Journal articles: 'Database spatio-temporal'

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Song, W., and F. Zhang. "Spatio-temporal topological relationships between land parcels in cadastral database." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-6 (April 23, 2014): 89–92. http://dx.doi.org/10.5194/isprsarchives-xl-6-89-2014.

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There are complex spatio-temporal relationships among cadastral entities. Cadastral spatio-temporal data model should not only describe the data structure of cadastral objects, but also express cadastral spatio-temporal relationships between cadastral objects. In the past, many experts and scholars have proposed a variety of cadastral spatio-temporal data models, but few of them concentrated on the representation of spatiotemporal relationships and few of them make systematic studies on spatiotemporal relationships between cadastral objects. The studies on spatio-temporal topological relationships are not abundant. In the paper, we initially review current approaches to the studies of spatio-temporal topological relationships, and argue that spatio-temporal topological relation is the combination of temporal topology on the time dimension and spatial topology on the spatial dimension. Subsequently, we discuss and develop an integrated representation of spatio-temporal topological relationships within a 3-dimensional temporal space. In the end, based on the semantics of spatiotemporal changes between land parcels, we conclude the possible spatio-temporal topological relations between land parcels, which provide the theoretical basis for creating, updating and maintaining of land parcels in the cadastral database.

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Zhang, Chengcui. "A Survey of Visual Traffic Surveillance Using Spatio-Temporal Analysis and Mining." International Journal of Multimedia Data Engineering and Management 4, no. 3 (2013): 42–60. http://dx.doi.org/10.4018/jmdem.2013070103.

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The focus of this survey is on spatio-temporal data mining and database retrieval for visual traffic surveillance systems. In many traffic surveillance applications, such as incident detection, abnormal events detection, vehicle speed estimation, and traffic volume estimation, the data used for reasoning is really in the form of spatio-temporal data (e.g. vehicle trajectories). How to effectively analyze these spatio-temporal data to automatically find its inherent characteristics for different visual traffic surveillance applications has been of great interest. Examples of spatio-temporal patterns extracted from traffic surveillance videos include, but are not limited to, sudden stops, harsh turns, speeding, and collisions. To meet the different needs of various traffic surveillance applications, several application- or event- specific models have been proposed in the literature. This paper provides a survey of different models and data mining algorithms to cover state of the art in spatio-temporal modelling, spatio-temporal data mining, and spatio-temporal retrieval for traffic surveillance video databases. In addition, the database model issues and challenges for traffic surveillance videos are also discussed in this survey.

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Liu, Ruiqi, Paul Vincent Kuper, and Martin Breunig. "Implementation of Database-Supported Analysis for Spatio-Temporal Digital Terrain Models." AGILE: GIScience Series 6 (June 9, 2025): 1–7. https://doi.org/10.5194/agile-giss-6-36-2025.

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Abstract. Digital Terrain Models (DTMs) have consistently been a focus of research in a wide range of fields that require observation and analysis of Earth's surface elevation. However, spatio-temporal changes of DTMs are particularly important as they can provide critical insights into phenomena such as natural hazards and urban development. Analysing these spatio-temporal changes in DTMs usually involves large volumes of data. Therefore, a geodatabase is essential for organizing and managing theses DTM datasets, enabling spatio-temporal retrieval for subsequent analysis. The exemplary implementation of analytical methods for spatio-temporal DTM datasets is the key focus of this paper. We present an event-based time-stamping data model for the management of spatio-temporal DTMs. We also conduct regional statistical analysis across different regions as well as elevation change analysis within the same region over time. Various resampling algorithms are described to harmonize different resolutions of DTMs for elevation change analysis. Finally, we visualize the results of our spatio-temporal analysis in the web-based environment. In the future, further research will also integrate AI methods to enhance spatio-temporal analysis of big DTM datasets.

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Liu, Jun, and Jing Wei Li. "Trajectory Modeling of Moving Objects Based in Spatio-Temporal Database." Advanced Materials Research 181-182 (January 2011): 54–59. http://dx.doi.org/10.4028/www.scientific.net/amr.181-182.54.

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Trajectory modeling of moving objects is one of the key problems in spatio-tempo-ral databases research. In a series of moves spatio-temporal database information and finding time to update in actual application space and threshold distance threshold, this paper combines the ideas of the linear regression model in mathematics to propose the trajectory model, which is on the basis of the Moving Objects Spatio-Temporal model and supports the past, present and future information processing. It effectively predicts the moving objects of movement trend and scope at any time within the threshold.

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PELEKIS, NIKOS, BABIS THEODOULIDIS, IOANNIS KOPANAKIS, and YANNIS THEODORIDIS. "Literature review of spatio-temporal database models." Knowledge Engineering Review 19, no. 3 (2004): 235–74. http://dx.doi.org/10.1017/s026988890400013x.

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Recent efforts in spatial and temporal data models and database systems have attempted to achieve an appropriate kind of interaction between the two areas. This paper reviews the different types of spatio-temporal data models that have been proposed in the literature as well as new theories and concepts that have emerged. It provides an overview of previous achievements within the domain and critically evaluates the various approaches through the use of a case study and the construction of a comparison framework. This comparative review is followed by a comprehensive description of the new lines of research that emanate from the latest efforts inside the spatio-temporal research community.

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Rainu Nandal, Rainu Nandal. "Spatio-Temporal Database and Its Models: A Review." IOSR Journal of Computer Engineering 11, no. 2 (2013): 91–100. http://dx.doi.org/10.9790/0661-11291100.

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Zhou, Xiao-guang, Jun Chen, Jie Jiang, Jian-jun Zhu, and Zhi-lin Li. "Event-based incremental updating of spatio-temporal database." Journal of Central South University of Technology 11, no. 2 (2004): 192–98. http://dx.doi.org/10.1007/s11771-004-0040-3.

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Zhang, Jianhai, Zhiyong Feng, Yong Su, Meng Xing, and Wanli Xue. "Riemannian Spatio-Temporal Features of Locomotion for Individual Recognition." Sensors 19, no. 1 (2018): 56. http://dx.doi.org/10.3390/s19010056.

