Special Issue "Spatiotemporal Big Data Analytics"

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: 30 September 2021.

Special Issue Editors

Prof. Rage Uday Kiran
Website
Guest Editor
Database Systems Lab, The University of Aizu, Fukushima, 965-8580, Japan
Interests: data mining; artificial intelligence; recommender systems
Prof. Philippe Fournier-Viger

Guest Editor
School of Computer Science, Harbin Institute of Technology (Shenzhen), Shenzhen 518000, China
Interests: data mining; big data; artificial intelligence; pattern mining; itemset mining; graph mining; sequence prediction
Prof. José María Luna
Website
Guest Editor
Department of Computer Science and Numerical Analysis, University of Córdoba, 14071 Córdoba, Andalucía, Spain
Interests: pattern mining; application of data mining in education
Special Issues and Collections in MDPI journals
Prof. Agarwal Sonali

Guest Editor
Department of Indian Institute of Information Technology (IIIT), Allahabad, Jhalwa, Prayagraj 211015, India
Interests: stream analytics; big data; stream data mining; complex event processing system; support vector machines; software engineering

Special Issue Information

Dear Colleagues,

In many fields, data contains both space and time information. However, jointly considering both dimensions in data analysis raises new challenges that many traditional data mining and machine learning techniques are unable to cope with. Mining spatiotemporal databases can provide useful insights for many real-world applications such as e-commerce, Internet of Things, agriculture, healthcare, intelligent transportation systems, meteorology, and astronomy. For instance, in the intelligent transportation systems, spatiotemporal big data analytics can help to detect, control, and monitor the set of road segments in which congestion may regularly happen in a transportation network. In meteorology, spatiotemporal big data analytics can help to detect the geographical regions which are regularly prone to droughts. In the Internet of Things, spatiotemporal big data analytics can help to detect, control, and monitor the nearby areas where people are regularly exposed to harmful levels of air pollution.  

This Special Issue is intended to report high-quality research on recent advances on spatiotemporal big data analytics, more specifically the state-of-the-art algorithms, models, methodologies, and systems for handling spatiotemporal data. Authors are solicited to submit unpublished or extended version of conference papers, related but not limited to the following topics of interest:

  • Visionary papers on Society 5.0/Industry 4.0 applications
  • Mining spatiotemporal databases
  • Mining spatiotemporal data streams
  • Mining uncertain spatiotemporal data
  • Spatiotemporal trajectory analytics
  • Spatiotemporal multimedia analytics
  • Machine learning/deep learning of spatiotemporal data
  • Advanced data analytical methods on spatiotemporal data
  • Analytics of meteorological datasets
  • Analytics of astronomical big data
  • Mining lifelong data
  • Optimizing machine learning algorithms for spatiotemporal big data
  • Energy efficient mining of spatiotemporal big data
  • User interfaces for spatiotemporal applications
  • Multi-core and distributed mining algorithms for spatiotemporal big data analytics
  • Decision support systems
  • Intelligent transportation systems
  • Case studies

Prof. Rage Uday Kiran
Prof. Philippe Fournier-Viger
Prof. José María Luna
Prof. Agarwal Sonali
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. Electronics is an international peer-reviewed open access semimonthly 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

  • Visionary papers on Society 5.0/Industry 4.0 applications
  • Mining spatiotemporal databases
  • Mining spatiotemporal data streams
  • Mining uncertain spatiotemporal data
  • Spatiotemporal trajectory analytics
  • Spatiotemporal multimedia analytics
  • Machine learning/deep learning of spatiotemporal data
  • Advanced data analytical methods on spatiotemporal data
  • Analytics of meteorological datasets
  • Analytics of astronomical big data
  • Mining lifelong data
  • Optimizing machine learning algorithms for spatiotemporal big da-ta
  • Energy efficient mining of spatiotemporal big data
  • User interfaces for spatiotemporal applications
  • Multi-core and distributed mining algorithms for spatiotemporal big data analytics
  • Decision support systems
  • Intelligent transportation systems
  • Case studies

Published Papers

This special issue is now open for submission.
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