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Статті в журналах з теми "Traces clustering"
Greco, G., A. Guzzo, L. Pontieri, and D. Sacca. "Discovering expressive process models by clustering log traces." IEEE Transactions on Knowledge and Data Engineering 18, no. 8 (August 2006): 1010–27. http://dx.doi.org/10.1109/tkde.2006.123.
Повний текст джерелаWu, Jianhong, Hossein Zivari-Piran, John D. Hunter, and John G. Milton. "Projective Clustering Using Neural Networks with Adaptive Delay and Signal Transmission Loss." Neural Computation 23, no. 6 (June 2011): 1568–604. http://dx.doi.org/10.1162/neco_a_00124.
Повний текст джерелаGomez, Gibran, Platon Kotzias, Matteo Dell’Amico, Leyla Bilge, and Juan Caballero. "Unsupervised Detection and Clustering of Malicious TLS Flows." Security and Communication Networks 2023 (January 12, 2023): 1–17. http://dx.doi.org/10.1155/2023/3676692.
Повний текст джерелаCuzzocrea, Alfredo, Francesco Folino, Massimo Guarascio, and Luigi Pontieri. "Deviance-Aware Discovery of High-Quality Process Models." International Journal on Artificial Intelligence Tools 27, no. 07 (November 2018): 1860009. http://dx.doi.org/10.1142/s0218213018600096.
Повний текст джерелаDobrota, Milan, Boris Delibašić, and Pavlos Delias. "A Skiing Trace Clustering Model for Injury Risk Assessment." International Journal of Decision Support System Technology 8, no. 1 (January 2016): 56–68. http://dx.doi.org/10.4018/ijdsst.2016010104.
Повний текст джерелаDong, Zhenfen, Yuheng Men, Zhengming Li, Zhenzhen Liu, and Jianwei Ji. "Chilling Injury Segmentation of Tomato Leaves Based on Fluorescence Images and Improved k-Means++ Clustering." Transactions of the ASABE 64, no. 1 (2021): 13–22. http://dx.doi.org/10.13031/trans.13212.
Повний текст джерелаChang, Xiangmao, Quan Wang, Zhiguo Qu, and Yanchao Zhao. "The integration of compressive sensing and clustering for date gathering in unmanned aircraft system–aided networks." International Journal of Distributed Sensor Networks 13, no. 8 (August 2017): 155014771772771. http://dx.doi.org/10.1177/1550147717727713.
Повний текст джерелаNAKAZATO, Junji, Jungsuk SONG, Masashi ETO, Daisuke INOUE, and Koji NAKAO. "A Novel Malware Clustering Method Using Frequency of Function Call Traces in Parallel Threads." IEICE Transactions on Information and Systems E94-D, no. 11 (2011): 2150–58. http://dx.doi.org/10.1587/transinf.e94.d.2150.
Повний текст джерелаN, Pushpalatha M., and runalini M. "Clustering The Duplicate Open Crash Reports Based on Call Stack Traces of Crash Reports." International Journal of Computer Sciences and Engineering 6, no. 9 (September 30, 2018): 207–10. http://dx.doi.org/10.26438/ijcse/v6i9.207210.
Повний текст джерелаRojas, Alexis, Gregory P. Dietl, Michał Kowalewski, Roger W. Portell, Austin Hendy, and Jason K. Blackburn. "Spatial point pattern analysis of traces (SPPAT): An approach for visualizing and quantifying site-selectivity patterns of drilling predators." Paleobiology 46, no. 2 (May 2020): 259–71. http://dx.doi.org/10.1017/pab.2020.15.
Повний текст джерелаДисертації з теми "Traces clustering"
Iegorov, Oleg. "Une approche de fouille de données pour le débogage temporel des applications embarquées de streaming." Thesis, Université Grenoble Alpes (ComUE), 2016. http://www.theses.fr/2016GREAM032/document.
