Littérature scientifique sur le sujet « Spatio-temporal indexing »
Créez une référence correcte selon les styles APA, MLA, Chicago, Harvard et plusieurs autres
Consultez les listes thématiques d’articles de revues, de livres, de thèses, de rapports de conférences et d’autres sources académiques sur le sujet « Spatio-temporal indexing ».
À côté de chaque source dans la liste de références il y a un bouton « Ajouter à la bibliographie ». Cliquez sur ce bouton, et nous générerons automatiquement la référence bibliographique pour la source choisie selon votre style de citation préféré : APA, MLA, Harvard, Vancouver, Chicago, etc.
Vous pouvez aussi télécharger le texte intégral de la publication scolaire au format pdf et consulter son résumé en ligne lorsque ces informations sont inclues dans les métadonnées.
Articles de revues sur le sujet "Spatio-temporal indexing"
Li, Wu, Wu et 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 (12 novembre 2019) : 512. http://dx.doi.org/10.3390/ijgi8110512.
Texte intégralFeng, Bin, Qing Zhu, Mingwei Liu, Yun Li, Junxiao Zhang, Xiao Fu, Yan Zhou, Maosu Li, Huagui He et 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 (8 septembre 2018) : 371. http://dx.doi.org/10.3390/ijgi7090371.
Texte intégralFatima, Nikhat, Ayesha Ameen et Syed Raziuddin. « STQP : Spatio-Temporal Indexing and Query Processing ». International Journal of Computer Applications 150, no 10 (15 septembre 2016) : 5–9. http://dx.doi.org/10.5120/ijca2016911514.
Texte intégralHe, Zhenwen, Menno-Jan Kraak, Otto Huisman, Xiaogang Ma et Jing Xiao. « Parallel indexing technique for spatio-temporal data ». ISPRS Journal of Photogrammetry and Remote Sensing 78 (avril 2013) : 116–28. http://dx.doi.org/10.1016/j.isprsjprs.2013.01.014.
Texte intégralNi, Jinfeng, et Chinya V. Ravishankar. « Indexing Spatio-Temporal Trajectories with Efficient Polynomial Approximations ». IEEE Transactions on Knowledge and Data Engineering 19, no 5 (mai 2007) : 663–78. http://dx.doi.org/10.1109/tkde.2007.1006.
Texte intégralIdris, F. M., et S. Panchanathan. « Spatio-temporal indexing of vector quantized video sequences ». IEEE Transactions on Circuits and Systems for Video Technology 7, no 5 (1997) : 728–40. http://dx.doi.org/10.1109/76.633489.
Texte intégralCho, Hyung-Ju, et Chin-Wan Chung. « Indexing range sum queries in spatio-temporal databases ». Information and Software Technology 49, no 4 (avril 2007) : 324–31. http://dx.doi.org/10.1016/j.infsof.2006.05.005.
Texte intégralA, John, Sugumaran M et Rajesh R S. « INDEXING AND QUERY PROCESSING TECHNIQUES IN SPATIO-TEMPORAL DATA ». ICTACT Journal on Soft Computing 06, no 03 (1 avril 2016) : 1198–217. http://dx.doi.org/10.21917/ijsc.2016.0167.
Texte intégralVazirgiannis, Michael, Yannis Theodoridis et Timos Sellis. « Spatio-temporal composition and indexing for large multimedia applications ». Multimedia Systems 6, no 4 (1 juillet 1998) : 284–98. http://dx.doi.org/10.1007/s005300050094.
Texte intégralCho, Hyung-Ju, Jun-Ki Min et Chin-Wan Chung. « An adaptive indexing technique using spatio-temporal query workloads ». Information and Software Technology 46, no 4 (mars 2004) : 229–41. http://dx.doi.org/10.1016/j.infsof.2003.07.001.
Texte intégralThèses sur le sujet "Spatio-temporal indexing"
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.
Texte intégralIncludes bibliographical references (leaves 208-215). Also available in electronic version. Access restricted to campus users.
Tamošiūnas, Saulius. « Mobilių objektų indeksavimas duomenų bazėse ». Master's thesis, Lithuanian Academic Libraries Network (LABT), 2014. http://vddb.library.lt/obj/LT-eLABa-0001:E.02~2006~D_20140702_191451-16943.
