Relevant bibliographies by topics / Database spatio-temporal

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Database spatio-temporal'

Author: Grafiati
Published: 3 June 2025
Last updated: 6 July 2025

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Journal articles on the topic "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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Dissertations / Theses on the topic "Database spatio-temporal"

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Sun, Jimeng. "Analysis of predictive spatio-temporal queries /." View Abstract or Full-Text, 2003. http://library.ust.hk/cgi/db/thesis.pl?COMP%202003%20SUN.

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Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2003.<br>Includes bibliographical references (leaves 62-65). Also available in electronic version. Access restricted to campus users.
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Tao, Yufei. "Indexing and query processing of spatio-temporal data /." View Abstract or Full-Text, 2002. http://library.ust.hk/cgi/db/thesis.pl?COMP%202002%20TAO.

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Thesis (Ph. D.)--Hong Kong University of Science and Technology, 2002.<br>Includes bibliographical references (leaves 208-215). Also available in electronic version. Access restricted to campus users.
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Szarmes, Michael C. "Modeling the evolution of spatio-temporal database structures for GIS applications." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp04/mq20885.pdf.

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Nehme, Rimma V. "Continuous query processing on spatio-temporal data streams." Link to electronic thesis, 2005. http://www.wpi.edu/Pubs/ETD/Available/etd-082305-154035/.

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Khan, Mohammed Saquib Akmal. "Efficient Spatio-Temporal Network Analytics in Epidemiological Studies using Distributed Databases." Thesis, Virginia Tech, 2015. http://hdl.handle.net/10919/51223.

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Real-time Spatio-Temporal Analytics has become an integral part of Epidemiological studies. The size of the spatio-temporal data has been increasing tremendously over the years, gradually evolving into Big Data. The processing in such domains are highly data and compute intensive. High performance computing resources resources are actively being used to handle such workloads over massive datasets. This confluence of High performance computing and datasets with Big Data characteristics poses great challenges pertaining to data handling and processing. The resource management of supercomputers is in conflict with the data-intensive nature of spatio-temporal analytics. This is further exacerbated due to the fact that the data management is decoupled from the computing resources. Problems of these nature has provided great opportunities in the growth and development of tools and concepts centered around MapReduce based solutions. However, we believe that advanced relational concepts can still be employed to provide an effective solution to handle these issues and challenges. In this study, we explore distributed databases to efficiently handle spatio-temporal Big Data for epidemiological studies. We propose DiceX (Data Intensive Computational Epidemiology using supercomputers), which couples high-performance, Big Data and relational computing by embedding distributed data storage and processing engines within the supercomputer. It is characterized by scalable strategies for data ingestion, unified framework to setup and configure various processing engines, along with the ability to pause, materialize and restore images of a data session. In addition, we have successfully configured DiceX to support approximation algorithms from MADlib Analytics Library [54], primarily Count-Min Sketch or CM Sketch [33][34][35]. DiceX enables a new style of Big Data processing, which is centered around the use of clustered databases and exploits supercomputing resources. It can effectively exploit the cores, memory and compute nodes of supercomputers to scale processing of spatio-temporal queries on datasets of large volume. Thus, it provides a scalable and efficient tool for data management and processing of spatio-temporal data. Although DiceX has been designed for computational epidemiology, it can be easily extended to different data-intensive domains facing similar issues and challenges. We thank our external collaborators and members of the Network Dynamics and Simulation Science Laboratory (NDSSL) for their suggestions and comments. This work has been partially supported by DTRA CNIMS Contract HDTRA1-11-D-0016-0001, DTRA Validation Grant HDTRA1-11-1-0016, NSF - Network Science and Engineering Grant CNS-1011769, NIH and NIGMS - Models of Infectious Disease Agent Study Grant 5U01GM070694-11. Disclaimer: The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the U.S. Government.<br>Master of Science
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Jin, Meihan. "Un modèle spatio-temporel sémantique pour la modélisation de mobilités en milieu urbain." Thesis, Brest, 2017. http://www.theses.fr/2017BRES0067/document.