Повний текст джерелаDebugging streaming applications run on multimedia embedded systems found in modern consumer electronics (e.g. in set-top boxes, smartphones, etc) is one of the most challenging areas of embedded software development. With each generation of hardware, more powerful and complex Systems-on-Chip (SoC) are released, and developers constantly strive to adapt their applications to these new platforms. Embedded software must not only return correct results but also deliver these results on time in order to respect the Quality-of-Service (QoS) properties of the entire system. The non-respect of QoS properties lead to the appearance of temporal bugs which manifest themselves in multimedia embedded systems as, for example, glitches in the video or cracks in the sound. Temporal debugging proves to be tricky as temporal bugs are not related to the functional correctness of the code, thus making traditional GDB-like debuggers essentially useless. Violations of QoS properties can stem from complex interactions between a particular application and the system or other applications; the complete execution context must be, therefore, taken into account in order to perform temporal debugging. Recent advances in tracing technology allow software developers to capture a trace of the system's execution and to analyze it afterwards to understand which particular system activity is responsible for the violations of QoS properties. However, such traces have a large volume, and understanding them requires data analysis skills that are currently out of the scope of the developers' education.In this thesis, we propose SATM (Streaming Application Trace Miner) - a novel temporal debugging approach for embedded streaming applications. SATM is based on the premise that such applications are designed under the dataflow model of computation, i.e. as a directed graph where data flows between computational units called actors. In such setting, actors must be scheduled in a periodic way in order to meet QoS properties expressed as real-time constraints, e.g. displaying 30 video frames per second. We show that an actor which does not eventually respect its period at runtime causes the violation of the application’s real-time constraints. In practice, SATM is a data analysis workflow combining statistical measures and data mining algorithms. It provides an automatic solution to the problem of temporal debugging of streaming applications. Given an execution trace of a streaming application exhibiting low QoS as well as a list of its actors, SATM firstly determines exact actors’ invocations found in the trace. It then discovers the actors’ periods, as well as parts of the trace in which the periods are not respected. Those parts are further analyzed to extract patterns of system activity that differentiate them from other parts of the trace. Such patterns can give strong hints on the origin of the problem and are returned to the developer. More specifically, we represent those patterns as minimal contrast sequences and investigate various solutions to mine such sequences from execution trace data.Finally, we demonstrate SATM’s ability to detect both an artificial perturbation injected in an open source multimedia framework, as well as temporal bugs from two industrial use cases coming from STMicroelectronics. We also provide an extensive analysis of sequential pattern mining algorithms applied on execution trace data and explain why state-of-the-art algorithms fail to efficiently mine sequential patterns from real-world traces
Teboul, Bruno. "Le développement du neuromarketing aux Etats-Unis et en France. Acteurs-réseaux, traces et controverses." Thesis, Paris Sciences et Lettres (ComUE), 2016. http://www.theses.fr/2016PSLED036/document.
Повний текст джерелаOur research explores the comparative development of neuromarketing between the United States and France. We start by analyzing the literature on neuromarketing. We use as theoretical and methodological framework the Actor Network Theory (ANT) (in the wake of the work of Bruno Latour and Michel Callon). We show how “human and non-human” entities (“actants”): actor-network, traces (publications) and controversies form the pillars of a new discipline such as the neuromarketing. Our hybrid approach “qualitative-quantitative” allows us to build an applied methodology of the ANT: bibliometric analysis (Publish Or Perish), text mining, clustering and semantic analysis of the scientific literature and web of the neuromarketing. From these results, we build data visualizations, mapping of network graphs (Gephi) that reveal the interrelations and associations between actors, traces and controversies about neuromarketing
Hamdi, Marwa. "Modélisation des processus utilisateurs à partir des traces d’exécution, application aux systèmes d’information faiblement structurés." Electronic Thesis or Diss., La Rochelle, 2022. http://www.theses.fr/2022LAROS036.
Повний текст джерелаThis research focuses on extracting users’ journeys in a digital library characterized by weakly structured business processes. In this thesis, we investigate whether it is possible to model user journeys using process mining. The discovered models allow system designers to respond more efficiently to users’ needs and to present them with a set of recommendations. For our study, we have chosen to extract the users’ journey models of the digital library Gallica, based on real traces generated by their users. First, we adapt these browsing traces in a well-defined format compatible with process mining techniques. The originality of our contribution concerns the grouping of similar paths, considering the existing characteristics in the traces, to avoid the generation of complex models, often not exploitable, from such voluminous and unstructured traces. Finally, we validate our method on two simulated and real data sets. We compare our method to two other methods inspired by existing works. The results show that our method outper forms the existing ones on both datasets in clustering and modeling
Mauss, Benoit. "Réactions élastiques et inélastiques résonantes pour la caractérisation expérimentale de la cible active ACTAR TPC." Thesis, Normandie, 2018. http://www.theses.fr/2018NORMC226/document.