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Individual recognition based on skeletal sequence is a challenging computer vision task with multiple important applications, such as public security, human–computer interaction, and surveillance. However, much of the existing work usually fails to provide any explicit quantitative differences between different individuals. In this paper, we propose a novel 3D spatio-temporal geometric feature representation of locomotion on Riemannian manifold, which explicitly reveals the intrinsic differences between individuals. To this end, we construct mean sequence by aligning related motion sequences on the Riemannian manifold. The differences in respect to this mean sequence are modeled as spatial state descriptors. Subsequently, a temporal hierarchy of covariance are imposed on the state descriptors, making it a higher-order statistical spatio-temporal feature representation, showing unique biometric characteristics for individuals. Finally, we introduce a kernel metric learning method to improve the classification accuracy. We evaluated our method on two public databases: the CMU Mocap database and the UPCV Gait database. Furthermore, we also constructed a new database for evaluating running and analyzing two major influence factors of walking. As a result, the proposed approach achieves promising results in all experiments.

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Pérez Montoya, Luis Miguel, and Francisco Javier Moreno Arboleda. "Comparing two spatio-temporal query languages: SQLST and Güting’s language." Ingeniería e Investigación 28, no. 3 (2008): 138–44. http://dx.doi.org/10.15446/ing.investig.v28n3.15133.

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Spatio-temporal databases allow us to represent objects and phenomena from the real world which change position or shape as time elapses. Several query languages have been proposed during the last decade to deal with this type of database. Two of these languages have been compared in this paper: SQLST and Güting’s language. The comparison was based on criteria which have been applied to programming languages; however, they were adapted here to evaluate database query languages. The results led to concluding that both languages high degree of expressiveness may affect other criteria such as readability and simplicity, especially in the case of Güting’s language.

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Li, Wu, Wu, and Zhao. "An Adaptive Construction Method of Hierarchical Spatio-Temporal Index for Vector Data under Peer-to-Peer Networks." ISPRS International Journal of Geo-Information 8, no. 11 (2019): 512. http://dx.doi.org/10.3390/ijgi8110512.

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Spatio-temporal indexing is a key technique in spatio-temporal data storage and management. Indexing methods based on spatial filling curves are popular in research on the spatio-temporal indexing of vector data in the Not Relational (NoSQL) database. However, the existing methods mostly focus on spatial indexing, which makes it difficult to balance the efficiencies of time and space queries. In addition, for non-point elements (line and polygon elements), it remains difficult to determine the optimal index level. To address these issues, this paper proposes an adaptive construction method of hierarchical spatio-temporal index for vector data. Firstly, a joint spatio-temporal information coding based on the combination of the partition and sort key strategies is presented. Secondly, the multilevel expression structure of spatio-temporal elements consisting of point and non-point elements in the joint coding is given. Finally, an adaptive multi-level index tree is proposed to realize the spatio-temporal index (Multi-level Sphere 3, MLS3) based on the spatio-temporal characteristics of geographical entities. Comparison with the XZ3 index algorithm proposed by GeoMesa proved that the MLS3 indexing method not only reasonably expresses the spatio-temporal features of non-point elements and determines their optimal index level, but also avoids storage hotspots while achieving spatio-temporal retrieval with high efficiency.

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Gao, Y., J. Liu, J. Liu, et al. "RESEARCH ON CONSTRUCTION OF NATURAL RESOURCES THREE-DIMENSIONAL SPATIO-TEMPORAL DATABASE SYSTEM." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-4/W9-2024 (March 8, 2024): 175–82. http://dx.doi.org/10.5194/isprs-archives-xlviii-4-w9-2024-175-2024.

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Abstract. The construction of an efficient and practical database management system based on large-scale three-dimensional spatiotemporal databases presents a novel challenge in the field of natural resource information technology. This research, with the objective of supporting the contemporary era of unified natural resource management and building upon the foundation of a national-level three-dimensional spatiotemporal natural resource database, proposes a comprehensive framework for a distributed architecture database management system. Subsequently, the research outlines the system's components, encompassing a service publishing system, online application system, professional management system, and operational supervision system, and designs core functionalities for the integrated management of natural resource elements within a three-dimensional spatiotemporal context. Building upon this framework, the research utilizes cloud-based database software and hardware environments, along with distributed technology pathways, to establish a national-level three-dimensional spatiotemporal database management system for natural resources. Furthermore, it conducts typical applications in the field of natural resources. The results demonstrate that the database management system effectively achieves the integration, representation, and analytical application of large-scale natural resource survey and monitoring data within a three-dimensional spatiotemporal context. It plays a crucial supportive role in the development of a national-level three-dimensional spatiotemporal natural resource database and holds valuable insights for the construction of provincial-level database management systems and other related systems in various domains.

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Breunig, M., AB Cremers, S. Shumilov, and J. Siebeck. "Spatio-temporal database support for long-period scientific data." Data Science Journal 2 (2003): 175–91. http://dx.doi.org/10.2481/dsj.2.175.

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Tanin, Egemen, Rui Zhang, and Lars Kulik. "Spatio-temporal database research at the University of Melbourne." ACM SIGMOD Record 38, no. 3 (2010): 35–39. http://dx.doi.org/10.1145/1815933.1815942.

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Zongyao, Sha, and Bian Fuling. "Spatio-temporal data model based on relational database system." Geo-spatial Information Science 5, no. 2 (2002): 22–27. http://dx.doi.org/10.1007/bf02833882.

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Zhang, Yitao, Ruiqing Xu, Wangping Lu, et al. "Multi-Modal Spatio-Temporal Knowledge Graph of Ship Management." Applied Sciences 13, no. 16 (2023): 9393. http://dx.doi.org/10.3390/app13169393.

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In modern maritime activities, the quality of ship communication directly impacts the safety, efficiency, and economic viability of ship operations. Therefore, predicting and analyzing ship communication status has become a crucial task to ensure the smooth operation of ships. Currently, ship communication status analysis heavily relies on large-scale, multi-source heterogeneous data with spatio-temporal and multi-modal features, which presents challenges for ship communication quality prediction tasks. To address this issue, this paper constructs a multi-modal spatio-temporal ontology and a multi-modal spatio-temporal knowledge graph for ship communication, guided by existing ontologies and domain knowledge. This approach effectively integrates multi-modal spatio-temporal data, providing support for subsequent efficient data analysis and applications. Taking the scenario of fishing vessel communication activities as an example, the query tasks for ship communication knowledge are successfully performed using a graph database, and we combine the spatio-temporal knowledge graph with graph convolutional neural network technology to achieve real-time communication quality prediction for fishing vessels, further validating the practical value of the multi-modal spatio-temporal knowledge graph.