Texte intégralOver the past few years, there has been a continuous improvement in the wireless communications and the positioning technologies. As a result, tracking the changing positions of continuously moving objects is becoming increasingly feasible and necessary. Databases that deal with objects that change their location and/or shape over time are called spatio-temporal databases. Traditional database approaches for effective information retrieval cannot be used as the moving objects database is highly dynamic. A need for so called spatio-temporal indexing techniques comes to scene. Mainly, by the problem they are addressed to, indices are divided into two groups: a) indexing the past and b) indexing the current and predicted future positions. Also the have been proposed techniques covering both problems. This work is a survey for well known and used indices. Also there is a performance comparison between several past indexing methods. STR Tree, TB Tree and the predecessor of many indices, the R Tree are compared in various aspects using generated datasets of simulated objects movement.
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.
Texte intégralIntelligent 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]
X n.s.
Tsuruda, Renata Miwa. « STB-index : um índice baseado em bitmap para data warehouse espaço-temporal ». Universidade Federal de São Carlos, 2012. https://repositorio.ufscar.br/handle/ufscar/525.
Texte intégralFinanciadora de Estudos e Projetos
The growing concern with the support of the decision-making process has made companies to search technologies that support their decisions. The technology most widely used presently is the Data Warehouse (DW), which allows storing data so it is possible to produce useful and reliable information to assist in strategic decisions. Combining the concepts of Spatial Data Warehouse (SDW), that allows geometry storage and managing, and Temporal Data Warehouse (TDW), which allows storing data changes that occur in the real-world, a research topic known as Spatio-Temporal Data Warehouse (STDW) has emerged. STDW are suitable for the treatment of geometries that change over time. These technologies, combined with the steady growth volume of data, show the necessity of index structures to improve the performance of analytical query processing with spatial predicates and also with geometries that may vary over time. In this sense, this work focused on proposing an index for STDW called Spatio-Temporal Bitmap Index, or STB-index. The proposed index was designed to processing drill-down and roll-up queries considering the existence of predefined spatial hierarchies and with spatial attributes that can vary its position and shape over time. The validation of STB-index was performed by conducting experimental tests using a DWET created from synthetic data. Tests evaluated the elapsed time and the number of disk accesses to construct the index, the amount of storage space of the index and the elapsed time and the number of disk accesses for query processing. Results were compared with query processing using database management system resources and STBindex improved the query performance by 98.12% up to 99.22% in response time compared to materialized views.
A crescente preocupação com o suporte ao processo de tomada de decisão estratégica fez com que as empresas buscassem tecnologias que apoiassem as suas decisões. A tecnologia mais utilizada atualmente é a de Data Warehouse (DW), que permite armazenar dados de forma que seja possível produzir informação útil e confiável para auxiliar na tomada de decisão estratégica. Aliando-se os conceitos de Data Warehouse Espacial (DWE), que permite o armazenamento e o gerenciamento de geometrias, e de Data Warehouse Temporal (DWT), que possibilita representar as mudanças nos dados que ocorrem no mundo real, surgiu o tema de pesquisa conhecido por Data Warehouse Espaço-Temporal (DWET), que é próprio para o tratamento de geometrias que se alteram ao longo do tempo. Essas tecnologias, aliadas ao constante crescimento no volume de dados armazenados, evidenciam a necessidade de estruturas de indexação que melhorem o desempenho do processamento de consultas analíticas com predicados espaciais e com variação das geometrias no tempo. Nesse sentido, este trabalho se concentrou na proposta de um índice para DWET denominado Spatio- Temporal Bitmap Index, ou STB-index. O índice proposto foi projetado para o processamento de consultas do tipo drill-down e roll-up considerando a existência de hierarquias espaciais predefinidas, sendo que os atributos espaciais podem variar sua posição e sua forma ao longo do tempo. A validação do STB-index ocorreu por meio da realização de testes experimentais utilizando um DWET criado a partir de dados sintéticos. Os testes avaliaram o tempo e o número de acessos a disco para a construção do índice, a quantidade de espaço para armazenamento do índice e o tempo e número de acessos a disco para o processamento de consultas analíticas. Os resultados obtidos foram comparados com o processamento de consultas utilizando os recursos disponíveis dos sistemas gerenciadores de banco de dados, sendo que o STB-index apresentou um ganho de desempenho entre 98,12% e 99,22% no tempo de resposta das consultas se comparado ao uso de visões materializadas.
