Spatio-Temporal and Constraint Databases

A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).

Deadline for manuscript submissions: closed (30 November 2021) | Viewed by 4535

Special Issue Editors


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Guest Editor
Data Science Institute and Database and Theoretical Computer Science Group, Hasselt University, Agoralaan, Gebouw D, 3590 Diepenbeek, Belgium
Interests: constraint databases; spatial and spatiotemporal databases; GIS; spatiotemporal data analysis; computational algebraic geometry
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Guest Editor
Department of Computer Science and Engineering, University of Nebraska-Lincoln, Lincoln, NE 68588, USA
Interests: databases; computational linguistics; bioinformatics; geoinformatics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

During the last three decades, the topic of constraint databases has evolved into a mature area of computer science with sound mathematical foundations and with a profound theoretical understanding of the expressive power of a variety of query languages. Constraint databases are especially suited for applications in which possibly infinite sets of continuous data, which have a geometric interpretation, need to be stored in a computer. Today, the most important application domains of constraint databases are geographic information systems (GIS), spatial databases, and spatiotemporal databases. In these applications, infinite geometrical sets of continuous data are finitely represented by means of finite combinations of polynomial equality and inequality constraints that describe these data sets (in mathematical terms, these geometrical data sets are known as semi-algebraic sets, and they have been extensively studied in real algebraic geometry). On the other hand, constraint databases provide us with a new view on classic (linear and non-linear) optimization theory.

For this Special Issue, we encourage authors to address the topic of spatiotemporal data from either a theoretical or an applications perspective. We especially welcome papers that use spatiotemporal data in the context of constraint databases.

Topics include but are not limited to:

  • Spatiotemporal data and databases;
  • Trajectory data and databases;
  • Uncertainty in spatiotemporal data;
  • Query evaluation techniques;
  • Query optimization;
  • Linear constraint spatiotemporal data;
  • Non-linear constraint spatiotemporal data;
  • Constraints-based data visualization;
  • Data mining spatiotemporal data.

Prof. Dr. Bart Kuijpers
Prof. Dr. Peter Revesz
Guest Editors

Manuscript Submission Information

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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. ISPRS International Journal of Geo-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 1700 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

  • Constraint databases
  • Spatial databases
  • Spatiotemporal databases
  • GIS
  • Spatiotemporal data analysis
  • Computational algebraic geometry
  • Query languages
  • Logical query languages
  • Query evaluation and optimization
  • Data mining

Published Papers (2 papers)

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Research

22 pages, 2309 KiB  
Article
Cluster Nested Loop k-Farthest Neighbor Join Algorithm for Spatial Networks
by Hyung-Ju Cho
ISPRS Int. J. Geo-Inf. 2022, 11(2), 123; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi11020123 - 09 Feb 2022
Cited by 1 | Viewed by 1760
Abstract
This paper considers k-farthest neighbor (kFN) join queries in spatial networks where the distance between two points is the length of the shortest path connecting them. Given a positive integer k, a set of query points Q, and [...] Read more.
This paper considers k-farthest neighbor (kFN) join queries in spatial networks where the distance between two points is the length of the shortest path connecting them. Given a positive integer k, a set of query points Q, and a set of data points P, the kFN join query retrieves the k data points farthest from each query point in Q. There are many real-life applications using kFN join queries, including artificial intelligence, computational geometry, information retrieval, and pattern recognition. However, the solutions based on the Euclidean distance or nearest neighbor search are not suitable for our purpose due to the difference in the problem definition. Therefore, this paper proposes a cluster nested loop join (CNLJ) algorithm, which clusters query points (data points) into query clusters (data clusters) and reduces the number of kFN queries required to perform the kFN join. An empirical study was performed using real-life roadmaps to confirm the superiority and scalability of the CNLJ algorithm compared to the conventional solutions in various conditions. Full article
(This article belongs to the Special Issue Spatio-Temporal and Constraint Databases)
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29 pages, 2030 KiB  
Article
Time-Series-Based Queries on Stable Transportation Networks Equipped with Sensors
by Erik Bollen, Rik Hendrix, Bart Kuijpers and Alejandro Vaisman
ISPRS Int. J. Geo-Inf. 2021, 10(8), 531; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10080531 - 07 Aug 2021
Cited by 4 | Viewed by 2110
Abstract
In this paper, we propose a formalism to query transportation networks that are equipped with sensors that produce time-series data. The core of the proposed query mechanism is a logic-based language that is capable to return time, value, and time-series outputs, as well [...] Read more.
In this paper, we propose a formalism to query transportation networks that are equipped with sensors that produce time-series data. The core of the proposed query mechanism is a logic-based language that is capable to return time, value, and time-series outputs, as well as Boolean queries. We can also use the language for node selection and path selection. Furthermore, we propose an implementation of this language in a graph database system and evaluate its working on a fragment of the Flemish river system that is equipped with sensors that measure the water height at regular moments in time. Full article
(This article belongs to the Special Issue Spatio-Temporal and Constraint Databases)
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