Academic literature on the topic 'Dynamic spatio-temporal queries'
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Journal articles on the topic "Dynamic spatio-temporal queries"
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Feng, Bin, Qing Zhu, Mingwei Liu, Yun Li, Junxiao Zhang, Xiao Fu, Yan Zhou, Maosu Li, Huagui He, and Weijun Yang. "An Efficient Graph-Based Spatio-Temporal Indexing Method for Task-Oriented Multi-Modal Scene Data Organization." ISPRS International Journal of Geo-Information 7, no. 9 (September 8, 2018): 371. http://dx.doi.org/10.3390/ijgi7090371.
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Task-oriented scene data in big data and cloud environments of a smart city that must be time-critically processed are dynamic and associated with increasing complexities and heterogeneities. Existing hybrid tree-based external indexing methods are input/output (I/O)-intensive, query schema-fixed, and difficult when representing the complex relationships of real-time multi-modal scene data; specifically, queries are limited to a certain spatio-temporal range or a small number of selected attributes. This paper proposes a new spatio-temporal indexing method for task-oriented multi-modal scene data organization. First, a hybrid spatio-temporal index architecture is proposed based on the analysis of the characteristics of scene data and the driving forces behind the scene tasks. Second, a graph-based spatio-temporal relation indexing approach, named the spatio-temporal relation graph (STR-graph), is constructed for this architecture. The global graph-based index, internal and external operation mechanisms, and optimization strategy of the STR-graph index are introduced in detail. Finally, index efficiency comparison experiments are conducted, and the results show that the STR-graph performs excellently in index generation and can efficiently address the diverse requirements of different visualization tasks for data scheduling; specifically, the STR-graph is more efficient when addressing complex and uncertain spatio-temporal relation queries.
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Lee, Seokjun, and Incheol Kim. "A Robotic Context Query-Processing Framework Based on Spatio-Temporal Context Ontology." Sensors 18, no. 10 (October 5, 2018): 3336. http://dx.doi.org/10.3390/s18103336.
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Service robots operating in indoor environments should recognize dynamic changes from sensors, such as RGB-depth (RGB-D) cameras, and recall the past context. Therefore, we propose a context query-processing framework, comprising spatio-temporal robotic context query language (ST-RCQL) and a spatio-temporal robotic context query-processing system (ST-RCQP), for service robots. We designed them based on spatio-temporal context ontology. ST-RCQL can query not only the current context knowledge, but also the past. In addition, ST-RCQL includes a variety of time operators and time constants; thus, queries can be written very efficiently. The ST-RCQP is a query-processing system equipped with a perception handler, working memory, and backward reasoner for real-time query-processing. Moreover, ST-RCQP accelerates query-processing speed by building a spatio-temporal index in the working memory, where percepts are stored. Through various qualitative and quantitative experiments, we demonstrate the high efficiency and performance of the proposed context query-processing framework.
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Xie, Jiong, Zhen Chen, Jianwei Liu, Fang Wang, Feifei Li, Zhida Chen, Yinpei Liu, et al. "Ganos." Proceedings of the VLDB Endowment 15, no. 12 (August 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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Abburu, Sunitha. "GIS Based Interoperable Platform for Disaster Data Exchange Using OGC Standards and Spatial Query." International Journal of Web Portals 9, no. 1 (January 2017): 29–51. http://dx.doi.org/10.4018/ijwp.2017010103.
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Accurate, speedy and interoperable information exchange among the stakeholders achieve effective rescue and relief operations in an emergency. The current research work aims at location-based real time or near real time disaster data gathering and accumulation. The dynamic disaster data is integrated with the static geospatial data to facilitate spatial analytics and disseminate the integrated data through OGC web services to various stakeholders for further processing by different expert domain applications. The research work also facilitates spatio-temporal querying system through Geo-query, and OLAP operations on integrated disaster data with geospatial visualization. The design and implementation of the work is achieved through a mobile application integrated with a GIS based web portal by a centralized remote server. The entire architecture has been tested by implementing in an emergency situation and facilitated by an effective interoperable information exchange and spatio-temporal queries.
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Sibolla, Bolelang H., Serena Coetzee, and Terence L. Van Zyl. "A Framework for Visual Analytics of Spatio-Temporal Sensor Observations from Data Streams." ISPRS International Journal of Geo-Information 7, no. 12 (December 11, 2018): 475. http://dx.doi.org/10.3390/ijgi7120475.