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La croissance rapide et la complexité de nombreuses villes contemporaines offrent de nombreux défis de recherche pour les scientifiques à la recherche d'une meilleure compréhension des mobilités qui se produisent dans l'espace et dans le temps. A l’heure où de très grandes séries de données de trajectoires en milieu urbain sont disponibles grâce à profusion de nombreux capteurs de positionnement et de services de nombreuses et nouvelles opportunités de recherche et d’application nous sont offertes. Cependant, une bonne intégration de ces données de mobilité nécessite encore l'élaboration de cadres méthodologiques et conceptuels tout comme la mise en oeuvre de bases de données spatio-temporelles qui offriront les capacités appropriées de représentation et de manipulation des données. La recherche développée dans cette thèse introduit une modélisation conceptuelle et une approche de gestion de base de données spatio-temporelles pour représenter et analyser des trajectoires humaines dans des espaces urbains. Le modèle considère les dimensions spatiales, temporelles et sémantiques afin de tenir compte de l’ensemble des propriétés issues des informations de mobilité. Plusieurs abstractions de données de mobilité et des outils de manipulation de données sont développés et expérimentés à partir d’une large base de données de trajectoires disponibles dans la ville de Pékin. L'intérêt de l'approche est double: il montre d’une part que de larges ensembles de données de mobilité peuvent être intégrés au sein de SGBD spatiotemporels extensibles; d’autre part des outils de manipulation et d’interrogation spécifiques peuvent être dérivés à partir de fonctions intégrées au sein d’un langage d’interrogation. Le potentiel de l’approche est illustré par une série d’interrogations qui montrent comment à partir d’une large base de données de trajectoires quelques patrons de déplacements peuvent être obtenus<br>Massive trajectory datasets generated in modern cities generate not only novel research opportunities but also important methodological challenges for academics and decision-makers searching for a better understanding of travel patterns in space and time. This PhD research is oriented towards the conceptual and GIS-based modeling of human displacements derived from large sets of urban trajectories. The motivation behind this study originates from the necessity to search for and explore travel patterns that emerge from citizens acting in the city. Our research introduces a conceptual modelling framework whose objective is to integrate and analyze human displacements within a GIS-based practical solution. The framework combines conceptual and logical models that represent travel trajectories of citizens moving in a given city. The whole approach has been implemented in a geographical database system, experimented in the context of transportation data, and enriched by a series of query interface manipulations and specific functions that illustrate the potential of our whole framework for urban studies. The whole framework has been experimented on top of the Geolife project and large trajectories datasets available in the city of Beijing. Overall, the findings are twofold: first, it appears that our modelling framework can appropriately act as an extensible geographical database support for the integration of large trajectory datasets; second the approach shows that several emerging human displacements can be explored from the manipulation of large urban trajectories
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Dvořáček, Ondřej. "Dotazování nad časoprostorovými daty pohybujících se objektů." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2009. http://www.nusl.cz/ntk/nusl-236692.

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This master's thesis is devoted to the studies of possibilities, which can be used for representation of moving objects data and for querying such spatio-temporal data. It also shows results of the master's thesis created by Ing. Jaroslav Vališ, that should be used for the solution of this master's thesis. But based on the theoretical grounds defined at the beginning of this work was designed and implemented new database extension for saving and querying spatio-temporal data. Special usage of this extension is demonstrated in an example application. This application uses the database extension for the implementation of its own database functions that are domain specific. At the conclusion, there are presented ways of the farther development of this database extension and the results of this master's thesis are there set into the context of the following project, doctoral thesis "Moving objects database".
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Vališ, Jaroslav. "Databáze pohybujících se objektů." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2008. http://www.nusl.cz/ntk/nusl-235886.

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This work treats the representation of moving objects and operations over these objects. Intro­duces the support for spatio-temporal data in Oracle Database 10g and presents two designs of moving objects database structure. Upon these designs a database was implemented using the user-defined data types. Sample application provides a graphical output of stored spatial data and allows us to call an implemented spatio-temporal operations. Finally, an evaluation of achieved results is done and possible extensions of project are discussed.
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Charfi, Manel. "Declarative approach for long-term sensor data storage." Thesis, Lyon, 2017. http://www.theses.fr/2017LYSEI081/document.