Cortés, Rudyar. « Scalable location-temporal range query processing for structured peer-to-peer networks ». Thesis, Paris 6, 2017. http://www.theses.fr/2017PA066106/document.
Texte intégralIndexing and retrieving data by location and time allows people to share and explore massive geotagged datasets observed on social networks such as Facebook, Flickr, and Twitter. This scenario known as a Location Based Social Network (LBSN) is composed of millions of users, sharing and performing location-temporal range queries in order to retrieve geotagged data generated inside a given geographic area and time interval. A key challenge is to provide a scalable architecture that allow to perform insertions and location-temporal range queries from a high number of users. In order to achieve this, Distributed Hash Tables (DHTs) and the Peer-to-Peer (P2P) computing paradigms provide a powerful building block for implementing large scale applications. However, DHTs are ill-suited for supporting range queries because the use of hash functions destroy data locality for the sake of load balance. Existing solutions that use a DHT as a building block allow to perform range queries. Nonetheless, they do not target location-temporal range queries and they exhibit poor performance in terms of query response time and message traffic. This thesis proposes two scalable solutions for indexing and retrieving geotagged data based on location and time
Ton, That Dai Hai. « Gestion efficace et partage sécurisé des traces de mobilité ». Thesis, Université Paris-Saclay (ComUE), 2016. http://www.theses.fr/2016SACLV003/document.
Texte intégralNowadays, the advances in the development of mobile devices, as well as embedded sensors have permitted an unprecedented number of services to the user. At the same time, most mobile devices generate, store and communicate a large amount of personal information continuously. While managing personal information on the mobile devices is still a big challenge, sharing and accessing these information in a safe and secure way is always an open and hot topic. Personal mobile devices may have various form factors such as mobile phones, smart devices, stick computers, secure tokens or etc. It could be used to record, sense, store data of user's context or environment surrounding him. The most common contextual information is user's location. Personal data generated and stored on these devices is valuable for many applications or services to user, but it is sensitive and needs to be protected in order to ensure the individual privacy. In particular, most mobile applications have access to accurate and real-time location information, raising serious privacy concerns for their users.In this dissertation, we dedicate the two parts to manage the location traces, i.e. the spatio-temporal data on mobile devices. In particular, we offer an extension of spatio-temporal data types and operators for embedded environments. These data types reconcile the features of spatio-temporal data with the embedded requirements by offering an optimal data presentation called Spatio-temporal object (STOB) dedicated for embedded devices. More importantly, in order to optimize the query processing, we also propose an efficient indexing technique for spatio-temporal data called TRIFL designed for flash storage. TRIFL stands for TRajectory Index for Flash memory. It exploits unique properties of trajectory insertion, and optimizes the data structure for the behavior of flash and the buffer cache. These ideas allow TRIFL to archive much better performance in both Flash and magnetic storage compared to its competitors.Additionally, we also investigate the protect user's sensitive information in the remaining part of this thesis by offering a privacy-aware protocol for participatory sensing applications called PAMPAS. PAMPAS relies on secure hardware solutions and proposes a user-centric privacy-aware protocol that fully protects personal data while taking advantage of distributed computing. For this to be done, we also propose a partitioning algorithm an aggregate algorithm in PAMPAS. This combination drastically reduces the overall costs making it possible to run the protocol in near real-time at a large scale of participants, without any personal information leakage
Dvořák, Jan. « Doménové indexy v prostředí Oracle 11g ». Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2011. http://www.nusl.cz/ntk/nusl-237062.
Texte intégralAhmed, Tanvir. « Analytics on Indoor Moving Objects with Applications in Airport Baggage Tracking ». Doctoral thesis, Universite Libre de Bruxelles, 2016. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/231657.
Texte intégralDoctorat en Sciences de l'ingénieur
info:eu-repo/semantics/nonPublished
Martin, Pierre-Etienne. « Détection et classification fines d'actions à partir de vidéos par réseaux de neurones à convolutions spatio-temporelles : Application au tennis de table ». Thesis, Bordeaux, 2020. http://www.theses.fr/2020BORD0313.
