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Electronics
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Advances in Spatiotemporal Data Management and Analytics
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Advances in Spatiotemporal Data Management and Analytics

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

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 11056

Special Issue Editors

Prof. Dr. Yanwei Yu

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Guest Editor
Department of Computer Science and Technology, Ocean University of China, Qingdao 266005, China
Interests: data mining; machine learning; database systems
Special Issues, Collections and Topics in MDPI journals
Dr. Zhu Xiao

E-Mail Website
Guest Editor
College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China
Interests: internet of vehicles; intelligent transportation systems; trajectory big data mining
Dr. Ziqiang Yu

E-Mail Website
Guest Editor
School of Computer and Control Engineering, Yantai University, Yantai 264005, China
Interests: spatiotemporal data processing; distributed computing; graph computing

Special Issue Information

Dear Colleagues,

The recent advances in mobile devices (e.g., smartphones and wearable sensors) and location-based social media (e.g., online mapping services, ride-hailing services, and location-based social networks) and their widespread use are generating huge volumes of spatiotemporal data. Spatiotemporal data has many unique features, including spatial and temporal information, and other related information such as textual semantics, attribute values, and venue categories. The management and analysis of spatiotemporal data fundamentally enhances the user experience of a variety of location-based applications, including real-time route planning, next-location recommendations, online food ordering and delivery, location-aware crowdsourcing, and trip advisors. Therefore, spatiotemporal data management and analytics have an increasingly important impact on human lives and activities.

Thanks to big data and recent developments in spatiotemporal data management and analytics techniques, much attention has been paid to developing effective data mining and processing techniques for spatiotemporal data. However, maximizing its usability for various mining tasks while ensuring privacy and reliability through the processing of multi-source heterogeneous and massive-scale spatiotemporal data remains an open challenge.

The analytics of multi-source spatiotemporal data enables us to quickly extract useful information for spatiotemporal applications, which can further improve the effectiveness and reliability of various spatiotemporal mining tasks. This Special Issue aims to develop effective spatiotemporal data management techniques, novel deep learning models, multi-source data processing techniques, privacy-preserving spatial data analytics, and location-aware queries to build effective and efficient spatiotemporal management and analytics systems. Research and development topics for this Special Issue include, but are not limited to:

(1) Spatiotemporal data preprocessing, including data cleaning, feature selection and extraction, data clustering, and map-matching.

(2) Spatiotemporal data mining.

(3) Deep learning/reinforcement learning/transfer learning using spatiotemporal data.

(4) Multisource data stream analytics.

(5) Location-based services and social networks.

(6) Privacy-preserving spatiotemporal data mining.

(7) Graph modeling and algorithms using spatiotemporal data.

(8) Recommender systems using spatiotemporal data.

(9) Spatiotemporal data query-processing systems.

(10) Emerging applications in spatiotemporal data management (e.g., the metaverse).

Dr. Yanwei Yu
Dr. Zhu Xiao
Dr. Ziqiang Yu
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

  • spatiotemporal data management
  • data mining
  • location-based services
  • query processing
  • deep learning
  • recommender systems
  • privacy and security

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Published Papers (4 papers)