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Jahn, M. W., P. E. Bradley, M. Al Doori, and M. Breunig. "TOPOLOGICALLY CONSISTENT MODELS FOR EFFICIENT BIG GEO-SPATIO-TEMPORAL DATA DISTRIBUTION." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-4/W5 (October 23, 2017): 65–72. http://dx.doi.org/10.5194/isprs-annals-iv-4-w5-65-2017.

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Geo-spatio-temporal topology models are likely to become a key concept to check the consistency of 3D (spatial space) and 4D (spatial + temporal space) models for emerging GIS applications such as subsurface reservoir modelling or the simulation of energy and water supply of mega or smart cities. Furthermore, the data management for complex models consisting of big geo-spatial data is a challenge for GIS and geo-database research. General challenges, concepts, and techniques of big geo-spatial data management are presented. In this paper we introduce a sound mathematical approach for a topologically consistent geo-spatio-temporal model based on the concept of the incidence graph. We redesign DB4GeO, our service-based geo-spatio-temporal database architecture, on the way to the parallel management of massive geo-spatial data. Approaches for a new geo-spatio-temporal and object model of DB4GeO meeting the requirements of big geo-spatial data are discussed in detail. Finally, a conclusion and outlook on our future research are given on the way to support the processing of geo-analytics and -simulations in a parallel and distributed system environment.

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Chen, Xiaoying, Chong Zhang, Bin Ge, and Weidong Xiao. "Efficient Historical Query in HBase for Spatio-Temporal Decision Support." International Journal of Computers Communications & Control 11, no. 5 (2016): 613. http://dx.doi.org/10.15837/ijccc.2016.5.2611.

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Comparing to last decade, technologies to gather spatio-temporal data are more and more developed and easy to use or deploy, thus tens of billions, even trillions of sensed data are accumulated, which poses a challenge to spatio-temporal Decision Support System (stDSS). Traditional database hardly supports such huge volume, and tends to bring performance bottleneck to the analysis platform. Hence in this paper, we argue to use NoSQL database, HBase, to replace traditional back-end storage system. Under such context, the well-studied spatio-temporal querying techniques in traditional database should be shifted to HBase system parallel. However, this problem is not solved well in HBase, as many previous works tackle the problem only by designing schema, i.e., designing row key and column key formation for HBase, which we don’t believe is an effective solution. In this paper, we address this problem from nature level of HBase, and propose an index structure as a built-in component for HBase. STEHIX (Spatio-TEmporal Hbase IndeX) is adapted to two-level architecture of HBase and suitable for HBase to process spatio-temporal queries. It is composed of index in the meta table (the first level) and region index (the second level) for indexing inner structure of HBase regions. Base on this structure, three queries, range query, kNN query and GNN query are solved by proposing algorithms, respectively. For achieving load balancing and scalable kNN query, two optimizations are also presented. We implement STEHIX and conduct experiments on real dataset, and the results show our design outperforms a previous work in many aspects.

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Feng, Deying, Jie Yang, Yanxia Wei, Hairong Xiao, and Laigang Zhang. "An Efficient Person Search Method Using Spatio-Temporal Features for Surveillance Videos." Applied Sciences 12, no. 15 (2022): 7670. http://dx.doi.org/10.3390/app12157670.

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Existing person search methods mainly focus on searching for the target person using database images. However, this is different from real-world surveillance videos which involve a temporal relationship between video frames. To solve this problem, we propose an efficient person search method that employs spatio-temporal features in surveillance videos. This method not only considers the spatial features of persons in each frame, but also utilizes the temporal relationship of the same person between adjacent frames. For this purpose, the spatial features are extracted by combining Yolo network with Resnet-50 model, and the temporal relationship is processed by gated recurrent unit. The spatio-temporal features are generated by the following average pooling layer and used to represent persons in the videos. To ensure search efficiency, locality sensitive hashing is used to organize massive spatio-temporal features and calculate the similarity. A surveillance video database is also constructed to evaluate the proposed method, and the experimental results demonstrate that our method improves search accuracy while ensuring search efficiency.

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Kuper, P. V., M. Breunig, M. Al-Doori, and A. Thomsen. "APPLICATION OF 3D SPATIO-TEMPORAL DATA MODELING, MANAGEMENT, AND ANALYSIS IN DB4GEO." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-2/W1 (October 5, 2016): 163–70. http://dx.doi.org/10.5194/isprs-annals-iv-2-w1-163-2016.

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Many of today´s world wide challenges such as climate change, water supply and transport systems in cities or movements of crowds need spatio-temporal data to be examined in detail. Thus the number of examinations in 3D space dealing with geospatial objects moving in space and time or even changing their shapes in time will rapidly increase in the future. Prominent spatio-temporal applications are subsurface reservoir modeling, water supply after seawater desalination and the development of transport systems in mega cities. All of these applications generate large spatio-temporal data sets. However, the modeling, management and analysis of 3D geo-objects with changing shape and attributes in time still is a challenge for geospatial database architectures. In this article we describe the application of concepts for the modeling, management and analysis of 2.5D and 3D spatial plus 1D temporal objects implemented in DB4GeO, our service-oriented geospatial database architecture. An example application with spatio-temporal data of a landfill, near the city of Osnabrück in Germany demonstrates the usage of the concepts. Finally, an outlook on our future research focusing on new applications with big data analysis in three spatial plus one temporal dimension in the United Arab Emirates, especially the Dubai area, is given.

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Karpachevskiy, Andrey, and German Titov. "Spatio-temporal database design for backbone power grid of Russia." InterCarto. InterGIS 27, no. 2 (2021): 306–14. http://dx.doi.org/10.35595/2414-9179-2021-2-27-306-314.

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Backbone power lines in Russia have a complex spatial structure. There are no systematized and topologically consistent spatio-temporal data about them. however, the study of their evolution requires not only data mining, but also a comprehensive design of the structure of the spatio-temporal database. The structure should provide effective data storage, be convenient for filling the database and editing data, provide the ability to reconstruct the network for a given period and apply spatial analysis methods. Open sources like power grid operator reports, schemes and programs of power grid development, public cadastral map, information from Situational and Analytical Center of the Ministry of Energy and very high spatial resolution remote sensing data are the main data sources. Users do not have direct access to the database but refer to it using queries. Interaction with the database is carried out through Application programming Interface (API). This allows downloading data from the database as well as embedding them into external systems, for example, connecting analysis tools to them, creating cartographic web applications with this data. Data preprocessing is performed in python using the Arcpy module, the database is created with PostgreSQl, the API works on PostgREST. Consistent multi-temporal spatial database serves as the basis for analyzing the structural features of electrical networks, makes it possible to visualize the history of the development of the power grid of the territory in an interactive web-based mapping application, allows to apply geoprocessing tools and special network analysis tools. The detailed study of the evolution of backbone power grids is crucial in long-term strategies for the development of the power grid. Abroad, studies of the evolution of electrical networks usually operate with a schematic graph of a network without reference to real spatial geometry, therefore, there is no problem of designing the structure of spatio-temporal database. yet, ignoring topomorphological relationships in the network leads to the loss of information about electrical networks, which leads to a loss in the quality of spatial analysis.