Texte intégralAction recognition in videos is one of the key problems in visual data interpretation. Despite intensive research, differencing and recognizing similar actions remains a challenge. This thesis deals with fine-grained classification of sport gestures from videos, with an application to table tennis.In this manuscript, we propose a method based on deep learning for automatically segmenting and classifying table tennis strokes in videos. Our aim is to design a smart system for students and teachers for analyzing their performances. By profiling the players, a teacher can therefore tailor the training sessions more efficiently in order to improve their skills. Players can also have an instant feedback on their performances.For developing such a system with fine-grained classification, a very specific dataset is needed to supervise the learning process. To that aim, we built the “TTStroke-21” dataset, which is composed of 20 stroke classes plus a rejection class. The TTStroke-21 dataset comprises video clips of recorded table tennis exercises performed by students at the sport faculty of the University of Bordeaux - STAPS. These recorded sessions were annotated by professional players or teachers using a crowdsourced annotation platform. The annotations consist in a description of the handedness of the player and information for each stroke performed (starting and ending frames, class of the stroke).Fine-grained action recognition has some notable differences with coarse-grained action recognition. In general, datasets used for coarse-grained action recognition, the background context often provides discriminative information that methods can use to classify the action, rather than focusing on the action itself. In fine-grained classification, where the inter-class similarity is high, discriminative visual features are harder to extract and the motion plays a key role for characterizing an action.In this thesis, we introduce a Twin Spatio-Temporal Convolutional Neural Network. This deep learning network takes as inputs an RGB image sequence and its computed Optical Flow. The RGB image sequence allows our model to capture appearance features while the optical flow captures motion features. Those two streams are processed in parallel using 3D convolutions, and fused at the last stage of the network. Spatio-temporal features extracted in the network allow efficient classification of video clips from TTStroke-21. Our method gets an average classification performance of 87.3% with a best run of 93.2% accuracy on the test set. When applied on joint detection and classification task, the proposed method reaches an accuracy of 82.6%.A systematic study of the influence of each stream and fusion types on classification accuracy has been performed, giving clues on how to obtain the best performances. A comparison of different optical flow methods and the role of their normalization on the classification score is also done. The extracted features are also analyzed by back-tracing strong features from the last convolutional layer to understand the decision path of the trained model. Finally, we introduce an attention mechanism to help the model focusing on particular characteristic features and also to speed up the training process. For comparison purposes, we provide performances of other methods on TTStroke-21 and test our model on other datasets. We notice that models performing well on coarse-grained action datasets do not always perform well on our fine-grained action dataset.The research presented in this manuscript was validated with publications in one international journal, five international conference papers, two international workshop papers and a reconductible task in MediaEval workshop in which participants can apply their action recognition methods to TTStroke-21. Two additional international workshop papers are in process along with one book chapter
Křížová, Martina. « Indexování dat pohybujících se objektů ». Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2010. http://www.nusl.cz/ntk/nusl-237256.
Texte intégralChapitres de livres sur le sujet "Spatio-temporal indexing"
Shekhar, Shashi, et Hui Xiong. « Spatio-temporal Indexing ». Dans Encyclopedia of GIS, 1121. Boston, MA : Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_1321.
Texte intégralHadjieleftheriou, Marios, George Kollios, Vassilis J. Tsotras et Dimitrios Gunopulos. « Indexing Spatio-temporal Archives ». Dans Encyclopedia of GIS, 530–38. Boston, MA : Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_617.
Texte intégralHadjieleftheriou, Marios, George Kollios, Vassilis J. Tsotras et Dimitrios Gunopulos. « Indexing Spatio-temporal Archives ». Dans Encyclopedia of GIS, 1–12. Cham : Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-23519-6_617-2.
Texte intégralShekhar, Shashi, et Hui Xiong. « Indexing API, Spatial/Spatio-temporal ». Dans Encyclopedia of GIS, 497. Boston, MA : Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_599.
Texte intégralShekhar, Shashi, et Hui Xiong. « Indexing Framework, Spatial/Spatio-temporal ». Dans Encyclopedia of GIS, 502. Boston, MA : Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_601.
Texte intégralMokbel, Mohamed F., et Walid G. Aref. « Indexing Historical Spatio-temporal Data ». Dans Encyclopedia of Database Systems, 1–4. New York, NY : Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4899-7993-3_198-2.