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Research

17 pages, 18284 KiB  
Article
Evaluation of Hand Washing Procedure Using Vision-Based Frame Level and Spatio-Temporal Level Data Models
by Rüstem Özakar and Eyüp Gedikli
Electronics 2023, 12(9), 2024; https://doi.org/10.3390/electronics12092024 - 27 Apr 2023
Cited by 1 | Viewed by 3174
Abstract
Hand hygiene is obligatory for all healthcare workers and vital for patient care. During COVID-19, adequate hand washing was among recommended measures for preventing virus transmission. A general hand-washing procedure consisting several steps is recommended by World Health Organization for ensuring hand hygiene. [...] Read more.
Hand hygiene is obligatory for all healthcare workers and vital for patient care. During COVID-19, adequate hand washing was among recommended measures for preventing virus transmission. A general hand-washing procedure consisting several steps is recommended by World Health Organization for ensuring hand hygiene. This process can vary from person to person and human supervision for inspection would be impractical. In this study, we propose computer vision-based new methods using 12 different neural network models and 4 different data models (RGB, Point Cloud, Point Gesture Map, Projection) for the classification of 8 universally accepted hand-washing steps. These methods can also perform well under situations where the order of steps is not observed or the duration of steps are varied. Using a custom dataset, we achieved 100% accuracy with one of the models, and 94.23% average accuracy for all models. We also developed a real-time robust data acquisition technique where RGB and depth streams from Kinect 2.0 camera were utilized. Results showed that with the proposed methods and data models, efficient hand hygiene control is possible. Full article
(This article belongs to the Special Issue Advances in Spatiotemporal Data Management and Analytics)
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16 pages, 2805 KiB  
Article
Robust Subspace Clustering with Block Diagonal Representation for Noisy Image Datasets
by Qiang Li, Ziqi Xie and Lihong Wang
Electronics 2023, 12(5), 1249; https://doi.org/10.3390/electronics12051249 - 5 Mar 2023
Cited by 2 | Viewed by 1562
Abstract
As a relatively advanced method, the subspace clustering algorithm by block diagonal representation (BDR) will be competent in performing subspace clustering on a dataset if the dataset is assumed to be noise-free and drawn from the union of independent linear subspaces. Unfortunately, this [...] Read more.
As a relatively advanced method, the subspace clustering algorithm by block diagonal representation (BDR) will be competent in performing subspace clustering on a dataset if the dataset is assumed to be noise-free and drawn from the union of independent linear subspaces. Unfortunately, this assumption is far from reality, since the real data are usually corrupted by various noises and the subspaces of data overlap with each other, the performance of linear subspace clustering algorithms, including BDR, degrades on the real complex data. To solve this problem, we design a new objective function based on BDR, in which l2,1 norm of the reconstruction error is introduced to model the noises and improve the robustness of the algorithm. After optimizing the objective function, we present the corresponding subspace clustering algorithm to pursue a self-expressive coefficient matrix with a block diagonal structure for a noisy dataset. An affinity matrix is constructed based on the coefficient matrix, and then fed to the spectral clustering algorithm to obtain the final clustering results. Experiments on several artificial noisy image datasets show that the proposed algorithm has robustness and better clustering performance than the compared algorithms. Full article
(This article belongs to the Special Issue Advances in Spatiotemporal Data Management and Analytics)
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13 pages, 4235 KiB  
Article
Visual Three-Dimensional Reconstruction Based on Spatiotemporal Analysis Method
by Xiaoliang Meng, Fuzhen Sun, Liye Zhang, Chao Fang and Xiaoyu Wang
Electronics 2023, 12(3), 535; https://doi.org/10.3390/electronics12030535 - 20 Jan 2023
Cited by 1 | Viewed by 1626
Abstract
To accurately reconstruct the three-dimensional (3D) surface of dynamic objects, we proposed a wrapped phase extraction method for spatiotemporal analysis based on 3D wavelet transform (WT). Our proposed method uses a 2D spatial fringe image combined with the time dimension and forms a [...] Read more.
To accurately reconstruct the three-dimensional (3D) surface of dynamic objects, we proposed a wrapped phase extraction method for spatiotemporal analysis based on 3D wavelet transform (WT). Our proposed method uses a 2D spatial fringe image combined with the time dimension and forms a 3D image sequence. The encoded fringe image sequence’s wrapped phase information was extracted by 3D WT and complex Morlet wavelet, and we improved the wrapped phase extraction’s accuracy by using the characteristics of spatiotemporal analysis and a multi-scale analysis of 3D WT, then we reconstructed the measured object by wrapped phase unwrapping and phase height transformation. Our simulation experiment results show that our proposed method can further filter the noise in the time dimension, and its accuracy is better than that of the one- (1D) and two-dimensional (2D) WT wrapped phase extraction method and the 3D Fourier transform wrapped phase extraction method because the reconstructed spherical crown’s RMSE value does not exceed 0.25 and the PVE value is less than 0.95. Our results show that the proposed method can be applied to the dynamic 3D reconstruction of a real human thoracic and abdominal surface, which fluctuates slowly with respiration movement, further verifying its effectiveness. Full article
(This article belongs to the Special Issue Advances in Spatiotemporal Data Management and Analytics)
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14 pages, 447 KiB  
Article
Improving the Performance of Cold-Start Recommendation by Fusion of Attention Network and Meta-Learning
by Shilong Liu, Yang Liu, Xiaotong Zhang, Cheng Xu, Jie He and Yue Qi
Electronics 2023, 12(2), 376; https://doi.org/10.3390/electronics12020376 - 11 Jan 2023
Cited by 8 | Viewed by 3098
Abstract
The cold-start problem has always been a key challenge in the recommendation research field. As a popular method to learn a learner that can rapidly adapt to a new task through a small number of updates, meta-learning is considered to be a feasible [...] Read more.
The cold-start problem has always been a key challenge in the recommendation research field. As a popular method to learn a learner that can rapidly adapt to a new task through a small number of updates, meta-learning is considered to be a feasible algorithm to reduce the error of cold-start recommendation. However, meta-learning does not take the diverse interests of users into account, which limits the performance improvement in cold-start scenarios. In this paper, we proposed a new model for a cold-start recommendation, which combines the attention mechanism and meta learning. This method enhances the ability of modeling the personalized user interest by learning the weights between users and items based on the attention mechanism and then improves the performance of the cold-start recommendation. We validated the model with two publicly available datasets in the recommendation field. Compared with the three benchmark methods, the proposed model reduces the mean absolute error by at least 2.3% and the root mean square error of 2.5%. Full article
(This article belongs to the Special Issue Advances in Spatiotemporal Data Management and Analytics)
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