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Electronics
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Spatiotemporal Big Data Analytics
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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: closed (30 November 2021) | Viewed by 7914

Special Issue Editors

Prof. Rage Uday Kiran

E-Mail Website
Guest Editor
Database Systems Lab, The University of Aizu, Fukushima 965-8580, Japan
Interests: data mining; artificial intelligence; recommender systems
Special Issues, Collections and Topics in MDPI journals
Prof. Philippe Fournier-Viger

E-Mail
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

E-Mail 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, Collections and Topics in MDPI journals
Prof. Agarwal Sonali

E-Mail
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 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. 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 2400 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 (3 papers)

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Research

22 pages, 937 KiB  
Article
Efficient Discovery of Partial Periodic Patterns in Large Temporal Databases
by Rage Uday Kiran, Pamalla Veena, Penugonda Ravikumar, Chennupati Saideep, Koji Zettsu, Haichuan Shang, Masashi Toyoda, Masaru Kitsuregawa and P. Krishna Reddy
Electronics 2022, 11(10), 1523; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics11101523 - 10 May 2022
Cited by 5 | Viewed by 1503
Abstract
Periodic pattern mining is an emerging technique for knowledge discovery. Most previous approaches have aimed to find only those patterns that exhibit full (or perfect) periodic behavior in databases. Consequently, the existing approaches miss interesting patterns that exhibit partial periodic behavior in a [...] Read more.
Periodic pattern mining is an emerging technique for knowledge discovery. Most previous approaches have aimed to find only those patterns that exhibit full (or perfect) periodic behavior in databases. Consequently, the existing approaches miss interesting patterns that exhibit partial periodic behavior in a database. With this motivation, this paper proposes a novel model for finding partial periodic patterns that may exist in temporal databases. An efficient pattern-growth algorithm, called Partial Periodic Pattern-growth (3P-growth), is also presented, which can effectively find all desired patterns within a database. Substantial experiments on both real-world and synthetic databases showed that our algorithm is not only efficient in terms of memory and runtime, but is also highly scalable. Finally, the effectiveness of our patterns is demonstrated using two case studies. In the first case study, our model was employed to identify the highly polluted areas in Japan. In the second case study, our model was employed to identify the road segments on which people regularly face traffic congestion. Full article
(This article belongs to the Special Issue Spatiotemporal Big Data Analytics)
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18 pages, 1111 KiB  
Article
An Evaluation of Hardware-Efficient Quantum Neural Networks for Image Data Classification
by Tuyen Nguyen, Incheon Paik, Yutaka Watanobe and Truong Cong Thang
Electronics 2022, 11(3), 437; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics11030437 - 01 Feb 2022
Cited by 12 | Viewed by 2826
Abstract
Quantum computing is expected to fundamentally change computer systems in the future. Recently, a new research topic of quantum computing is the hybrid quantum–classical approach for machine learning, in which a parameterized quantum circuit, also called quantum neural network (QNN), is optimized by [...] Read more.
Quantum computing is expected to fundamentally change computer systems in the future. Recently, a new research topic of quantum computing is the hybrid quantum–classical approach for machine learning, in which a parameterized quantum circuit, also called quantum neural network (QNN), is optimized by a classical computer. This hybrid approach can have the benefits of both quantum computing and classical machine learning methods. In this early stage, it is of crucial importance to understand the new characteristics of quantum neural networks for different machine learning tasks. In this paper, we will study quantum neural networks for the task of classifying images, which are high-dimensional spatial data. In contrast to previous evaluations of low-dimensional or scalar data, we will investigate the impacts of practical encoding types, circuit depth, bias term, and readout on classification performance on the popular MNIST image dataset. Various interesting findings on learning behaviors of different QNNs are obtained through experimental results. To the best of our knowledge, this is the first work that considers various QNN aspects for image data. Full article
(This article belongs to the Special Issue Spatiotemporal Big Data Analytics)
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20 pages, 2117 KiB  
Article
Efficient Discovery of Periodic-Frequent Patterns in Columnar Temporal Databases
by Penugonda Ravikumar, Palla Likhitha, Bathala Venus Vikranth Raj, Rage Uday Kiran, Yutaka Watanobe and Koji Zettsu
Electronics 2021, 10(12), 1478; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics10121478 - 19 Jun 2021
Cited by 14 | Viewed by 1995
Abstract
Discovering periodic-frequent patterns in temporal databases is a challenging problem of great importance in many real-world applications. Though several algorithms were described in the literature to tackle the problem of periodic-frequent pattern mining, most of these algorithms use the traditional horizontal (or row) [...] Read more.
Discovering periodic-frequent patterns in temporal databases is a challenging problem of great importance in many real-world applications. Though several algorithms were described in the literature to tackle the problem of periodic-frequent pattern mining, most of these algorithms use the traditional horizontal (or row) database layout, that is, either they need to scan the database several times or do not allow asynchronous computation of periodic-frequent patterns. As a result, this kind of database layout makes the algorithms for discovering periodic-frequent patterns both time and memory inefficient. One cannot ignore the importance of mining the data stored in a vertical (or columnar) database layout. It is because real-world big data is widely stored in columnar database layout. With this motivation, this paper proposes an efficient algorithm, Periodic Frequent-Equivalence CLass Transformation (PF-ECLAT), to find periodic-frequent patterns in a columnar temporal database. Experimental results on sparse and dense real-world and synthetic databases demonstrate that PF-ECLAT is memory and runtime efficient and highly scalable. Finally, we demonstrate the usefulness of PF-ECLAT with two case studies. In the first case study, we have employed our algorithm to identify the geographical areas in which people were periodically exposed to harmful levels of air pollution in Japan. In the second case study, we have utilized our algorithm to discover the set of road segments in which congestion was regularly observed in a transportation network. Full article
(This article belongs to the Special Issue Spatiotemporal Big Data Analytics)
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