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Mehmood, Nadeem, Syed Muhmmad Aqil Burney, Kashif Rizwan, Asadullah Shah, and Adnan Nadeem. "Building Spatio-Temporal Database Model Based on Ontological Approach using Relational Database Environment." Mehran University Research Journal of Engineering and Technology 36, no. 4 (2017): 891–900. http://dx.doi.org/10.22581/muet1982.1704.13.

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Ashby, M. P. J. "Studying Crime and Place with the Crime Open Database." Research Data Journal for the Humanities and Social Sciences 4, no. 1 (2019): 65–80. http://dx.doi.org/10.1163/24523666-00401007.

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The study of spatial and temporal crime patterns is important for both academic understanding of crime-generating processes and for policies aimed at reducing crime. However, studying crime and place is often made more difficult by restrictions on access to appropriate crime data. This means understanding of many spatio-temporal crime patterns are limited to data from a single geographic setting, and there are few attempts at replication. This article introduces the Crime Open Database (code), a database of 16 million offenses from 10 of the largest United States cities over 11 years and more than 60 offense types. Open crime data were obtained from each city, having been published in multiple incompatible formats. The data were processed to harmonize geographic co-ordinates, dates and times, offense categories and location types, as well as adding census and other geographic identifiers. The resulting database allows the wider study of spatio-temporal patterns of crime across multiple US cities, allowing greater understanding of variations in the relationships between crime and place across different settings, as well as facilitating replication of research.

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Tinitana, Julio César Cuenca, Carlos Adrian Correa-Florez, Diego Patino, and José Vuelvas. "Spatio-Temporal Kriging Based Economic Dispatch Problem Including Wind Uncertainty." Energies 13, no. 23 (2020): 6419. http://dx.doi.org/10.3390/en13236419.

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The integration of renewable generation adds complexity to the operation of the power system due to its unpredictable characteristics. Therefore, the development of methods to accurately model the uncertainty is necessary. In this paper, the spatio-temporal kriging and analog approaches are used to forecast wind power generation and used as the input to solve an economic dispatch problem, considering the uncertainties of wind generation. Spatio-temporal kriging captures the spatial and temporal information available in the database to improve wind forecasts. We evaluate the performance of using the spatio-temporal kriging, and comparisons are carried out versus other approaches in the framework of the economic power dispatch problem, for which simulations are developed on the modified IEEE 3-bus and IEEE 24-bus test systems. The results demonstrate that the use of kriging based spatio-temporal models in the context of economic power dispatch can provide an opportunity for lower operating costs in the presence of uncertainty when compared to other approaches.

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Mościcka, Albina, and Marta Kuźma. "Spatio-Temporal Database of Places Located in the Border Area." ISPRS International Journal of Geo-Information 7, no. 3 (2018): 108. http://dx.doi.org/10.3390/ijgi7030108.

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KIRIMURA, Takashi, Tomoki NAKAYA, and Keiji YANO. "Building a Spatio-temporal GIS Database about Boundaries of Municipalities." Theory and Applications of GIS 19, no. 2 (2011): 139–48. http://dx.doi.org/10.5638/thagis.19.139.

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Ding, Zhiming, Bin Yang, Yuanying Chi, and Limin Guo. "Enabling Smart Transportation Systems: A Parallel Spatio-Temporal Database Approach." IEEE Transactions on Computers 65, no. 5 (2016): 1377–91. http://dx.doi.org/10.1109/tc.2015.2479596.

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Miguelangel, Trevino, and Al Lail Mustafa. "Database Web Application for Administering Spatio-Temporal Access Control Policies." American Journal of Advanced Research 5, no. 1 (2021): 6–12. https://doi.org/10.5281/zenodo.5112633.

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Governmental and business organizations use the standard authorization model─ Role-based access control (RBAC) ─ to specify and administer access policies for electronic resources. In RBAC-based applications, access is granted or denied based on users’ credentials. However, the RBAC model lacks features that allow applications to determine access based on time and location, spatio-temporal information. This access requirement is important for a growing number of mobile applications. Researchers have proposed new access control models to accommodate organizations' reliance on mobile applications. The General Spatio-temporal Role-Based Access Control model (GSTRBAC) is a model that incorporates time and location constraints as additional factors to grant access to resources. This paper presents the results of our undergraduate research project on creating a relational database that provides a way to store and retrieve GSTRBAC policy information. Further, the paper describes a web application that security analysts can use to administer GSTRBAC policies.

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Zhu, Yichuan, Zenon Medina-Cetina, and Alma Rosa Pineda-Contreras. "Spatio-Temporal Statistical Characterization of Boundary Kinematic Phenomena of Triaxial Sand Specimens." Materials 15, no. 6 (2022): 2189. http://dx.doi.org/10.3390/ma15062189.

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This paper follows up on a reference paper that inspired MDPI’s Topic “Stochastic Geomechanics: From Experimentation to Forward Modeling”, where global and local deformation effects on sand specimens are fully described from high resolution boundary displacement fields, and supported by its experimental database, which is open to the scientific community for further study. This paper introduces the use of spatio-temporal statistics from a subset of such an experimental database to characterize the specimens’ spatio-temporal displacement fields, populated by repeating a set of triaxial compression tests on drained, dry, vacuum-consolidated sand specimens, tested under similar experimentally controlled conditions. A three-dimensional digital image correlation (3D-DIC) technique was used to measure the specimens’ boundary displacement fields throughout the course of shearing under axial compression. Spatio-temporal first- and second-order statistics were computed for different data dimensionality conditions (0D, 0D-T, 1D-T, 3D-T) to identify and characterize the dominant failure mechanisms across different testing specimens. This allowed us to quantify localization phenomena’s spatio-temporal uncertainty. Results show that the uncertainty captured along the deformation process across different dimensionality conditions can be directly associated with different failure mechanisms, including localization patterns, such as the onset and evolution of shear, compression, and expansion bands. These spatio-temporal observations show the dependencies between locally distinctive displacement regions over a specimen’s surface, and across different times during a specimen’s shearing process. Results of this work provide boundary spatio-temporal statistics of experimental evidence in sands, which sets the basis for the development of research on the numerical simulation of sand’s constitutive behavior. Moreover, it allows to add a new understanding on the effect of uncertainty on the mechanistic interpretation of sands’ kinematic phenomena.

