Добірка наукової літератури з теми "Temporal Event Sequence"
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Статті в журналах з теми "Temporal Event Sequence"
Monroe, Megan, Rongjian Lan, Hanseung Lee, Catherine Plaisant, and Ben Shneiderman. "Temporal Event Sequence Simplification." IEEE Transactions on Visualization and Computer Graphics 19, no. 12 (December 2013): 2227–36. http://dx.doi.org/10.1109/tvcg.2013.200.
Повний текст джерелаSegura, Susana, Pablo Fernandez-Berrocal, and Ruth M. J. Byrne. "Temporal and causal order effects in thinking about what might have been." Quarterly Journal of Experimental Psychology Section A 55, no. 4 (October 2002): 1295–305. http://dx.doi.org/10.1080/02724980244000125.
Повний текст джерелаGupta, Vinayak, Srikanta Bedathur, and Abir De. "Learning Temporal Point Processes for Efficient Retrieval of Continuous Time Event Sequences." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 4 (June 28, 2022): 4005–13. http://dx.doi.org/10.1609/aaai.v36i4.20317.
Повний текст джерелаBellmund, Jacob L. S., Ignacio Polti, and Christian F. Doeller. "Sequence Memory in the Hippocampal–Entorhinal Region." Journal of Cognitive Neuroscience 32, no. 11 (November 2020): 2056–70. http://dx.doi.org/10.1162/jocn_a_01592.
Повний текст джерелаKoseoglu, Baran, Erdem Kaya, Selim Balcisoy, and Burcin Bozkaya. "ST Sequence Miner: visualization and mining of spatio-temporal event sequences." Visual Computer 36, no. 10-12 (July 16, 2020): 2369–81. http://dx.doi.org/10.1007/s00371-020-01894-6.
Повний текст джерелаXu, Fuyu, and Kate Beard. "A Unifying Framework for Analysis of Spatial-Temporal Event Sequence Similarity and Its Applications." ISPRS International Journal of Geo-Information 10, no. 9 (September 9, 2021): 594. http://dx.doi.org/10.3390/ijgi10090594.
Повний текст джерелаLandgraf, Steffen, Susanne Raisig, and Elke van der Meer. "Discerning Temporal Expectancy Effects in Script Processing: Evidence from Pupillary and Eye Movement Recordings." Journal of the International Neuropsychological Society 18, no. 2 (January 23, 2012): 351–60. http://dx.doi.org/10.1017/s1355617711001809.
Повний текст джерелаKEMPE, STEFFEN, JOCHEN HIPP, CARSTEN LANQUILLON, and RUDOLF KRUSE. "MINING FREQUENT TEMPORAL PATTERNS IN INTERVAL SEQUENCES." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 16, no. 05 (October 2008): 645–61. http://dx.doi.org/10.1142/s0218488508005546.
Повний текст джерелаMuthmainah, Hajar Hujjatul, and Oktiva Herry Chandra. "The Use of Temporal Deixis in Portraying Time Displacement and Sequences of Event in Short Stories." Culturalistics: Journal of Cultural, Literary, and Linguistic Studies 5, no. 3 (September 2, 2021): 46–52. http://dx.doi.org/10.14710/culturalistics.v5i3.12750.
Повний текст джерелаBrandenberger, Laurence. "Predicting Network Events to Assess Goodness of Fit of Relational Event Models." Political Analysis 27, no. 4 (April 29, 2019): 556–71. http://dx.doi.org/10.1017/pan.2019.10.
Повний текст джерелаДисертації з теми "Temporal Event Sequence"
Max, Lindblad. "The impact of parsing methods on recurrent neural networks applied to event-based vehicular signal data." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-223966.
Повний текст джерелаDenna avhandling jämför två olika tillvägagångssätt vad gäller parsningen av händelsebaserad signaldata från fordon för att producera indata till en förutsägelsemodell i form av ett neuronnät, nämligen händelseparsning, där datan förblir ojämnt fördelad över tidsdomänen, och skivparsning, där datan är omgjord till att istället vara jämnt fördelad över tidsdomänen. Det dataset som används för dessa experiment är ett antal signalloggar från fordon som kommer från Scania. Jämförelser mellan parsningsmetoderna gjordes genom att först träna ett lång korttidsminne (LSTM) återkommande neuronnät (RNN) på vardera av de skapade dataseten för att sedan mäta utmatningsfelet och resurskostnader för varje modell efter att de validerats på en delad uppsättning av valideringsdata. Resultaten från dessa tester visar tydligt på att skivparsning står sig väl mot händelseparsning.
