Storage Method for Real-Time Big Data on the Internet of Things

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Internet of Things (IoT)".

Deadline for manuscript submissions: 15 September 2024 | Viewed by 4220

Special Issue Editors


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Guest Editor
School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ 85281, USA
Interests: storage systems; database systems; next-generation data infrastructure; new storage techniques

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Guest Editor
Department of Electrical and Computer Engineering, University of Miami, Coral Gables, FL 33146, USA
Interests: machine learning; IoT

Special Issue Information

Dear Colleagues,

We invite original and unpublished submissions discussing your research to the Special Issue on "Storage Methods for Real-Time Big Data on Internet of Things", with a focus on storage systems, databases, data processing systems, and data platforms for data in IoT.

A large volume of data is generated from the devices and sensors from Internet-of-Things (IoT) systems, which have rapidly evolved and are transforming the way we live, work, and interact with our environments. How to efficiently process, store, and manage such a large volume of real-time data is challenging but has not been well studied. The increasing amount of data generated by IoT devices requires scalable and reliable storage solutions that can handle the volume, velocity, and variety of data. At the same time, the real-time nature of IoT data requires low latency, high performance, and good cost effectiveness to enable quick decision making, data processing, data mining, and AI support.

This Special Issue aims to gather contributions to IoT data storage from both academia and industry. Thereby, the Special Issue will advance with novel approaches, solid system designs and evaluations, creative data processing with AI/ML, novel attacks or state-of-the-art surveys related to the data persistency, protection, processing, and management for real-time big data in IoT. Research areas covered by the Special Issue may include (but are not restricted to) the following:

  • File systems
  • Cloud storage systems
  • NoSQL databases for big data
  • Timeseries databases
  • Edge computing and data processing
  • Embedded storage engines
  • Data transmission and caching for IoT
  • Real-time data processing
  • In-storage, in-network, and in-memory processing
  • Data structure and indexing for big data in IoT
  • Data security and privacy in IoT
  • IoT data-related workload characterization and modeling
  • Distributed storage and processing systems for IoT data
  • Efficient data storage and process method design
  • IoT data analysis, modeling, and measurement
  • Distributed AI for data storage and processing
  • Joint IoT data transmission and storage optimization
  • Experimental testbeds for distributed storage systems
  • Privacy and security issues for distributed storage systems
  • Data storage in emerging applications, such as autonomous vehicle systems and virtual reality systems

We look forward to receiving your contributions.

Dr. Zhichao Cao
Dr. Mingzhe Chen
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. Information 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 1600 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.

Published Papers (2 papers)

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26 pages, 5277 KiB  
Article
Towards a Conceptual Framework for Data Management in Business Intelligence
by Ramakolote Judas Mositsa, John Andrew Van der Poll and Cyrille Dongmo
Information 2023, 14(10), 547; https://doi.org/10.3390/info14100547 - 6 Oct 2023
Cited by 1 | Viewed by 2080
Abstract
Business intelligence (BI) refers to technologies, tools, and practices for collecting, integrating, analyzing, and presenting large volumes of information to enable improved decision-making. A modern BI architecture typically consists of a data warehouse made up of one or more data marts that consolidate [...] Read more.
Business intelligence (BI) refers to technologies, tools, and practices for collecting, integrating, analyzing, and presenting large volumes of information to enable improved decision-making. A modern BI architecture typically consists of a data warehouse made up of one or more data marts that consolidate data from several operational databases. BI further incorporates a combination of analytics, data management, and reporting tools, together with associated methodologies for managing and analyzing data. An important goal of BI initiatives is to improve business decision-making for organizations to increase revenue, improve operational efficiency, and gain a competitive advantage. In this article, we analyze qualitatively various prominent business intelligence (BI) frameworks in the literature and develop a comprehensive BI framework from these. Through the technique of qualitative propositions, we identify the properties, respective advantages, and possible disadvantages of the said BI frameworks to develop a comprehensive framework aimed mainly at data management, incorporating the advantages and eliminating the disadvantages of the individual frameworks. The BI landscape is vast, so as a limitation, we note that the new framework is conceptual; hence, no implementation or any quantitative measurement is performed at this stage. That said, our work exhibits originality since it combines numerous BI frameworks into a comprehensive framework, thereby contributing to conceptual BI framework development. As part of future work, the new framework will be formally specified, followed by a practical phase, namely, conducting case studies in the industry to assist companies in their BI applications. Full article
(This article belongs to the Special Issue Storage Method for Real-Time Big Data on the Internet of Things)
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Review

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36 pages, 1541 KiB  
Review
Storage Standards and Solutions, Data Storage, Sharing, and Structuring in Digital Health: A Brazilian Case Study
by Nicollas Rodrigues de Oliveira, Yago de Rezende dos Santos, Ana Carolina Rocha Mendes, Guilherme Nunes Nasseh Barbosa, Marcela Tuler de Oliveira, Rafael Valle, Dianne Scherly Varela Medeiros and Diogo M. F. Mattos
Information 2024, 15(1), 20; https://doi.org/10.3390/info15010020 - 29 Dec 2023
Cited by 1 | Viewed by 1817
Abstract
The COVID-19 pandemic has highlighted the necessity for agile health services that enable reliable and secure information exchange, but achieving proper, private, and secure sharing of EMRs remains a challenge due to diverse data formats and fragmented records across multiple data silos, resulting [...] Read more.
The COVID-19 pandemic has highlighted the necessity for agile health services that enable reliable and secure information exchange, but achieving proper, private, and secure sharing of EMRs remains a challenge due to diverse data formats and fragmented records across multiple data silos, resulting in hindered coordination between healthcare teams, potential medical errors, and delays in patient care. While centralized EMR systems pose privacy risks and data format diversity complicates interoperability, blockchain technology offers a promising solution by providing decentralized storage, ensuring data integrity, enhancing access control, eliminating intermediaries, and increasing efficiency in healthcare. By focusing on a Brazilian case study, this paper explores the significance of EMR standards, security challenges, and blockchain-based approaches to promote interoperability and secure data sharing in the healthcare industry. Full article
(This article belongs to the Special Issue Storage Method for Real-Time Big Data on the Internet of Things)
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