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Jian, Xueyan, Wen Yuan, Wu Yuan, Xinqi Gao, and Rong Wang. "Automated Construction and Mining of Text-Based Modern Chinese Character Databases: A Case Study of Fujian." Information 16, no. 4 (2025): 324. https://doi.org/10.3390/info16040324.

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Historical figures are crucial for understanding historical processes and social changes. However, existing databases of historical figures primarily focused on ancient Chinese individuals and are limited by the simplistic organization of textual information, lacking structured processing. Therefore, this study proposes an automatic method for constructing a spatio-temporal database of modern Chinese figures. The character state transition matrix reveals the spatio-temporal evolution of historical figures, while the random walk algorithm identifies their primary migration patterns. Using historical figures from Fujian Province (1840–2009) as a case study, the results demonstrate that this method effectively constructs the spatio-temporal chain of figures, encompassing time, space, and events. The character state transition matrix indicates a fluctuating trend of state change from 1840 to 2009, initially increasing and then decreasing. By applying keyword extraction and the random walk method, this study finds that the state transitions and their causes align with the historical trends. The four-dimensional analytical framework of “character-time-space-event” established in this study holds significant value for the field of digital humanities.

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Peng, Weichen, Luo Chen, Xue Ouyang, and Wei Xiong. "A Time-Identified R-Tree: A Workload-Controllable Dynamic Spatio-Temporal Index Scheme for Streaming Processing." ISPRS International Journal of Geo-Information 13, no. 2 (2024): 49. http://dx.doi.org/10.3390/ijgi13020049.

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Many kinds of spatio-temporal data in our daily lives, such as the trajectory data of moving objects, stream natively. Streaming systems exhibit significant advantages in processing streaming data due to their distributed architecture, high throughput, and real-time performance. The use of streaming processing techniques for spatio-temporal data applications is a promising research direction. However, due to the strong dynamic nature of data in streaming processing systems, traditional spatio-temporal indexing techniques based on relatively static data cannot be used directly in stream-processing environments. It is necessary to study and design new spatio-temporal indexing strategies. Hence, we propose a workload-controllable dynamic spatio-temporal index based on the R-tree. In order to restrict memory usage, we formulate an INSERT and batch-REMOVE (I&BR) method and append a collection mechanism to the traditional R-tree. To improve the updating performance, we propose a time-identified R-tree (TIR). Moreover, we propose a distributed system prototype called a time-identified R-tree farm (TIRF). Experiments show that the TIR could work in a scenario with a controllable usage of memory and a stable response time. The throughput of the TIRF could reach 1 million points per second. The performance of a range search in the TIRF is many times better than in PostgreSQL, which is a widely used database system for spatio-temporal applications.

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Bac-Bronowicz, Joanna, Gabriela Wojciechowska, and Izabela Piech. "3D Data Acquisition for Spatio-temporal Analysis of Architectural and Urban Environment Changes of Wrocław Cathedral." Civil and Environmental Engineering Reports 35, no. 2 (2025): 314–29. https://doi.org/10.59440/ceer/203532.

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This paper presents a method for preparing digital spatial data to study the spatio-temporal evolution of the urban and architectural environment. Methods for comparing spatial data from different periods allows a comprehensive spatial and temporal analysis of architectural changes and the urban environment surroundings of the Wroclaw Cathedral. The data can be obtained through digital surveying using various methods, digitization of architectural plans, old maps and iconographic sources, and downloading of current databases. The study of architectural change in historic buildings and changes in their surroundings is an important aspect of cultural heritage research. The use of digital spatial data makes it possible to analyse spatio-temporal data from different time periods and to trace their historical context. Capturing this data in digital form and storing it in a multi-resolution database allows comprehensive comparisons to be made and provides a broader general view of history.

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Barbosa, Ivan Carlos da Costa, Emerson Renato Maciel da Silva, Helder José Farias da Silva, Luiz Gonzaga da Silva Costa, Maria Isabel Vitorino, and Adriano Marlisom Leão de Sousa. "Avaliação da precipitação baseada em dados do CHIRPS e GPCC com medições na superfície para a sub-bacia do rio Guamá no Nordeste Paraense." Ciência e Natura 42 (February 17, 2020): e32. http://dx.doi.org/10.5902/2179460x42094.

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Rain is one of the most important variables in climate studies in Amazon because of it is large variability in time and space scales. Many basins and sub-basins in the region are deficient in regular and uniform monitoring of data observed on the surface. Today, the remote sensing products available provide satellite estimated rainfall data for a large spatio-temporal distribution and for almost every globe. Therefore, this study aims to evaluate the performance of rainfall data obtained from remote sensing for the sub-basin region of the Guamá River, Northeastern Pará, compared to data observed on terrestrial rain gauges. In addition to identifying the spatio-temporal behavior of rain in the area. The rainfall data used were: rain measured by rain gauge (Hidroweb) and rain estimated by remote sensing and made available by the high resolution precipitation database of GPCC and CHIRPS products, for the period between 1988 and 2018. The data were compared with a remarkably high correlation (r = 0.99) and a satisfactory agreement index (d = 0.98). The two estimated databases showed an approximate overestimation of the observed precipitation and a spatio-temporal distribution consistent with that expected for the region.

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Zhou, Chunlei, Xinwei Dong, Liang Ji, Bijun Zhang, Zhongping Xu, and Chengping Zhang. "Hierarchical mining algorithm for high dimensional spatiotemporal big data based on association rules." E3S Web of Conferences 256 (2021): 02040. http://dx.doi.org/10.1051/e3sconf/202125602040.