Sun, Xingzhi. "Knowledge discovery in long temporal event sequences /." [St. Lucia, Qld.], 2005. http://www.library.uq.edu.au/pdfserve.php?image=thesisabs/absthe18601.pdf.
Повний текст джерелаČížek, Tomáš. "Rozpoznávání událostí ve fotbalu z prostoročasových dat objektů ve hře." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2018. http://www.nusl.cz/ntk/nusl-385989.
Повний текст джерелаHumphreys, Gruffydd R. "Temporal binding in causal and non-causal event sequences." Thesis, Cardiff University, 2010. http://orca.cf.ac.uk/55868/.
Повний текст джерелаKrstajić, Miloš [Verfasser]. "Visual Analytics of Temporal Event Sequences in News Streams / Miloš Krstajić." Konstanz : Bibliothek der Universität Konstanz, 2014. http://d-nb.info/1079719725/34.
Повний текст джерелаSammouri, Wissam. "Data mining of temporal sequences for the prediction of infrequent failure events : application on floating train data for predictive maintenance." Thesis, Paris Est, 2014. http://www.theses.fr/2014PEST1041/document.
Повний текст джерелаIn order to meet the mounting social and economic demands, railway operators and manufacturers are striving for a longer availability and a better reliability of railway transportation systems. Commercial trains are being equipped with state-of-the-art onboard intelligent sensors monitoring various subsystems all over the train. These sensors provide real-time flow of data, called floating train data, consisting of georeferenced events, along with their spatial and temporal coordinates. Once ordered with respect to time, these events can be considered as long temporal sequences which can be mined for possible relationships. This has created a neccessity for sequential data mining techniques in order to derive meaningful associations rules or classification models from these data. Once discovered, these rules and models can then be used to perform an on-line analysis of the incoming event stream in order to predict the occurrence of target events, i.e, severe failures that require immediate corrective maintenance actions. The work in this thesis tackles the above mentioned data mining task. We aim to investigate and develop various methodologies to discover association rules and classification models which can help predict rare tilt and traction failures in sequences using past events that are less critical. The investigated techniques constitute two major axes: Association analysis, which is temporal and Classification techniques, which is not temporal. The main challenges confronting the data mining task and increasing its complexity are mainly the rarity of the target events to be predicted in addition to the heavy redundancy of some events and the frequent occurrence of data bursts. The results obtained on real datasets collected from a fleet of trains allows to highlight the effectiveness of the approaches and methodologies used
Vaudry, Pierre-Luc. "Narrative generation by associative network extraction from real-life temporal data." Thèse, 2016. http://hdl.handle.net/1866/18473.
Повний текст джерелаData about events abounds in our technological society. An attractive way of presenting real-life temporal data to facilitate its interpretation is an automatically generated narrative. Narrative comprehension involves the construction of a causal network by the reader. Narrative data-to-text systems seem to acknowledge causal relations as important. However, they play a secondary role in their document planners and their identification relies mostly on domain knowledge. This thesis proposes an assisted temporal data interpretation model by narrative generation in which narratives are structured with the help of a mix of automatically mined and manually defined association rules. The associations suggest causal hypotheses to the reader who can thus construct more easily a causal representation of the events. This model should be applicable to any repetitive temporal data, preferably including actions or activities, such as Activity of Daily Living (ADL) data. Sequential association rules are selected based on the criteria of confidence and statistical significance as measured in training data. World and domain knowledge association rules are based on the similarity of some aspect of a pair of events or on causal patterns difficult to detect statistically. To interpret a specific period to summarize, pairs of events for which an association rule applies are associated. Some extra associations are then derived. Together the events and associations form an associative network. The most important step of the Natural Language Generation (NLG) pipeline is document planning, comprising event selection and document structuring. For event selection, the model relies on the confidence of sequential associations to select the most unusual facts. The assumption is that an event that is implied by another one with a relatively high probability may be left implicit in the text. The structure of the narrative is called the connecting associative thread because it allows the reader to follow associations from the beginning to the end of the text. It takes the form of a spanning tree over the previously selected associative sub-network. The associations it contains are selected based on association type preferences and relative temporal distance. The connecting associative thread is then segmented into paragraphs, sentences, and phrases and the associations are translated to rhetorical relations. The microplanning step defines lexico-syntactic templates describing each event type. When two event descriptions need to be assembled in the same sentence, a discourse marker expressing the specified rhetorical relation is employed. A main event and a preceding main event are determined for each sentence. When the associative thread parent of the main event is not the preceding main event, an anaphor is added to the sentence front discourse marker. Surface realization can be performed in English or French thanks to bilingual lexico-syntactic specifications and the SimpleNLG-EnFr Java library. The results of a textual quality evaluation show that the texts are understandable and the lexical choices adequate.
