Special Issue "Feature Papers in Big Data"

A special issue of Informatics (ISSN 2227-9709). This special issue belongs to the section "Big Data Mining and Analytics".

Deadline for manuscript submissions: 31 July 2021.

Special Issue Editor

Dr. Weitian Tong
E-Mail Website
Guest Editor
Department of Computer Science, Eastern Michigan University, Ypsilanti, USA
Interests: big data processing; design of efficient algorithms; smart city; operations research

Special Issue Information

Dear Colleagues,

Big data is a area that treats ways to analyze, systematically extract information from, or otherwise deal with data sets that are too large or complex to be dealt with by traditional data-processing application software. Now, Big Data are being significantly used in the field of healthcare, international development, education, media, insurance, Internet of Things (IoT), etc.

This Special Issue in Informatics welcomes papers in the field of big data. The scope includes but is not limited to data capture and storage; search, sharing, and analytics; big data technologies; data visualization; architectures for massively parallel processing; data mining tools and techniques; machine learning algorithms for Big Data; cloud computing platforms; distributed file systems and databases; and scalable storage systems. New submitted papers must be pre-peer-reviewed by the Editorial Board. If your paper is well prepared and approved for further publication, you might be eligible for discounts for your publication.

Dr. Weitian Tong
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 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. Informatics 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

  • data capture and storage
  • big data technologies
  • data visualization
  • data mining tools and techniques
  • machine learning algorithms for big data
  • cloud computing platforms
  • distributed file systems and databases
  • scalable storage systems

Published Papers (3 papers)

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Research

Open AccessArticle
Deep Learning and Parallel Processing Spatio-Temporal Clustering Unveil New Ionian Distinct Seismic Zone
Informatics 2020, 7(4), 39; https://0-doi-org.brum.beds.ac.uk/10.3390/informatics7040039 - 29 Sep 2020
Cited by 2 | Viewed by 675
Abstract
This research work employs theoretical and empirical expert knowledge in constructing an agglomerative parallel processing algorithm that performs spatio-temporal clustering upon seismic data. This is made possible by exploiting the spatial and temporal sphere of influence of the main earthquakes solely, clustering seismic [...] Read more.
This research work employs theoretical and empirical expert knowledge in constructing an agglomerative parallel processing algorithm that performs spatio-temporal clustering upon seismic data. This is made possible by exploiting the spatial and temporal sphere of influence of the main earthquakes solely, clustering seismic events into a number of fuzzy bordered, interactive and yet potentially distinct seismic zones. To evaluate whether the unveiled clusters indeed depict a distinct seismic zone, deep learning neural networks are deployed to map seismic energy release rates with time intervals between consecutive large earthquakes. Such a correlation fails should there be influence by neighboring seismic areas, hence casting the seismic region as non-distinct, or if the extent of the seismic zone has not been captured fully. For the deep learning neural network to depict such a correlation requires a steady seismic energy input flow. To address that the western area of the Hellenic seismic arc has been selected as a test case due to the nearly constant motion of the African plate that sinks beneath the Eurasian plate at a steady yearly rate. This causes a steady flow of strain energy stored in tectonic underground faults, i.e., the seismic energy storage elements; a partial release of which, when propagated all the way to the surface, casts as an earthquake. The results are complementary two-fold with the correlation between the energy release rates and the time interval amongst large earthquakes supporting the presence of a potential distinct seismic zone in the Ionian Sea and vice versa. Full article
(This article belongs to the Special Issue Feature Papers in Big Data)
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Open AccessArticle
Exploring Casual COVID-19 Data Visualizations on Twitter: Topics and Challenges
Informatics 2020, 7(3), 35; https://0-doi-org.brum.beds.ac.uk/10.3390/informatics7030035 - 15 Sep 2020
Cited by 1 | Viewed by 2429
Abstract
Social networking sites such as Twitter have been a popular choice for people to express their opinions, report real-life events, and provide a perspective on what is happening around the world. In the outbreak of the COVID-19 pandemic, people have used Twitter to [...] Read more.
Social networking sites such as Twitter have been a popular choice for people to express their opinions, report real-life events, and provide a perspective on what is happening around the world. In the outbreak of the COVID-19 pandemic, people have used Twitter to spontaneously share data visualizations from news outlets and government agencies and to post casual data visualizations that they individually crafted. We conducted a Twitter crawl of 5409 visualizations (from the period between 14 April 2020 and 9 May 2020) to capture what people are posting. Our study explores what people are posting, what they retweet the most, and the challenges that may arise when interpreting COVID-19 data visualization on Twitter. Our findings show that multiple factors, such as the source of the data, who created the chart (individual vs. organization), the type of visualization, and the variables on the chart influence the retweet count of the original post. We identify and discuss five challenges that arise when interpreting these casual data visualizations, and discuss recommendations that should be considered by Twitter users while designing COVID-19 data visualizations to facilitate data interpretation and to avoid the spread of misconceptions and confusion. Full article
(This article belongs to the Special Issue Feature Papers in Big Data)
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Open AccessArticle
Automated Configuration of NoSQL Performance and Scalability Tactics for Data-Intensive Applications
Informatics 2020, 7(3), 29; https://0-doi-org.brum.beds.ac.uk/10.3390/informatics7030029 - 08 Aug 2020
Viewed by 1238
Abstract
This paper presents the architecture, implementation and evaluation of a middleware support layer for NoSQL storage systems. Our middleware automatically selects performance and scalability tactics in terms of application specific workloads. Enterprises are turning to NoSQL storage technologies for their data-intensive computing and [...] Read more.
This paper presents the architecture, implementation and evaluation of a middleware support layer for NoSQL storage systems. Our middleware automatically selects performance and scalability tactics in terms of application specific workloads. Enterprises are turning to NoSQL storage technologies for their data-intensive computing and analytics applications. Comprehensive benchmarks of different Big Data platforms can help drive decisions which solutions to adopt. However, selecting the best performing technology, configuring the deployment for scalability and tuning parameters at runtime for an optimal service delivery remain challenging tasks, especially when application workloads evolve over time. Our middleware solves this problem at runtime by monitoring the data growth, changes in the read-write-query mix at run-time, as well as other system metrics that are indicative of sub-optimal performance. Our middleware employs supervised machine learning on historic and current monitoring information and corresponding configurations to select the best combinations of high-level tactics and adapt NoSQL systems to evolving workloads. This work has been driven by two real world case studies with different QoS requirements. The evaluation demonstrates that our middleware can adapt to unseen workloads of data-intensive applications, and automate the configuration of different families of NoSQL systems at runtime to optimize the performance and scalability of such applications. Full article
(This article belongs to the Special Issue Feature Papers in Big Data)
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