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The traditional data mining algorithm focuses too much on a single dimension of data time or space, ignoring the association between time and space, which leads to a large amount of computation and low processing efficiency of the mining algorithm and makes it difficult to guarantee the final data mining effect. In response to the above problems, a hierarchical mining algorithm based on association rules for high-dimensional spatio-temporal big data is proposed. Based on the traditional association rules, after establishing the association rules of spatio-temporal data, the data to be mined are cleaned for redundancy. After selecting the local linear embedding algorithm to reduce the dimensionality of the data, a hierarchical mining strategy is developed to realize high-dimensional spatio-temporal big data mining by searching frequent predicates to form a spatio-temporal transaction database. The simulation experiment results verify that the algorithm has high complexity and can effectively reduce the processing volume, which can improve the processing efficiency by at least 56.26% compared with other algorithms.

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Truong, Anh Tuan. "Privacy Preserving Spatio-Temporal Databases Based on k-Anonymity." Science & Technology Development Journal - Engineering and Technology 3, SI1 (2020): SI82—SI94. http://dx.doi.org/10.32508/stdjet.v3isi1.517.

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The development of location-based services and mobile devices has lead to an increase in the location data. Through the data mining process, some valuable information can be discovered from location data. In the other words, an attacker may also extract some private (sensitive) information of the user and this may make threats against the user privacy. Therefore, location privacy protection becomes an important requirement to the success in the development of location-based services. In this paper, we propose a grid-based approach as well as an algorithm to guarantee k-anonymity, a well-known privacy protection approach, in a location database. The proposed approach considers only the information that has significance for the data mining process while ignoring the un-related information. The experiment results show the effectiveness of the proposed approach in comparison with the literature ones.

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Ostroukh, О., D. Chomko, V. Ostroukh, and I. Pidlisetska. "USE OF SPATIAL ANALYSIS TOOLS AND CAPABILITY OF GEOINFORMATION SYSTEMS FOR ASSESSMENT OF THE STATE OF UNDERGROUND WATERS." Visnyk of Taras Shevchenko National University of Kyiv. Geology, no. 4 (91) (2020): 100–105. http://dx.doi.org/10.17721/1728-2713.91.14.

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The results of the study of the ecological-hydrogeochemical state of the underground waters of the Chop-Mukachevo basin and the identification of its spatio-temporal changes in the chemical composition using GIS facilities are presented. The methodology for processing the initial hydrogeological data on the basis of geographic information technologies has been improved and tested on the example of spatio-temporal changes in the indicators of chemical composition. A specialized geoinformation database of the chemical composition of groundwater has been created. The evaluation of spatio-temporal patterns of changes in the chemical composition using statistical analysis, cartographic modeling, and spatial analysis in GIS, identified areas with increased salinity and hardness of groundwater. Based on an integrated approach, zoning was performed and the current hydrogeochemical state of the groundwater resources of the study area was evaluated.

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Sorokine, A., and R. N. Stewart. "SPATIO-TEMPORAL DATA MODEL FOR INTEGRATING EVOLVING NATION-LEVEL DATASETS." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-4/W2 (October 19, 2017): 69–76. http://dx.doi.org/10.5194/isprs-annals-iv-4-w2-69-2017.

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Ability to easily combine the data from diverse sources in a single analytical workflow is one of the greatest promises of the Big Data technologies. However, such integration is often challenging as datasets originate from different vendors, governments, and research communities that results in multiple incompatibilities including data representations, formats, and semantics. Semantics differences are hardest to handle: different communities often use different attribute definitions and associate the records with different sets of evolving geographic entities. Analysis of global socioeconomic variables across multiple datasets over prolonged time is often complicated by the difference in how boundaries and histories of countries or other geographic entities are represented. Here we propose an event-based data model for depicting and tracking histories of evolving geographic units (countries, provinces, etc.) and their representations in disparate data. The model addresses the semantic challenge of preserving identity of geographic entities over time by defining criteria for the entity existence, a set of events that may affect its existence, and rules for mapping between different representations (datasets). Proposed model is used for maintaining an evolving compound database of global socioeconomic and environmental data harvested from multiple sources. Practical implementation of our model is demonstrated using PostgreSQL object-relational database with the use of temporal, geospatial, and NoSQL database extensions.

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Baker Effendi, Sedick, Brink van der Merwe, and Wolf-Tilo Balke. "Suitability of Graph Database Technology for the Analysis of Spatio-Temporal Data." Future Internet 12, no. 5 (2020): 78. http://dx.doi.org/10.3390/fi12050078.

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Every day large quantities of spatio-temporal data are captured, whether by Web-based companies for social data mining or by other industries for a variety of applications ranging from disaster relief to marine data analysis. Making sense of all this data dramatically increases the need for intelligent backend systems to provide realtime query response times while scaling well (in terms of storage and performance) with increasing quantities of structured or semi-structured, multi-dimensional data. Currently, relational database solutions with spatial extensions such as PostGIS, seem to come to their limits. However, the use of graph database technology has been rising in popularity and has been found to handle graph-like spatio-temporal data much more effectively. Motivated by the need to effectively store multi-dimensional, interconnected data, this paper investigates whether or not graph database technology is better suited when compared to the extended relational approach. Three database technologies will be investigated using real world datasets namely: PostgreSQL, JanusGraph, and TigerGraph. The datasets used are the Yelp challenge dataset and an ambulance response simulation dataset, thus combining real world spatial data with realistic simulations offering more control over the dataset. Our extensive evaluation is based on how each database performs under practical data analysis scenarios similar to those found on enterprise level.

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Pan, Ning, Ke Wang, and Jing Jing Tan. "A Spatio-Temporal Storage Method for Land-Use Change." Applied Mechanics and Materials 687-691 (November 2014): 3078–82. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.3078.

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Frequent land-use changes might produce a large amount of historical data which are valuable for data mining and decision-making. Based on the traditional Whole-state-recording Mode, the Special-state-recording Mode was proposed, focusing on the temporal aspect. This mode could optimize the land use database and reduce redundant change record. It could also improve data rollback and historical backtracking functions. The mode was successfully applied to land use planning in Zhejiang Province.

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Wieland, Marc, and Massimiliano Pittore. "A Spatio-Temporal Building Exposure Database and Information Life-Cycle Management Solution." ISPRS International Journal of Geo-Information 6, no. 4 (2017): 114. http://dx.doi.org/10.3390/ijgi6040114.

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Di Felice, Paolino. "The Design of a Spatio-Temporal Database to Investigate on Sex Offenders." Procedia - Social and Behavioral Sciences 73 (February 2013): 403–9. http://dx.doi.org/10.1016/j.sbspro.2013.02.068.

