Literatura académica sobre el tema "Complex temporal data"
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Artículos de revistas sobre el tema "Complex temporal data"
Käfer, Wolfgang y Harald Schöning. "Realizing a temporal complex-object data model". ACM SIGMOD Record 21, n.º 2 (junio de 1992): 266–75. http://dx.doi.org/10.1145/141484.130323.
Texto completoHarada, Lilian. "Detection of complex temporal patterns over data streams". Information Systems 29, n.º 6 (septiembre de 2004): 439–59. http://dx.doi.org/10.1016/j.is.2003.10.004.
Texto completoKvet, Michal, Emil Kršák y Karol Matiaško. "Study on Effective Temporal Data Retrieval Leveraging Complex Indexed Architecture". Applied Sciences 11, n.º 3 (20 de enero de 2021): 916. http://dx.doi.org/10.3390/app11030916.
Texto completoCappello, C., S. De Iaco, S. Maggio y D. Posa. "Modeling spatio-temporal complex covariance functions for vectorial data". Spatial Statistics 47 (marzo de 2022): 100562. http://dx.doi.org/10.1016/j.spasta.2021.100562.
Texto completoWu, Xing, Shuai Mao, Luolin Xiong y Yang Tang. "A survey on temporal network dynamics with incomplete data". Electronic Research Archive 30, n.º 10 (2022): 3786–810. http://dx.doi.org/10.3934/era.2022193.
Texto completoWu, X., R. Zurita-Milla, M. J. Kraak y E. Izquierdo-Verdiguier. "CLUSTERING-BASED APPROACHES TO THE EXPLORATION OF SPATIO-TEMPORAL DATA". ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W7 (14 de septiembre de 2017): 1387–91. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w7-1387-2017.
Texto completoParra, R. Gonzalo, Nikolaos Papadopoulos, Laura Ahumada-Arranz, Jakob El Kholtei, Noah Mottelson, Yehor Horokhovsky, Barbara Treutlein y Johannes Soeding. "Reconstructing complex lineage trees from scRNA-seq data using MERLoT". Nucleic Acids Research 47, n.º 17 (20 de agosto de 2019): 8961–74. http://dx.doi.org/10.1093/nar/gkz706.
Texto completoPorch, William y Daniel Rodriguez. "Spatial Interpolation of Meteorological Data in Complex Terrain Using Temporal Statistics". Journal of Climate and Applied Meteorology 26, n.º 12 (diciembre de 1987): 1696–708. http://dx.doi.org/10.1175/1520-0450(1987)026<1696:siomdi>2.0.co;2.
Texto completoSong, Chao, Youfang Lin, Shengnan Guo y Huaiyu Wan. "Spatial-Temporal Synchronous Graph Convolutional Networks: A New Framework for Spatial-Temporal Network Data Forecasting". Proceedings of the AAAI Conference on Artificial Intelligence 34, n.º 01 (3 de abril de 2020): 914–21. http://dx.doi.org/10.1609/aaai.v34i01.5438.
Texto completoKosiuczenko, Piotr. "An Interval Temporal Logic for Time Series Specification and Data Integration". Remote Sensing 13, n.º 12 (8 de junio de 2021): 2236. http://dx.doi.org/10.3390/rs13122236.
Texto completoTesis sobre el tema "Complex temporal data"
Renz, Matthias. "Enhanced query processing on complex spatial and temporal data". Diss., [S.l.] : [s.n.], 2006. http://edoc.ub.uni-muenchen.de/archive/00006231.
Texto completoPacella, Massimo. "High-dimensional statistics for complex data". Doctoral thesis, Universita degli studi di Salerno, 2018. http://hdl.handle.net/10556/3016.
Texto completoHigh dimensional data analysis has become a popular research topic in the recent years, due to the emergence of various new applications in several fields of sciences underscoring the need for analysing massive data sets. One of the main challenge in analysing high dimensional data regards the interpretability of estimated models as well as the computational efficiency of procedures adopted. Such a purpose can be achieved through the identification of relevant variables that really affect the phenomenon of interest, so that effective models can be subsequently constructed and applied to solve practical problems. The first two chapters of the thesis are devoted in studying high dimensional statistics for variable selection. We firstly introduce a short but exhaustive review on the main developed techniques for the general problem of variable selection using nonparametric statistics. Lastly in chapter 3 we will present our proposal regarding a feature screening approach for non additive models developed by using of conditional information in the estimation procedure... [edited by Author]
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Törmänen, Patrik. "Forecasting important disease spreaders from temporal contact data". Thesis, Umeå universitet, Institutionen för fysik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-56747.
