific focus to address the different and dynamic needs of the diverse aging population in the future of smart living environments. First, it presents a probabilistic reasoning-based mathematical approach to model all possible forms of user interactions for any activity arising from user diversity of multiple users in such environments. Second, it presents a system that uses this approach with a machine learning method to model individual user-profiles and user-specific user interactions for detecting the dynamic indoor location of each specific user. Third, to address the need to develop highly accurate indoor localization systems for increased trust, reliance, and seamless user acceptance, the framework introduces a novel methodology where two boosting approaches—Gradient Boosting and the AdaBoost algorithm are integrated and used on a decision tree-based learning model to perform indoor localization. Fourth, the framework introduces two novel functionalities to provide semantic context to indoor localization in terms of detecting each user’s floor-specific location as well as tracking whether a specific user was located inside or outside a given spatial region in a multi-floor-based indoor setting. These novel functionalities of the proposed framework were tested on a dataset of localization-related Big Data collected from 18 different users who navigated in 3 buildings consisting of 5 floors and 254 indoor spatial regions, with an to address the limitation in prior works in this field centered around the lack of training data from diverse users. The results show that this approach of indoor localization for personalized AAL that models each specific user always achieves higher accuracy as compared to the traditional approach of modeling an average user. The results further demonstrate that the proposed framework outperforms all prior works in this field in terms of functionalities, performance characteristics, and operational features. Full article
(This article belongs to the Special Issue Advanced Data Mining Techniques for IoT and Big Data)
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20 pages, 1241 KiB  
Article
Big Data Research in Fighting COVID-19: Contributions and Techniques
by Dianadewi Riswantini, Ekasari Nugraheni, Andria Arisal, Purnomo Husnul Khotimah, Devi Munandar and Wiwin Suwarningsih
Big Data Cogn. Comput. 2021, 5(3), 30; https://0-doi-org.brum.beds.ac.uk/10.3390/bdcc5030030 - 12 Jul 2021
Cited by 12 | Viewed by 7011
Abstract
The COVID-19 pandemic has induced many problems in various sectors of human life. After more than one year of the pandemic, many studies have been conducted to discover various technological innovations and applications to combat the virus that has claimed many lives. The [...] Read more.
The COVID-19 pandemic has induced many problems in various sectors of human life. After more than one year of the pandemic, many studies have been conducted to discover various technological innovations and applications to combat the virus that has claimed many lives. The use of Big Data technology to mitigate the threats of the pandemic has been accelerated. Therefore, this survey aims to explore Big Data technology research in fighting the pandemic. Furthermore, the relevance of Big Data technology was analyzed while technological contributions to five main areas were highlighted. These include healthcare, social life, government policy, business and management, and the environment. The analytical techniques of machine learning, deep learning, statistics, and mathematics were discussed to solve issues regarding the pandemic. The data sources used in previous studies were also presented and they consist of government officials, institutional service, IoT generated, online media, and open data. Therefore, this study presents the role of Big Data technologies in enhancing the research relative to COVID-19 and provides insights into the current state of knowledge within the domain and references for further development or starting new studies are provided. Full article
(This article belongs to the Special Issue Advanced Data Mining Techniques for IoT and Big Data)
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16 pages, 1425 KiB  
Article
A Dynamic Intelligent Policies Analysis Mechanism for Personal Data Processing in the IoT Ecosystem
by Konstantinos Demertzis, Konstantinos Rantos and George Drosatos
Big Data Cogn. Comput. 2020, 4(2), 9; https://0-doi-org.brum.beds.ac.uk/10.3390/bdcc4020009 - 27 Apr 2020
Cited by 8 | Viewed by 5180
Abstract
The evolution of the Internet of Things is significantly affected by legal restrictions imposed for personal data handling, such as the European General Data Protection Regulation (GDPR). The main purpose of this regulation is to provide people in the digital age greater control [...] Read more.
The evolution of the Internet of Things is significantly affected by legal restrictions imposed for personal data handling, such as the European General Data Protection Regulation (GDPR). The main purpose of this regulation is to provide people in the digital age greater control over their personal data, with their freely given, specific, informed and unambiguous consent to collect and process the data concerning them. ADVOCATE is an advanced framework that fully complies with the requirements of GDPR, which, with the extensive use of blockchain and artificial intelligence technologies, aims to provide an environment that will support users in maintaining control of their personal data in the IoT ecosystem. This paper proposes and presents the Intelligent Policies Analysis Mechanism (IPAM) of the ADVOCATE framework, which, in an intelligent and fully automated manner, can identify conflicting rules or consents of the user, which may lead to the collection of personal data that can be used for profiling. In order to clearly identify and implement IPAM, the problem of recording user data from smart entertainment devices using Fuzzy Cognitive Maps (FCMs) was simulated. FCMs are an intelligent decision-making system that simulates the processes of a complex system, modeling the correlation base, knowing the behavioral and balance specialists of the system. Respectively, identifying conflicting rules that can lead to a profile, training is done using Extreme Learning Machines (ELMs), which are highly efficient neural systems of small and flexible architecture that can work optimally in complex environments. Full article
(This article belongs to the Special Issue Advanced Data Mining Techniques for IoT and Big Data)
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