Chiang, Yu-Sheng, and 姜佑昇. "Event Episode Discovery from Document Sequences: A Temporal-based Approach." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/93162489449498603591.
Повний текст джерела國立中山大學
資訊管理學系研究所
93
Recent advances in information and networking technologies have contributed significantly to global connectivity and greatly facilitated and fostered information creation, distribution, and access. The resultant ever-increasing volume of online textual documents creates an urgent need for new text mining techniques that can intelligently and automatically extract implicit and potentially useful knowledge from these documents for decision support. This research focuses on identifying and discovering event episodes together with their temporal relationships that occur frequently (referred to as evolution patterns in this study) in sequences of documents. The discovery of such evolution patterns can be applied in such domains as knowledge management and used to facilitate existing document management and retrieval techniques (e.g., event tracking). Specifically, we propose and design an evolution pattern (EP) discovery technique for mining evolution patterns from sequences of documents. We experimentally evaluate our proposed EP technique in the context of facilitating event tracking. Measured by miss and false alarm rates, the evolution-pattern supported event-tracking (EPET) technique exhibits better tracking effectiveness than a traditional event-tracking technique. The encouraging performance of the EPET technique demonstrates the potential usefulness of evolution patterns in supporting event tracking and suggests that the proposed EP technique could effectively discover event episodes and evolution patterns in sequences of documents.
Chang, Ming Jue, and 張明珠. "The effect of temporal distance, amount, sequence and risky events on consumer choice." Thesis, 1996. http://ndltd.ncl.edu.tw/handle/66385795163654062069.
Повний текст джерелаLaxman, Srivatsan. "Discovering Frequent Episodes : Fast Algorithms, Connections With HMMs And Generalizations." Thesis, 2006. http://hdl.handle.net/2005/375.
Повний текст джерелаКниги з теми "Temporal Event Sequence"
Ikenberry, G. John. The Rise, Character, and Evolution of International Order. Edited by Orfeo Fioretos, Tulia G. Falleti, and Adam Sheingate. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780199662814.013.32.
Повний текст джерелаЧастини книг з теми "Temporal Event Sequence"
Zhang, Wei. "Some Improvements on Event-Sequence Temporal Region Methods." In Machine Learning: ECML 2000, 446–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-45164-1_45.
Повний текст джерелаPlaisant, Catherine. "Life on the Line: Interacting with Temporal Event Sequence Representations." In Diagrammatic Representation and Inference, 1–2. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31223-6_1.
Повний текст джерелаChen, Jie, Hongxing He, Graham Williams, and Huidong Jin. "Temporal Sequence Associations for Rare Events." In Advances in Knowledge Discovery and Data Mining, 235–39. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24775-3_30.
Повний текст джерелаMirbagheri, S. Mohammad, and Howard J. Hamilton. "Similarity Matching of Temporal Event-Interval Sequences." In Advances in Artificial Intelligence, 420–25. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47358-7_43.
Повний текст джерелаSun, Xingzhi, Maria E. Orlowska, and Xiaofang Zhou. "Finding Event-Oriented Patterns in Long Temporal Sequences." In Advances in Knowledge Discovery and Data Mining, 15–26. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-36175-8_3.
Повний текст джерелаHan, Chao, Lei Duan, Zhangxi Lin, Ruiqi Qin, Peng Zhang, and Jyrki Nummenmaa. "Discovering Relationship Patterns Among Associated Temporal Event Sequences." In Database Systems for Advanced Applications, 107–23. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-18576-3_7.
Повний текст джерелаPlantevit, Marc, Vasile-Marian Scuturici, and Céline Robardet. "Temporal Dependency Detection Between Interval-Based Event Sequences." In New Frontiers in Mining Complex Patterns, 132–46. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-17876-9_9.