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Sensor networks generate substantial amounts of frequently updated, highly dynamic data that are transmitted as packets in a data stream. The high frequency and continuous unbound nature of data streams leads to challenges when deriving knowledge from the underlying observations. This paper presents (1) a state of the art review into visual analytics of geospatial, spatio-temporal streaming data, and (2) proposes a framework based on the identified gaps from the review. The framework consists of (1) the data model that characterizes the sensor observation data, (2) the user model, which addresses the user queries and manages domain knowledge, (3) the design model, which handles the patterns that can be uncovered from the data and corresponding visualizations, and (4) the visualization model, which handles the rendering of the data. The conclusion from the visualization model is that streaming sensor observations require tools that can handle multivariate, multiscale, and time series displays. The design model reveals that the most useful patterns are those that show relationships, anomalies, and aggregations of the data. The user model highlights the need for handling missing data, dealing with high frequency changes, as well as the ability to review retrospective changes.
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Radoi, Anamaria, and Corneliu Burileanu. "Retrieval of Similar Evolution Patterns from Satellite Image Time Series." Applied Sciences 8, no. 12 (December 1, 2018): 2435. http://dx.doi.org/10.3390/app8122435.
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Technological evolution in the remote sensing domain has allowed the acquisition of large archives of satellite image time series (SITS) for Earth Observation. In this context, the need to interpret Earth Observation image time series is continuously increasing and the extraction of information from these archives has become difficult without adequate tools. In this paper, we propose a fast and effective two-step technique for the retrieval of spatio-temporal patterns that are similar to a given query. The method is based on a query-by-example procedure whose inputs are evolution patterns provided by the end-user and outputs are other similar spatio-temporal patterns. The comparison between the temporal sequences and the queries is performed using the Dynamic Time Warping alignment method, whereas the separation between similar and non-similar patterns is determined via Expectation-Maximization. The experiments, which are assessed on both short and long SITS, prove the effectiveness of the proposed SITS retrieval method for different application scenarios. For the short SITS, we considered two application scenarios, namely the construction of two accumulation lakes and flooding caused by heavy rain. For the long SITS, we used a database formed of 88 Landsat images, and we showed that the proposed method is able to retrieve similar patterns of land cover and land use.
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Eiter, Thomas, Ryutaro Ichise, Josiane Xavier Parreira, Patrik Schneider, and Lihua Zhao. "Deploying spatial-stream query answering in C-ITS scenarios1." Semantic Web 12, no. 1 (November 19, 2020): 41–77. http://dx.doi.org/10.3233/sw-200408.
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Cooperative Intelligent Transport Systems (C-ITS) play an important role for providing the means to collect and exchange spatio-temporal data via V2X-based communication between vehicles and the infrastructure, which will become a central enabler for road safety of (semi)-autonomous vehicles. The Local Dynamic Map (LDM) is a key concept for integrating static and streamed data in a spatial context. The LDM has been semantically enhanced to allow for an elaborate domain model that is captured by a mobility ontology, and for queries over data streams that cater for semantic concepts and spatial relationships. Our approach for semantic enhancement is in the context of ontology-mediated query answering (OQA) and features conjunctive queries over DL-LiteA ontologies that support window operators over streams and spatial relations between spatial objects. In this paper, we show how this approach can be extended to address a wider range of use cases in the three C-ITS scenarios traffic statistics, traffic events detection, and advanced driving assistance systems. We define for the mentioned use cases requirements derived from necessary domain-specific features and report, based on them, on extensions of our query language and ontology model. The extensions include temporal relations, numeric predictions and trajectory predictions as well as optimization strategies such as caching. An experimental evaluation of queries that reflect the requirements has been conducted using the real-world traffic simulation tool PTV Vissim. It provides evidence for the feasibility/efficiency of our approach in the new scenarios.
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Beard, Kate, Melissa Kimble, Jing Yuan, Keith S. Evans, Wei Liu, Damian Brady, and Stephen Moore. "A Method for Heterogeneous Spatio-Temporal Data Integration in Support of Marine Aquaculture Site Selection." Journal of Marine Science and Engineering 8, no. 2 (February 4, 2020): 96. http://dx.doi.org/10.3390/jmse8020096.