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De nos jours, on a de plus en plus de capteurs qui ont tendance à apporter confort et facilité dans notre vie quotidienne. Ces capteurs sont faciles à déployer et à intégrer dans une variété d’applications (monitoring de bâtiments intelligents, aide à la personne,...). Ces milliers (voire millions)de capteurs sont de plus en plus envahissants et génèrent sans arrêt des masses énormes de données qu’on doit stocker et gérer pour le bon fonctionnement des applications qui en dépendent. A chaque fois qu'un capteur génère une donnée, deux dimensions sont d'un intérêt particulier : la dimension temporelle et la dimension spatiale. Ces deux dimensions permettent d'identifier l'instant de réception et la source émettrice de chaque donnée. Chaque dimension peut se voir associée à une hiérarchie de granularités qui peut varier selon le contexte d'application. Dans cette thèse, nous nous concentrons sur les applications nécessitant une conservation à long terme des données issues des flux de données capteurs. Notre approche vise à contrôler le stockage des données capteurs en ne gardant que les données jugées pertinentes selon la spécification des granularités spatio-temporelles représentatives des besoins applicatifs, afin d’améliorer l'efficacité de certaines requêtes. Notre idée clé consiste à emprunter l'approche déclarative développée pour la conception de bases de données à partir de contraintes et d'étendre les dépendances fonctionnelles avec des composantes spatiales et temporelles afin de revoir le processus classique de normalisation de schéma de base de données. Étant donné des flux de données capteurs, nous considérons à la fois les hiérarchies de granularités spatio-temporelles et les Dépendances Fonctionnelles SpatioTemporelles (DFSTs) comme objets de premier ordre pour concevoir des bases de données de capteurs compatibles avec n'importe quel SGBDR. Nous avons implémenté un prototype de cette architecture qui traite à la fois la conception de la base de données ainsi que le chargement des données. Nous avons mené des expériences avec des flux de donnés synthétiques et réels provenant de bâtiments intelligents. Nous avons comparé notre solution avec la solution de base et nous avons obtenu des résultats prometteurs en termes de performance de requêtes et d'utilisation de la mémoire. Nous avons également étudié le compromis entre la réduction des données et l'approximation des données<br>Nowadays, sensors are cheap, easy to deploy and immediate to integrate into applications. These thousands of sensors are increasingly invasive and are constantly generating enormous amounts of data that must be stored and managed for the proper functioning of the applications depending on them. Sensor data, in addition of being of major interest in real-time applications, e.g. building control, health supervision..., are also important for long-term reporting applications, e.g. reporting, statistics, research data... Whenever a sensor produces data, two dimensions are of particular interest: the temporal dimension to stamp the produced value at a particular time and the spatial dimension to identify the location of the sensor. Both dimensions have different granularities that can be organized into hierarchies specific to the concerned context application. In this PhD thesis, we focus on applications that require long-term storage of sensor data issued from sensor data streams. Since huge amount of sensor data can be generated, our main goal is to select only relevant data to be saved for further usage, in particular long-term query facilities. More precisely, our aim is to develop an approach that controls the storage of sensor data by keeping only the data considered as relevant according to the spatial and temporal granularities representative of the application requirements. In such cases, approximating data in order to reduce the quantity of stored values enhances the efficiency of those queries. Our key idea is to borrow the declarative approach developed in the seventies for database design from constraints and to extend functional dependencies with spatial and temporal components in order to revisit the classical database schema normalization process. Given sensor data streams, we consider both spatio-temporal granularity hierarchies and Spatio-Temporal Functional Dependencies (STFDs) as first class-citizens for designing sensor databases on top of any RDBMS. We propose a specific axiomatisation of STFDs and the associated attribute closure algorithm, leading to a new normalization algorithm. We have implemented a prototype of this architecture to deal with both database design and data loading. We conducted experiments with synthetic and real-life data streams from intelligent buildings
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Carrera, Hernández Jaime Jesús. "Spatio-temporal analysis of aquifer recharge and groundwater potentiometric levels in the Basin of Mexico through the development of a regional database and an open source tool for groundwater flow modelling." Thesis, McGill University, 2007. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=18446.