Texto completoSchaidnagel, Michael. "Automated feature construction for classification of complex, temporal data sequences". Thesis, University of the West of Scotland, 2016. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.692834.
Texto completoGao, Feng. "Complex medical event detection using temporal constraint reasoning". Thesis, University of Aberdeen, 2010. http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?pid=153271.
Texto completoAhmad, Saif. "A temporal pattern identification and summarization method for complex time serial data". Thesis, University of Surrey, 2007. http://epubs.surrey.ac.uk/843297/.
Texto completoJones-Todd, Charlotte M. "Modelling complex dependencies inherent in spatial and spatio-temporal point pattern data". Thesis, University of St Andrews, 2017. http://hdl.handle.net/10023/12009.
Texto completoIACOBELLO, GIOVANNI. "Spatio-temporal analysis of wall-bounded turbulence: A multidisciplinary perspective via complex networks". Doctoral thesis, Politecnico di Torino, 2020. http://hdl.handle.net/11583/2829683.
Texto completoEl, Ouassouli Amine. "Discovering complex quantitative dependencies between interval-based state streams". Thesis, Lyon, 2020. http://www.theses.fr/2020LYSEI061.
Texto completoThe increasing utilization of sensor devices in addition to human-given data make it possible to capture real world systems complexity through rich temporal descriptions. More precisely, the usage of a multitude of data sources types allows to monitor an environment by describing the evolution of several of its dimensions through data streams. One core characteristic of such configurations is heterogeneity that appears at different levels of the data generation process: data sources, time models and data models. In such context, one challenging task for monitoring systems is to discover non-trivial temporal knowledge that is directly actionable and suitable for human interpretation. In this thesis, we firstly propose to use a Temporal Abstraction (TA) approach to express information given by heterogeneous raw data streams with a unified interval-based representation, called state streams. A state reports on a high level environment configuration that is of interest for an application domain. Such approach solves problems introduced by heterogeneity, provides a high level pattern vocabulary and also permits also to integrate expert(s) knowledge into the discovery process. Second, we introduced the Complex Temporal Dependencies (CTD) that is a quantitative interval-based pattern model. It is defined similarly to a conjunctive normal form and allows to express complex temporal relations between states. Contrary to the majority of existing pattern models, a CTD is evaluated with automatic statistical assessment of streams intersection avoiding the use of any significance user-given parameter. Third, we proposed CTD-Miner a first efficient CTD mining framework. CTD-Miner performs an incremental dependency construction. CTD-Miner benefits from pruning techniques based on a statistical correspondence relationship that aims to accelerate the exploration search space by reducing redundant information and provide a more usable result set. Finally, we proposed the Interval Time Lag Discovery (ITLD) algorithm. ITLD is based on a confidence variation heuristic that permits to reduce the complexity of the pairwise dependency discovery process from quadratic to linear w.r.t a temporal constraint Δ on time lags. Experiments on simulated and real world data showed that ITLD provides efficiently more accurate results in comparison with existing approaches. Hence, ITLD enhances significantly the accuracy, performances and scalability of CTD-Miner. The encouraging results given by CTD-Miner on our real world motion data set suggests that it is possible to integrate insights given by real time video processing approaches in a knowledge discovery process opening interesting perspectives for monitoring smart environments
Sherwin, Jason. "A computational approach to achieve situational awareness from limited observations of a complex system". Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/33955.
Texto completoLibros sobre el tema "Complex temporal data"
Pernet, Bruno, ed. Larval Feeding: Mechanisms, Rates, and Performance in Nature. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198786962.003.0007.
Texto completoWikle, Christopher K. Spatial Statistics. Oxford University Press, 2018. http://dx.doi.org/10.1093/acrefore/9780190228620.013.710.
Texto completoHammond, Christopher J., Marc N. Potenza y Linda C. Mayes. Development of Impulse Control, Inhibition, and Self-Regulatory Behaviors in Normative Populations across the Lifespan. Editado por Jon E. Grant y Marc N. Potenza. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780195389715.013.0082.
Texto completoKomlos, John y Inas R. Kelly, eds. The Oxford Handbook of Economics and Human Biology. Oxford University Press, 2015. http://dx.doi.org/10.1093/oxfordhb/9780199389292.001.0001.
Texto completoHoldaway, Simon y Patricia Fanning. Geoarchaeology of Aboriginal Landscapes in Semi-arid Australia. CSIRO Publishing, 2014. http://dx.doi.org/10.1071/9780643108950.