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Aquaculture site selection, like most site suitability analyses, requires the assembly and combination of multiple variables. Geographic information systems GIS and multi-criteria evaluation (MCE) based approaches are commonly used for aquaculture site selection and demonstrate the integration of various information sources relevant for siting aquaculture. These analyses, however, tend to be one-time and result in a fixed site suitability plan. Within a dynamic marine environment experiencing potential regime shifts, a siting support tool that integrates new and evolving spatio-temporal data has benefits. This paper presents a flexible Voronoi cell-based GIS model for marine aquaculture siting. Rather than a one-time specification of suitable locations, the approach uses similarity measures on the characteristics of Voronoi cells to find cells with similar characteristics. We calculate a weighted aquaculture site tenure value for Voronoi cells that have been or are occupied by aquaculture farm sites. High scoring cells suggest suitable sites and serve as targets for similarity queries. We apply the approach to a case study on the coast of Maine using an R Shiny application to demonstrate the use of the framework for finding sites with similar characteristics.
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Ray, Suprio, and Bradford Nickerson. "Temporally relevant parallel top-k spatial keyword search." Journal of Spatial Information Science, no. 24 (June 20, 2022): 115–56. http://dx.doi.org/10.5311/josis.2022.24.199.
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New spatio-textual indexing methods are needed to support efficient search and update of the massive amounts of spatially referenced text being generated. Location based services using geo-tagged documents provide valuable ranked recommendations about nearby restaurants, services, sales, emergency events, and visitor attractions. Consequently, top-k spatial keyword search queries (TkSKQ) have received a lot of attention from the research community. Several spatio-textual indexes have been proposed to efficiently support TkSKQ. Some of these indexes support updates based on live document streams, but the ranking schemes employed by them do not simultaneously incorporate temporal relevance, textual similarity and spatial proximity. Moreover, existing approaches have limited or no capability to exploit parallelism with document ingestion and query execution. We present a parallel spatio-textual index, Pastri, to address the aforementioned issues. Pastri can be updated incrementally over real-time spatio-textual document streams. To support temporally relevant ranking of continuously generated document streams, we propose a dynamic ranking scheme. Our approach retrieves the top-k documents that are most temporally relevant at the time of a query execution. We implemented Pastri and we integrate it within a system with a persistent document store and several thread pools to exploit parallelism at various levels. Experimental evaluation involving real-world datasets and synthetic datasets (that we created) demonstrates that our system is able to sustain high document update throughput. Furthermore, Pastri's TkSKQ search performance is one to two orders of magnitude faster than other spatio-textual indexes.
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McFerren, G., and T. van Zyl. "GEOSPATIAL DATA STREAM PROCESSING IN PYTHON USING FOSS4G COMPONENTS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B7 (June 22, 2016): 931–37. http://dx.doi.org/10.5194/isprs-archives-xli-b7-931-2016.
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One viewpoint of current and future IT systems holds that there is an increase in the scale and velocity at which data are acquired and analysed from heterogeneous, dynamic sources. In the earth observation and geoinformatics domains, this process is driven by the increase in number and types of devices that report location and the proliferation of assorted sensors, from satellite constellations to oceanic buoy arrays. Much of these data will be encountered as self-contained messages on data streams - continuous, infinite flows of data. Spatial analytics over data streams concerns the search for spatial and spatio-temporal relationships within and amongst data “on the move”. In spatial databases, queries can assess a store of data to unpack spatial relationships; this is not the case on streams, where spatial relationships need to be established with the incomplete data available. Methods for spatially-based indexing, filtering, joining and transforming of streaming data need to be established and implemented in software components. This article describes the usage patterns and performance metrics of a number of well known FOSS4G Python software libraries within the data stream processing paradigm. In particular, we consider the RTree library for spatial indexing, the Shapely library for geometric processing and transformation and the PyProj library for projection and geodesic calculations over streams of geospatial data. We introduce a message oriented Python-based geospatial data streaming framework called Swordfish, which provides data stream processing primitives, functions, transports and a common data model for describing messages, based on the Open Geospatial Consortium Observations and Measurements (O&M) and Unidata Common Data Model (CDM) standards. We illustrate how the geospatial software components are integrated with the Swordfish framework. Furthermore, we describe the tight temporal constraints under which geospatial functionality can be invoked when processing high velocity, potentially infinite geospatial data streams. The article discusses the performance of these libraries under simulated streaming loads (size, complexity and volume of messages) and how they can be deployed and utilised with Swordfish under real load scenarios, illustrated by a set of Vessel Automatic Identification System (AIS) use cases. We conclude that the described software libraries are able to perform adequately under geospatial data stream processing scenarios - many real application use cases will be handled sufficiently by the software.