Повний текст джерелаLiang, Han, and Jörg Sander. "Detecting Statistically Significant Temporal Associations from Multiple Event Sequences." In Advances in Artificial Intelligence, 112–25. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38457-8_10.
Повний текст джерелаSun, Xingzhi, Maria E. Orlowska, and Xue Li. "Finding Negative Event-Oriented Patterns in Long Temporal Sequences." In Advances in Knowledge Discovery and Data Mining, 212–21. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24775-3_28.
Повний текст джерелаHong, Shenda, Meng Wu, Hongyan Li, and Zhengwu Wu. "Event2vec: Learning Representations of Events on Temporal Sequences." In Web and Big Data, 33–47. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63564-4_3.
Повний текст джерелаТези доповідей конференцій з теми "Temporal Event Sequence"
Gay, Dominique, Romain Guigoures, Marc Boulle, and Fabrice Clerot. "TESS: Temporal event sequence summarization." In 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA). IEEE, 2015. http://dx.doi.org/10.1109/dsaa.2015.7344904.
Повний текст джерелаCheng, Yu, Quanfu Fan, Sharath Pankanti, and Alok Choudhary. "Temporal Sequence Modeling for Video Event Detection." In 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2014. http://dx.doi.org/10.1109/cvpr.2014.286.
Повний текст джерелаLi, ZeXian, Tian Wang, Yi Yang, Yan Wang, Peng Shi, and Hichem Snoussi. "Event prediction via spatio-temporal sequence analysis." In 2019 Chinese Automation Congress (CAC). IEEE, 2019. http://dx.doi.org/10.1109/cac48633.2019.8996903.
Повний текст джерелаDu, Fan, Catherine Plaisant, Neil Spring, and Ben Shneiderman. "EventAction: Visual analytics for temporal event sequence recommendation." In 2016 IEEE Conference on Visual Analytics Science and Technology (VAST). IEEE, 2016. http://dx.doi.org/10.1109/vast.2016.7883512.
Повний текст джерелаShimohara, Uchiyama, and Tokunaga. "Back-propagation networks for event-driven temporal sequence processing." In Proceedings of 1993 IEEE International Conference on Neural Networks (ICNN '93). IEEE, 1988. http://dx.doi.org/10.1109/icnn.1988.23904.
Повний текст джерелаRen, Jiadong, and Haiyan Tian. "Sequential Pattern Mining with Inaccurate Event in Temporal Sequence." In 2008 Fourth International Conference on Networked Computing and Advanced Information Management (NCM). IEEE, 2008. http://dx.doi.org/10.1109/ncm.2008.93.
Повний текст джерелаZhang, Yunhao, Junchi Yan, Xiaolu Zhang, Jun Zhou, and Xiaokang Yang. "Learning Mixture of Neural Temporal Point Processes for Multi-dimensional Event Sequence Clustering." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/523.
Повний текст джерелаCao, Chengzhi, Xueyang Fu, Yurui Zhu, Gege Shi, and Zheng-Jun Zha. "Event-driven Video Deblurring via Spatio-Temporal Relation-Aware Network." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/112.
Повний текст джерелаCeolini, Enea, Daniel Neil, Tobi Delbruck, and Shih-Chii Liu. "Temporal sequence recognition in a self-organizing recurrent network." In 2016 Second International Conference on Event-based Control, Communication, and Signal Processing (EBCCSP). IEEE, 2016. http://dx.doi.org/10.1109/ebccsp.2016.7605258.
Повний текст джерелаBertoglio, Nicola, Gianfranco Lamperti, Marina Zanella, and Xiangfu Zhao. "Explanatory Diagnosis of Discrete-Event Systems with Temporal Information and Smart Knowledge-Compilation." In 17th International Conference on Principles of Knowledge Representation and Reasoning {KR-2020}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/kr.2020/14.
Повний текст джерелаЗвіти організацій з теми "Temporal Event Sequence"
Epel, Bernard L., Roger N. Beachy, A. Katz, G. Kotlinzky, M. Erlanger, A. Yahalom, M. Erlanger, and J. Szecsi. Isolation and Characterization of Plasmodesmata Components by Association with Tobacco Mosaic Virus Movement Proteins Fused with the Green Fluorescent Protein from Aequorea victoria. United States Department of Agriculture, September 1999. http://dx.doi.org/10.32747/1999.7573996.bard.
Повний текст джерела