Texte intégralMokbel, Mohamed F., et Walid G. Aref. « Indexing Historical Spatio-Temporal Data ». Dans Encyclopedia of Database Systems, 1448–51. Boston, MA : Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-39940-9_198.
Texte intégralŠaltenis, Simonas, et Christian S. Jensen. « Indexing of Objects on the Move ». Dans Mining Spatio-Temporal Information Systems, 21–41. Boston, MA : Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-1149-6_2.
Texte intégralHausser, Roland. « Spatio-temporal Indexing in Database Semantics ». Dans Computational Linguistics and Intelligent Text Processing, 53–68. Berlin, Heidelberg : Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44686-9_5.
Texte intégralBarceló, Lluis, Xavier Orriols et Xavier Binefa. « Spatio-Temporal Decomposition of Sport Events for Video Indexing ». Dans Lecture Notes in Computer Science, 435–45. Berlin, Heidelberg : Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-45113-7_43.
Texte intégralActes de conférences sur le sujet "Spatio-temporal indexing"
Emrich, Tobias, Hans-Peter Kriegel, Nikos Mamoulis, Matthias Renz et Andreas Züfle. « Indexing uncertain spatio-temporal data ». Dans the 21st ACM international conference. New York, New York, USA : ACM Press, 2012. http://dx.doi.org/10.1145/2396761.2396813.
Texte intégralTheodoridis, Yannis, Michael Vazirgiannis et Timos Sellis. « Spatio-temporal indexing for large multimedia applications ». Dans Proceedings of the Third IEEE International Conference on Multimedia Computing and Systems. IEEE, 1996. http://dx.doi.org/10.1109/mmcs.1996.535011.
Texte intégralCai, Yuhan, et Raymond Ng. « Indexing spatio-temporal trajectories with Chebyshev polynomials ». Dans the 2004 ACM SIGMOD international conference. New York, New York, USA : ACM Press, 2004. http://dx.doi.org/10.1145/1007568.1007636.
Texte intégralFox, Anthony, Chris Eichelberger, James Hughes et Skylar Lyon. « Spatio-temporal indexing in non-relational distributed databases ». Dans 2013 IEEE International Conference on Big Data. IEEE, 2013. http://dx.doi.org/10.1109/bigdata.2013.6691586.
Texte intégralZhang, Chong, Xiaoying Chen, Bin Ge et Weidong Xiao. « Indexing historical spatio-temporal data in the cloud ». Dans 2015 IEEE International Conference on Big Data (Big Data). IEEE, 2015. http://dx.doi.org/10.1109/bigdata.2015.7363948.
Texte intégralRodriguez, Felix R., et Manuel Barrena. « Spatio-temporal indexing of the Quikscat wind data ». Dans 2009 IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2009). IEEE, 2009. http://dx.doi.org/10.1109/igarss.2009.5418129.
Texte intégralPiro, Paolo, Sandrine Anthoine, Eric Debreuve et Michel Barlaud. « Scalable Spatio-Temporal Video Indexing Using Sparse Multiscale Patches ». Dans 2009 Seventh International Workshop on Content-Based Multimedia Indexing (CBMI). IEEE, 2009. http://dx.doi.org/10.1109/cbmi.2009.48.
Texte intégralSameh, Megrhi, Souidene Wided, Azeddine Beghdadi et Chokri B. Amar. « Video indexing using salient region based spatio-temporal segmentation approach ». Dans 2012 International Conference on Multimedia Computing and Systems (ICMCS). IEEE, 2012. http://dx.doi.org/10.1109/icmcs.2012.6320204.
Texte intégral« An Efficient Strategy for Spatio-temporal Data Indexing and Retrieval ». Dans International Conference on Knowledge Discovery and Information Retrieval. SciTePress - Science and and Technology Publications, 2012. http://dx.doi.org/10.5220/0004137102270232.
Texte intégralMartin, Pierre-Etienne, Jenny Benois-Pineau, Renaud Peteri et Julien Morlier. « Sport Action Recognition with Siamese Spatio-Temporal CNNs : Application to Table Tennis ». Dans 2018 International Conference on Content-Based Multimedia Indexing (CBMI). IEEE, 2018. http://dx.doi.org/10.1109/cbmi.2018.8516488.
Texte intégral