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Di Felice, Paolino. "Investigative Strategies on Top of a Spatio-temporal Database about Sex Offenders." Procedia - Social and Behavioral Sciences 73 (February 2013): 410–17. http://dx.doi.org/10.1016/j.sbspro.2013.02.069.

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Valenzuela, Pablo, María José Domínguez-Cuesta, Manuel Antonio Mora García, and Montserrat Jiménez-Sánchez. "A spatio-temporal landslide inventory for the NW of Spain: BAPA database." Geomorphology 293 (September 2017): 11–23. http://dx.doi.org/10.1016/j.geomorph.2017.05.010.

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Song, Liting, Shaojun Pan, Zichao Zhang, Longhao Jia, Wei-Hua Chen, and Xing-Ming Zhao. "STAB: a spatio-temporal cell atlas of the human brain." Nucleic Acids Research 49, no. D1 (2020): D1029—D1037. http://dx.doi.org/10.1093/nar/gkaa762.

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Abstract The human brain is the most complex organ consisting of billions of neuronal and non-neuronal cells that are organized into distinct anatomical and functional regions. Elucidating the cellular and transcriptome architecture underlying the brain is crucial for understanding brain functions and brain disorders. Thanks to the single-cell RNA sequencing technologies, it is becoming possible to dissect the cellular compositions of the brain. Although great effort has been made to explore the transcriptome architecture of the human brain, a comprehensive database with dynamic cellular compositions and molecular characteristics of the human brain during the lifespan is still not available. Here, we present STAB (a Spatio-Temporal cell Atlas of the human Brain), a database consists of single-cell transcriptomes across multiple brain regions and developmental periods. Right now, STAB contains single-cell gene expression profiling of 42 cell subtypes across 20 brain regions and 11 developmental periods. With STAB, the landscape of cell types and their regional heterogeneity and temporal dynamics across the human brain can be clearly seen, which can help to understand both the development of the normal human brain and the etiology of neuropsychiatric disorders. STAB is available at http://stab.comp-sysbio.org.

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Arun, Prathap R., G. Gopinath, and Bharath Singh Jebaraj. "Spatio-Temporal Analysis on Groundwater - Case Study for Thiruvallur District." E3S Web of Conferences 405 (2023): 04006. http://dx.doi.org/10.1051/e3sconf/202340504006.

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Thiruvallur District in Tamil Nadu is one such district, with groundwater levels reaching depths of 50m to 60m below the surface. Growing populations, variations in land use patterns, reduction in rainfall depth, and other factors all contributed to an increase in water demand and a decrease in water potential. This affects the quality of groundwater as well. In this study, the variation of both Groundwater potential and Groundwater Quality is analyzed. The collected data has undergone thorough processing to ensure its accuracy and relevance for the area to be studied. (Thiruvallur District). The base map for the place to be analyzed and the well-map are created in the QGIS software with the data. The processed data is imported into the QGIS to create a database. With the database, the spatial variation between the stations is mapped. The temporal variation of the stations is carried out. The spatial map is done for the groundwater level and Groundwater quality parameters like pH, TDS, Cl, etc. The temporal variation is shown in the graph for the GW level and GW quality parameters with respect to the well-points.

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Baden-Böhm, Franziska, Mario App, and Jan Thiele. "The FloRes Database: A floral resources trait database for pollinator habitat-assessment generated by a multistep workflow." Biodiversity Data Journal 10 (September 14, 2022): e83523. https://doi.org/10.3897/BDJ.10.e83523.

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The decline of pollinating insects in agricultural landscapes proceeds due to intensive land use and the associated loss of habitat and food sources. The feeding of those insects depends on the spatial and temporal distribution of nectar and pollen as food resource. Hence, to protect insect biodiversity, a spatio-temporal assessment of food quantity of their habitats is necessary. Therefore, sufficient data on traits of floral resources are required.As floral resources' traits of plants are important to quantify food availability, we present two databases, the FloRes Database (Floral Resources Database) and the raw database, from where FloRes was derived. Both databases contain the plant traits: (1) flowering period, (2) floral-unit density per day, (3) nectar volume per floral unit per day, (4) sugar content per floral unit, (5) sugar concentration in nectar, (6) pollen mass or volume per floral unit and per day, (7) protein content of pollen and (8) corolla depth. All traits were sampled from literature and online databases. The raw database consists of 702 specified plant species, 138 unspecified species 37 species (spec., sp), 22 species <i>pluralis</i> (spp) and, for 79, only the genus was identified) and two species complexes (agg.). Those 842 taxa belong to 488 genera and 102 families. Finally, only 27 taxa have a complete set of traits, too few for a sufficient assessment of spatio-temporal availability of floral food-resources.As information on floral resources is scattered throughout many publications with different units, we also present our multistep workflow implemented in five consecutive R-scripts. The multistep workflow standardises the trait units of the raw database to comparable entities with identical units and aggregates them on a reasonable taxonomic level into the second application database, the FloRes Database. Finally, the FloRes Database contains aggregated information of traits for 42 taxa and, when corolla depth is excluded, for 72 taxa.This is the first attempt to gather these eight traits from different literature sources into one database with a multistep workflow. The publication of the multistep workflow enables the users to extend the FloRes Database on their own demands with other literature data or newly-gathered data to improve quantification of food resources. Especially, the combination of pollen, nectar and the open flowers per square metre is, as far as we know, a novelty.The FloRes Database can be used to evaluate the quantity of food-resource habitats available for pollinators, for example, to compare seed mixtures of agri-environmental measures, such as flower strips, considering flower phenology on a daily basis.

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Xie, Jiong, Zhen Chen, Jianwei Liu, et al. "Ganos." Proceedings of the VLDB Endowment 15, no. 12 (2022): 3483–95. http://dx.doi.org/10.14778/3554821.3554838.