Texto completoEl-Bushra, Judy. How Should We Explain the Recurrence of Violent Conflict, and What Might Gender Have to Do with It? Editado por Fionnuala Ní Aoláin, Naomi Cahn, Dina Francesca Haynes y Nahla Valji. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199300983.013.5.
Texto completoMaher, Garret. Highly Skilled Lebanese Transnational Migrants. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190608873.003.0009.
Texto completoTeitelbaum, Michael S. High-Skilled Migration Policy Challenges from a US Perspective. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198815273.003.0007.
Texto completoSime, Stuart. 30. Striking Out, Discontinuance, and Stays. Oxford University Press, 2018. http://dx.doi.org/10.1093/he/9780198823100.003.3500.
Texto completoSime, Stuart. 30. Striking out, discontinuance, and stays. Oxford University Press, 2017. http://dx.doi.org/10.1093/he/9780198787570.003.3500.
Texto completoCapítulos de libros sobre el tema "Complex temporal data"
Kamps, Oliver y Joachim Peinke. "Analysis of Noisy Spatio-Temporal Data". En Understanding Complex Systems, 319–24. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27635-9_22.
Texto completoFriedrich, R., V. K. Jirsa, H. Haken y C. Uhl. "Analyzing Spatio-Temporal Patterns of Complex Systems". En Nonlinear Analysis of Physiological Data, 101–14. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-642-71949-3_7.
Texto completoEckardt, Matthias. "Reviewing Graphical Modelling of Multivariate Temporal Processes". En Analysis of Large and Complex Data, 221–29. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-25226-1_19.
Texto completoMorik, Katharina. "Some Machine Learning Approaches to the Analysis of Temporal Data". En Robustness and Complex Data Structures, 279–99. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-35494-6_17.
Texto completoPray, Keith A. y Carolina Ruiz. "Mining Expressive Temporal Associations from Complex Data". En Machine Learning and Data Mining in Pattern Recognition, 384–94. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11510888_38.
Texto completoSacchi, Lucia, Arianna Dagliati y Riccardo Bellazzi. "Analyzing Complex Patients’ Temporal Histories: New Frontiers in Temporal Data Mining". En Methods in Molecular Biology, 89–105. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-1985-7_6.
Texto completoHarada, Lilian. "Complex Temporal Patterns Detection over Continuous Data Streams". En Advances in Databases and Information Systems, 401–14. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45710-0_32.
Texto completoBueno, Renato, Daniel S. Kaster, Agma Juci Machado Traina y Caetano Traina. "Time-Aware Similarity Search: A Metric-Temporal Representation for Complex Data". En Advances in Spatial and Temporal Databases, 302–19. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02982-0_20.
Texto completoLoglisci, Corrado, Michelangelo Ceci, Angelo Impedovo y Donato Malerba. "Mining Spatio-Temporal Patterns of Periodic Changes in Climate Data". En New Frontiers in Mining Complex Patterns, 198–212. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61461-8_13.
Texto completoLima Graf, Jeniffer, Srđan Krstić y Joshua Schneider. "Metric First-Order Temporal Logic with Complex Data Types". En Runtime Verification, 126–47. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-44267-4_7.
Texto completoActas de conferencias sobre el tema "Complex temporal data"
Fogaça, Isis Caroline Oliveira de Sousa y Renato Bueno. "Temporal Evolution of Complex Data". En XXXV Simpósio Brasileiro de Banco de Dados. Sociedade Brasileira de Computação - SBC, 2020. http://dx.doi.org/10.5753/sbbd.2020.13622.
Texto completoHu, Xiao, Stavros Sintos, Junyang Gao, Pankaj K. Agarwal y Jun Yang. "Computing Complex Temporal Join Queries Efficiently". En SIGMOD/PODS '22: International Conference on Management of Data. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3514221.3517893.
Texto completoVerhein, Florian. "Mining Complex Spatio-Temporal Sequence Patterns". En Proceedings of the 2009 SIAM International Conference on Data Mining. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2009. http://dx.doi.org/10.1137/1.9781611972795.52.
Texto completoKäfer, Wolfgang y Harald Schöning. "Realizing a temporal complex-object data model". En the 1992 ACM SIGMOD international conference. New York, New York, USA: ACM Press, 1992. http://dx.doi.org/10.1145/130283.130323.
Texto completoZheng, Yang, Annies Ductan, Devin Thomas y Mohamed Y. Eltabakh. "Complex Patten Processing in Spatio-temporal Databases". En 3rd International Conference on Data Management Technologies and Applications. SCITEPRESS - Science and and Technology Publications, 2014. http://dx.doi.org/10.5220/0004992401570169.