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The Basin of Mexico, where the Mexico City Metropolitan Zone (MCMZ) and its 20 million inhabitants are located, has had a two-sided approach towards water management, as it has struggled to drain the lakes that once covered this region while at the same time it started to transport in water from adjacent basins in the 1950s for water supply. In addition, the large amount of water extracted from the Basin's aquifer has caused drawdown of the groundwater table and consequently, land subsidence which reaches 40 cm/yr in some areas.The inhabitants of the Basin of Mexico, which comprises five different political entities and in which different agencies are in charge of water supply rely on the Basin's aquifer system as its main water supply source. After analyzing the existing water management policies in the Basin, this work suggests that in order to improve water management a regional groundwater flow model is needed. In order to develop this regional model, different tasks need to be fulfilled: a regional database is needed and regional estimates of aquifer recharge are also required. In order to develop a regional hydrogeological database in this area, the use of both a Relational Database Management System (RDBMS) and a Geographic Information System (GIS) is proposed in order to improve regional data management in the study area. Data stored in this new database, the Basin of Mexico Hydrogeological Database (BMHDB) comprises data on climatological, borehole and runoff variables, readily providing information for the development of hydrogeological models.This work presents a daily soil water balance which uses different vegetation and soil types as well as the effect of topography on climatological variables and evapotranspiration used to estimate recharge to the regional aquifer. Through the application of this model, it has been shown that the mountains that enclose the Basin of Mexico are the main recharge areas of the Basin's regional aquifer system. The spati<br>La gestion de l'eau dans le bassin de Mexico (où se trouve la zone métropolitaine de la ville de Mexico avec ses 20 millions d'habitants), a eu deux approches opposées: d'abord on a lutté pour vider les lacs qui couvraient cette région auparavant puis dans les années 50 on commencé a importer de l'eau d'autres bassins.De plus, les grandes quantités d'eau extraites de la couche aquifère du bassin ont causé l'abaissement du niveau de la table d'eaux souterraines et par conséquent, l'aisément du terrain jusqu'à 40 cm/année dans quelques secteurs.Les habitants du Bassin de Mexico, qui comprend 5 entités politiques et où plusieurs organismes sont chargés de la gestion de l'eau, obtiennent leur eau essentiellement de l'aquifère. Après analyse des politiques actuelles de gestion de l'eau du bassin, ce travail suggère qu'a fin d'améliorer cette gestion, un modèle régional d'écoulement d'eaux souterraines est nécessaire. A fin de développer ce modèle régional il faut: une base de données hydrogéologiques et des évaluations de la recharge de la couche aquifère. En ce qui concerne la base de données hydrogéologiques régionale, on propose l'utilisation d'une base de données relationnelle et d'un système d'information géographique, ceci a fin d'améliorer la gestion des données. Les données stockées dans cette nouvelle base de données: "Base de Données Hydrogéologiques du Bassin de Mexico" sont des variables climatologiques, de forage et d'écoulement, fournissant aisément des informations pour le développement des modèles hydrogéologiques.Ce travail présente un bilan quotidien de l'eau (qui considère différents types de végétation et de sol ainsi que l'effet de la topographie sur des variables climatologiques et sur l'evapotranspiration) lequel est employé pour estimer la recharge à la couche aquifère régionale. Par l'application de ce modèle, on a montré que les montagnes qui renferment le bassin du Mexique sont les
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Books on the topic "Database spatio-temporal"

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Böhlen, Michael H., Christian S. Jensen, and Michel O. Scholl, eds. Spatio-Temporal Database Management. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48344-6.

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Kevin, Shaw, and Abdelguerfi Mahdi, eds. Mining spatio-temporal information systems. Kluwer Academic Publishers, 2002.

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Caluwe, Rita. Spatio-Temporal Databases: Flexible Querying and Reasoning. Springer Berlin Heidelberg, 2004.

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Ladner, Roy. Mining Spatio-Temporal Information Systems. Springer US, 2002.

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George, Betsy. Spatio-temporal Networks: Modeling and Algorithms. Springer New York, 2013.

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de Caluwe, Rita, Guy de Tré, and Gloria Bordogna, eds. Spatio-Temporal Databases. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-662-09968-1.

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Sellis, Timos K., Manolis Koubarakis, Andrew Frank, et al., eds. Spatio-Temporal Databases. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/b83622.

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Vieira, Marcos R., and Vassilis J. Tsotras. Spatio-Temporal Databases. Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-02408-0.

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1935-, Lasker G. E., International Institute for Advanced Studies in Systems Research and Cybernetics., and International Conference on Systems Research, Informatics, and Cybernetics (7th : 1994 : Baden-Baden, Germany), eds. Advances in database and expert systems: Federated and replicated databases, spatio-temporal databases, multi database systems, database design methodologies, complex object modeling, knowledge and knowledge-based systems, computer-based decison [sic] support systems, expert systems in a distributed database environment, formal models of legal reasoning, legal databases and legal expert systems. International Institute for Advanced Studies in Systems Research and Cybernetics, 1995.

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service), SpringerLink (Online, ed. Introduction to Databases: From Biological to Spatio-Temporal. Springer-Verlag London, 2010.

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Book chapters on the topic "Database spatio-temporal"

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Xiong, Xiaopeng, Mohamed F. Mokbel, and Walid G. Aref. "Spatio-Temporal Database." In Encyclopedia of GIS. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-23519-6_1316-2.

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Xiong, Xiaopeng, Mohamed F. Mokbel, and Walid G. Aref. "Spatio-temporal Database." In Encyclopedia of GIS. Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_1316.

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Yazici, Adnan, Öznur Yavuz, and Roy George. "An MPEG-7 Based Video Database Management System." In Spatio-Temporal Databases. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-662-09968-1_9.