Dissertations / Theses on the topic "Dynamic spatio-temporal queries"
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Leone, Marco. "Efficient indexing and retrieval from large moving object databases through dynamic spatio-temporal queries." Doctoral thesis, Universita degli studi di Salerno, 2012. http://hdl.handle.net/10556/987.
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2010 - 2011Intelligent Transportatin Systems have gained a great importance in the last decades given the growing need for security in many public environments, with particular attention for traffic scenarios, which are daily interested by accidents, traffic queues, highway code violations, driving in the wrong lane or on the wrong side, and so on. In the context of camera-based traffic analysis systems, in this thesis I will present a novel indexing scheme for the design of a system for the extraction, the storage and retrieval of moving objects' trajectories from surveillance cameras... [edited by author]
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Book chapters on the topic "Dynamic spatio-temporal queries"
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Ghosh, Shreya, and Jaydeep Das. "Dynamic Voltage and Frequency Scaling Approach for Processing Spatio-Temporal Queries in Mobile Environment." In Green Mobile Cloud Computing, 185–99. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08038-8_9.
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Abbassi, Kamel, and Tahar Ezzedine. "Queries Processing in Wireless Sensor Network." In Wireless Sensor Networks - Design, Deployment and Applications. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.94749.
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For the super-excellence applications used to control the water level in rivers, temperature handles a very large volume of information and does not stop constantly changing. These spatio-temporal data collected by a network of sensors form a set of thematic, integrated, non-volatile and historical data organized to help decision-making. Usually this process is performed with temporal, spatial and spatiotemporal queries. This in turn increases the execution time of the query load. In the literatures, several techniques have been identified such as materialized views (MV), indexes, fragmentation, scheduling, and buffer management. These techniques do not consider the update of the request load and the modification at the database level. In this chapter, we propose an optimal dynamic selection solution based on indexes and VMs. the solution is optimal when it meets the entire workload with a reasonable response time. The proposed approach supports modification at the database level and at the workload level to ensure the validity of the optimal solution for this the knapsack algorithm was used.
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Abburu, Sunitha. "GIS Based Interoperable Platform for Disaster Data Exchange Using OGC Standards and Spatial Query." In Emergency and Disaster Management, 683–706. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-6195-8.ch031.
Full textAbstract:
Accurate, speedy and interoperable information exchange among the stakeholders achieve effective rescue and relief operations in an emergency. The current research work aims at location-based real time or near real time disaster data gathering and accumulation. The dynamic disaster data is integrated with the static geospatial data to facilitate spatial analytics and disseminate the integrated data through OGC web services to various stakeholders for further processing by different expert domain applications. The research work also facilitates spatio-temporal querying system through Geo-query, and OLAP operations on integrated disaster data with geospatial visualization. The design and implementation of the work is achieved through a mobile application integrated with a GIS based web portal by a centralized remote server. The entire architecture has been tested by implementing in an emergency situation and facilitated by an effective interoperable information exchange and spatio-temporal queries.
Conference papers on the topic "Dynamic spatio-temporal queries"
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Mohamed, Mohamed M. Ali, and Ashfaq A. Khokhar. "Dynamic Indexing System for Spatio-temporal Queries in Wireless Sensor Networks." In 2011 12th IEEE International Conference on Mobile Data Management (MDM). IEEE, 2011. http://dx.doi.org/10.1109/mdm.2011.80.
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d'Acierno, Antonio, Marco Leone, Alessia Saggese, and Mario Vento. "A system for storing and retrieving huge amount of trajectory data, allowing spatio-temporal dynamic queries." In 2012 15th International IEEE Conference on Intelligent Transportation Systems - (ITSC 2012). IEEE, 2012. http://dx.doi.org/10.1109/itsc.2012.6338684.
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