Texto completoFeng, Xin y Odilon K. Senyana. "Mining Multiple Temporal Patterns of complex dynamic data systems". En 2009 IEEE Symposium on Computational Intelligence and Data Mining (CIDM). IEEE, 2009. http://dx.doi.org/10.1109/cidm.2009.4938679.
Texto completoOuassouli, Amine El, Lionel Robinault y Vasile-Marian Scuturici. "Mining complex temporal dependencies from heterogeneous sensor data streams". En the 23rd International Database Applications & Engineering Symposium. New York, New York, USA: ACM Press, 2019. http://dx.doi.org/10.1145/3331076.3331112.
Texto completoChen, Yueguo, Shouxu Jiang, Beng Chin Ooi y Anthony K. H. Tung. "Querying Complex Spatio-Temporal Sequences in Human Motion Databases". En 2008 IEEE 24th International Conference on Data Engineering (ICDE 2008). IEEE, 2008. http://dx.doi.org/10.1109/icde.2008.4497417.
Texto completoGal, Avigdor, Arik Senderovich y Matthias Weidlich. "Online Temporal Analysis of Complex Systems Using IoT Data Sensing". En 2018 IEEE 34th International Conference on Data Engineering (ICDE). IEEE, 2018. http://dx.doi.org/10.1109/icde.2018.00224.
Texto completoNielsen, Allan A., Henning Skriver y Knut Conradsen. "Complex Wishart Distribution Based Analysis of Polarimetric Synthetic Aperture Radar Data". En 2007 International Workshop on the Analysis of Multi-Temporal Remote Sensing Images. IEEE, 2007. http://dx.doi.org/10.1109/multitemp.2007.4293078.
Texto completoInformes sobre el tema "Complex temporal data"
Koopmann, Patrick. Ontology-Mediated Query Answering for Probabilistic Temporal Data with EL Ontologies (Extended Version). Technische Universität Dresden, 2018. http://dx.doi.org/10.25368/2022.242.
Texto completoJohansen, Richard, Alan Katzenmeyer, Kaytee Pokrzywinski y Molly Reif. A review of sensor-based approaches for monitoring rapid response treatments of cyanoHABs. Engineer Research and Development Center (U.S.), julio de 2023. http://dx.doi.org/10.21079/11681/47261.
Texto completoLauth, Timothy, David Biedenharn, Travis Dahl, Casey Mayne, Keaton Jones, Charles Little, Joseph Dunbar, Samantha Lucker y Nalini Torres. Technical assessment of the Old, Mississippi, Atchafalaya, and Red (OMAR) Rivers : geomorphic assessment. Engineer Research and Development Center (U.S.), agosto de 2022. http://dx.doi.org/10.21079/11681/45143.
Texto completoTaucher, Jan y Markus Schartau. Report on parameterizing seasonal response patterns in primary- and net community production to ocean alkalinization. OceanNETs, noviembre de 2021. http://dx.doi.org/10.3289/oceannets_d5.2.
Texto completoBorgwardt, Stefan y Veronika Thost. Temporal Query Answering in EL. Technische Universität Dresden, 2015. http://dx.doi.org/10.25368/2022.214.
Texto completoWilson, D., Matthew Kamrath, Caitlin Haedrich, Daniel Breton y Carl Hart. Urban noise distributions and the influence of geometric spreading on skewness. Engineer Research and Development Center (U.S.), noviembre de 2021. http://dx.doi.org/10.21079/11681/42483.
Texto completoSuir, Glenn, Molly Reif y Christina Saltus. Remote sensing capabilities to support EWN® projects : an R&D approach to improve project efficiencies and quantify performance. Engineer Research and Development Center (U.S.), agosto de 2022. http://dx.doi.org/10.21079/11681/45241.
Texto completoBourgaux, Camille y Anni-Yasmin Turhan. Temporal Query Answering in DL-Lite over Inconsistent Data. Technische Universität Dresden, 2017. http://dx.doi.org/10.25368/2022.236.
Texto completoSavaldi-Goldstein, Sigal y Todd C. Mockler. Precise Mapping of Growth Hormone Effects by Cell-Specific Gene Activation Response. United States Department of Agriculture, diciembre de 2012. http://dx.doi.org/10.32747/2012.7699849.bard.
Texto completoBaader, Franz, Stefan Borgwardt y Marcel Lippmann. On the Complexity of Temporal Query Answering. Technische Universität Dresden, 2013. http://dx.doi.org/10.25368/2022.191.
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