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Frentzos, Elias, Yannis Theodoridis, and Apostolos N. Papadopoulos. "Spatio-Temporal Trajectories." In Encyclopedia of Database Systems. Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4899-7993-3_364-2.

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Frentzos, Elias, Yannis Theodoridis, and Apostolos N. Papadopoulos. "Spatio-Temporal Trajectories." In Encyclopedia of Database Systems. Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-39940-9_364.

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de Tré, Guy, Rita de Caluwe, Jörg Verstraete, and Axel Hallez. "The Applicability of Generalized Constraints in Spatio-Temporal Database Modelling and Querying." In Spatio-Temporal Databases. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-662-09968-1_7.

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Thériault, Marius, Christophe Claramunt, and Paul Y. Villeneuve. "A Spatio-Temporal Taxonomy for the Representation of Spatial Set Behaviours." In Spatio-Temporal Database Management. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48344-6_1.

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Nascimento, Mario A., Jefferson R. O. Silva, and Yannis Theodoridis. "Evaluation of Access Structures for Discretely Moving Points." In Spatio-Temporal Database Management. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48344-6_10.

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Wijsen, Jef, and Raymond T. Ng. "Temporal Dependencies Generalized for Spatial and Other Dimensions." In Spatio-Temporal Database Management. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48344-6_11.

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Koubarakis, Manolis, and Spiros Skiadopoulos. "Tractable Query Answering in Indefinite Constraint Databases: Basic Results and Applications to Querying Spatiotemporal Information." In Spatio-Temporal Database Management. Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48344-6_12.

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Conference papers on the topic "Database spatio-temporal"

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Zhu, Dan, Xiangyu Wang, Cheng Huang, Peilin Han, Wei Hu, and Jianfeng Ma. "Enabling Efficient Spatio-Temporal Range Query over Encrypted Databases." In GLOBECOM 2024 - 2024 IEEE Global Communications Conference. IEEE, 2024. https://doi.org/10.1109/globecom52923.2024.10901112.

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Wang, Sheng-sheng, Da-you Liu, Juan Chen, and Hai-yang Jia. "Qualitative Spatio-Temporal Database." In 2006 International Conference on Machine Learning and Cybernetics. IEEE, 2006. http://dx.doi.org/10.1109/icmlc.2006.259132.

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Emrich, Tobias, Hans-Peter Kriegel, Markus Mauder, Matthias Renz, Goce Trajcevski, and Andreas Züfle. "Minimal spatio-temporal database repairs." In SIGSPATIAL'13: 21st SIGSPATIAL International Conference on Advances in Geographic Information Systems. ACM, 2013. http://dx.doi.org/10.1145/2525314.2525468.

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de Sede, M. H., A. Moine, and P. Marceau. "Systemic approach for spatio-temporal database structuration." In Proceedings. Tenth International Workshop on Database and Expert Systems Applications. DEXA 99. IEEE, 1999. http://dx.doi.org/10.1109/dexa.1999.795210.

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Bodolay, Michael, and Martha L. Escobar-Molano. "A schema-less spatio-temporal database system." In the 2000 ACM symposium. ACM Press, 2000. http://dx.doi.org/10.1145/335603.335839.

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Böhlen, Michael, Christian S. Jensen, and Bjørn Skjellaug. "Spatio-temporal database support for legacy applications." In the 1998 ACM symposium. ACM Press, 1998. http://dx.doi.org/10.1145/330560.330675.

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Belussi, Alberto, Carlo Combi, and Gabriele Pozzani. "Formal and conceptual modeling of spatio-temporal granularities." In the 2009 International Database Engineering & Applications Symposium. ACM Press, 2009. http://dx.doi.org/10.1145/1620432.1620462.

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Erwig, M., and M. Schneider. "Developments in spatio-temporal query languages." In Proceedings. Tenth International Workshop on Database and Expert Systems Applications. DEXA 99. IEEE, 1999. http://dx.doi.org/10.1109/dexa.1999.795206.

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Liu, Xiaohua, and Youchuan Wan. "Storing Spatio-Temporal Data in XML Native Database." In 2010 2nd International Workshop on Database Technology and Applications (DBTA). IEEE, 2010. http://dx.doi.org/10.1109/dbta.2010.5659107.

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Musdholifah, Aina, Siti Zaiton Bt Mohd Hashim, and Ito Wasito. "KNN-kernel based clustering for spatio-temporal database." In 2010 International Conference on Computer and Communication Engineering (ICCCE). IEEE, 2010. http://dx.doi.org/10.1109/iccce.2010.5556805.

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