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Recently, the trend of developing digital twins for smart cities has driven a need for managing large-scale multidimensional, dynamic, and scene-oriented spatial data. Due to larger data scale and more complex data structure, queries over such data are more complicated and expensive than those on traditional spatial data, which poses challenges to the system efficiency and deployment costs. The existing spatial databases have limited support in both data types and operations. Therefore, a new-generation spatial database with excellent performance and effective deployment costs is needed. This paper presents Ganos, a cloud-native spatial database engine of PolarDB for PostgreSQL that is developed by Alibaba Cloud, to efficiently manage multidimensional, dynamic, and scene-oriented spatial data. Ganos models 3D space and spatio-temporal dynamics as first-class citizens. Also, it natively supports spatial/spatio-temporal data types such as 3DMesh, Trajectory, Raster, PointCloud, etc. Besides, it implements a novel extended-storage mechanism that utilizes cloud-native object storage to reduce storage costs and enable uniform operations on the data in different storages. To facilitate processing "big" queries, Ganos extends PolarDB and provides spatial-oriented multi-level parallelism under the architecture of decoupling compute from storage in cloud-native databases, which achieves elasticity and excellent query performance. We demonstrate Ganos in real-life case studies. The performance of Ganos is evaluated using real datasets, and promising results are obtained. Finally, based on the extensive deployment and application of Ganos, the lessons learned from our customers and the expectations of modern cloud applications for new spatial database features are discussed.

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Richly, Keven, Rainer Schlosser, and Martin Boissier. "Budget-Conscious Fine-Grained Configuration Optimization for Spatio-Temporal Applications." Proceedings of the VLDB Endowment 15, no. 13 (2022): 4079–92. http://dx.doi.org/10.14778/3565838.3565858.

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Based on the performance requirements of modern spatio-temporal data mining applications, in-memory database systems are often used to store and process the data. To efficiently utilize the scarce DRAM capacities, modern database systems support various tuning possibilities to reduce the memory footprint (e.g., data compression) or increase performance (e.g., additional indexes). However, the selection of cost and performance balancing configurations is challenging due to the vast number of possible setups consisting of mutually dependent individual decisions. In this paper, we introduce a novel approach to jointly optimize the compression, sorting, indexing, and tiering configuration for spatio-temporal workloads. Further, we consider horizontal data partitioning, which enables the independent application of different tuning options on a fine-grained level. We propose different linear programming (LP) models addressing cost dependencies at different levels of accuracy to compute optimized tuning configurations for a given workload and memory budgets. To yield maintainable and robust configurations, we extend our LP-based approach to incorporate reconfiguration costs as well as a worst-case optimization for potential workload scenarios. Further, we demonstrate on a real-world dataset that our models allow to significantly reduce the memory footprint with equal performance or increase the performance with equal memory size compared to existing tuning heuristics.

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Guo, Zhan, Mingxin Lu, and Jin Han. "Temporal Graph Attention Network for Spatio-Temporal Feature Extraction in Research Topic Trend Prediction." Mathematics 13, no. 5 (2025): 686. https://doi.org/10.3390/math13050686.

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Comprehensively extracting spatio-temporal features is essential to research topic trend prediction. This necessity arises from the fact that research topics exhibit both temporal trend features and spatial correlation features. This study proposes a Temporal Graph Attention Network (T-GAT) to extract the spatio-temporal features of research topics and predict their trends. In this model, a temporal convolutional layer is employed to extract temporal trend features from multivariate topic time series. Additionally, a multi-head graph attention layer is introduced to capture spatial correlation features among research topics. This layer learns attention scores from the data by using scaled dot product operations and updates edge weights between topics accordingly, thereby mitigating the issue of over-smoothing. Furthermore, we introduce WFtopic-econ and WFtopic-polit, two domain-specific datasets for Chinese research topics constructed from the Wanfang Academic Database. Extensive experiments demonstrate that T-GAT outperforms baseline models in prediction accuracy, with RMSE and MAE being reduced by 4.8% to 7.1% and 14.5% to 18.4%, respectively, while R2 improved by 4.8% to 7.9% across varying observation time steps on the WFtopic-econ dataset. Moreover, on the WFtopic-polit dataset, RMSE and MAE were reduced by 4.0% to 5.3% and 10.0% to 10.7%, respectively, and R2 improved by 7.6% to 14.4%. These results validate the effectiveness of integrating graph attention with temporal convolution to model the spatio-temporal evolution of research topics, providing a robust tool for scholarly trend analysis and decision making.

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San Emeterio de la Parte, Mario, Sara Lana Serrano, Marta Muriel Elduayen, and José-Fernán Martínez-Ortega. "Spatio-Temporal Semantic Data Model for Precision Agriculture IoT Networks." Agriculture 13, no. 2 (2023): 360. http://dx.doi.org/10.3390/agriculture13020360.

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In crop and livestock management within the framework of precision agriculture, scenarios full of sensors and devices are deployed, involving the generation of a large volume of data. Some solutions require rapid data exchange for action or anomaly detection. However, the administration of this large amount of data, which in turn evolves over time, is highly complicated. Management systems add long-time delays to the spatio-temporal data injection and gathering. This paper proposes a novel spatio-temporal semantic data model for agriculture. To validate the model, data from real livestock and crop scenarios, retrieved from the AFarCloud smart farming platform, are modeled according to the proposal. Time-series Database (TSDB) engine InfluxDB is used to evaluate the model against data management. In addition, an architecture for the management of spatio-temporal semantic agricultural data in real-time is proposed. This architecture results in the DAM&DQ system responsible for data management as semantic middleware on the AFarCloud platform. The approach of this proposal is in line with the EU data-driven strategy.

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Zaki, Chamseddine, Christophe Claramunt, Abbass Nasser, and Semar Bahmad. "Merging Spatio-Temporal Objects and Business Processes: Land Reform Process Case Study." Applied Sciences 13, no. 22 (2023): 12372. http://dx.doi.org/10.3390/app132212372.

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Although significant progress has been achieved in the development of spatio-temporal models that capture real-world phenomena, more interaction between phenomenon-based and business-based modeling approaches is still required. This represents a much-needed step forward in balancing the management and scientific aspects of many urban applications. The main goal of this paper is to provide an integrated modeling framework that combines MADS (Modeling of Application Data with Spatio-temporal Features), a specific conceptual spatiotemporal model that successfully represents geographical data, with BPMN (Business Process Model Notation), which models and determines how business processes interact with geographic objects. The integration of MADS and BPMN guarantees clarity, extensibility, and autonomy between data and management operations, as it portrays spatio-temporal abstractions on a conceptual level. This integration offers a unique opportunity to accurately represent, understand, and explain the rationale behind the changes occurring in space and time. The approach has been implemented within a PostgreSQL/PostGIS database management system. A case study implementation shows the practical applicability and potential of this modeling approach.

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Journal articles on the topic 'Database spatio-temporal'

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