Повний текст джерелаACTAR TPC (ACtive TARget and Time Projection Chamber) is a next generation active target that was designed and built at GANIL (Grand Accélérateur d'Ions Lourds). Active targets are gaseous targets in which the gas is also used to track charged particles following the principles of time projection chambers (TPC). The TPC of ACTAR has a segmented anode of 16384 2 mm side square pixels. The high density of pixels is processed using the GET (General Electronics for TPCs) electronic system. This system also digitizes the signals over a time interval, enabling a full 3D event reconstruction. An eight time smaller demonstrator was first built to verify the electronics operation and the mechanical design. ACTAR TPC's final design was based on results obtained with the demonstrator which was tested using 6Li, 24Mg and 58Ni beams. The commissioning of ACTAR TPC was then carried out for the case of resonant scattering on a proton target using 18O and 20Ne beams. A track reconstruction algorithm is used to extract the angles and energies of the ions involved in the reactions. Results are compared to previous data to determine the detection system performances. Comparing the commissioning data with R matrix calculations, excitation functions resolutions in different cases are obtained. The use of ACTAR TPC is validated for future experiments. Furthermore, alpha clustering was studied in 10B through the resonant scattering 6Li + 4He, carried out with the demonstrator. Two resonances at 8.58 MeV and 9.52 MeV are observed for the first time in elastic scattering with this reaction channel
Lallouache, Mehdi. "Clustering in foreign exchange markets : price, trades and traders." Thesis, Châtenay-Malabry, Ecole centrale de Paris, 2015. http://www.theses.fr/2015ECAP0040/document.
Повний текст джерелаThe aim of this thesis is to study three types of clustering in foreign exchange markets, namely in price, trades arrivals and investors decisions. We investigate the statistical properties of the EBS order book for the EUR/USD and USD/JPY currency pairs and the impact of a ten-fold tick size reduction on its dynamics. A large fraction of limit orders are still placed right at or halfway between the old allowed prices. This generates price barriers where the best quotes lie for much of the time, which causes the emergence of distinct peaks in the average shape of the book at round distances. Furthermore, we argue that this clustering is mainly due to manual traders who remained set to the old price resolution. Automatic traders easily take price priority by submitting limit orders one tick ahead of clusters, as shown by the prominence of buy (sell) limit orders posted with rightmost digit one (nine).The clustering of trades arrivals is well-known in financial markets and Hawkes processes are particularly suited to describe this phenomenon. We raise the question of what part of market dynamics Hawkes processes are able to account for exactly. We document the accuracy of such processes as one varies the time interval of calibration and compare the performance of various types of kernels made up of sums of exponentials. Because of their around-the-clock opening times, FX markets are ideally suited to our aim as they allow us to avoid the complications of the long daily overnight closures of equity markets. One can achieve statistical significance according to three simultaneous tests provided that one uses kernels with two exponentials for fitting an hour at a time, and two or three exponentials for full days, while longer periods could not be fitted within statistical satisfaction because of the non-stationarity of the endogenous process. Fitted timescales are relatively short and endogeneity factor is high but sub-critical at about 0.8.Most agent-based models of financial markets implicitly assume that the agents interact through asset prices and exchanged volumes. Some of them add an explicit trader-trader interaction network on which rumors propagate or that encode groups that take common decisions. Contrarily to other types of data, such networks, if they exist, are necessarily implicit, which makes their determination a more challenging task. We analyze transaction data of all the clients of two liquidity providers, encompassing several years of trading. By assuming that the links between agents are determined by systematic simultaneous activity or inactivity, we show that interaction networks do exist. In addition, we find that the (in)activity of some agents systematically triggers the (in)activity of other traders, defining lead-lag relationships between the agents. This implies that the global investment flux is predictable, which we check by using sophisticated machine learning methods
Abonyi, J., FD Tamás, S. Potgieter, and H. Potgieter. "Analysis of Trace Elements in South African Clinkers using Latent Variable Model and Clustering." South African Journal of Chemistry, 2003. http://encore.tut.ac.za/iii/cpro/DigitalItemViewPage.external?sp=1000893.
Повний текст джерелаPetraro, Alessandro. "Clustering di tracce di mobilità per l’identificazione di stili di guida." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/13003/.
Повний текст джерелаAbonyia, J., FD Tamas, and S. Potgieter. "Analysis of trace elements in South African clinkers using latent variable model and clustering." South African Journal of Chemistry, 2003. http://encore.tut.ac.za/iii/cpro/DigitalItemViewPage.external?sp=1001952.
Повний текст джерелаQin, Tian. "Estimation of Water Demands Using an MCMC Algorithm with Clustering Methods." University of Cincinnati / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1544002222852385.
Повний текст джерелаLiang, Xuwei. "MODELING AND QUANTITATIVE ANALYSIS OF WHITE MATTER FIBER TRACTS IN DIFFUSION TENSOR IMAGING." UKnowledge, 2011. http://uknowledge.uky.edu/gradschool_diss/818.
Повний текст джерелаКниги з теми "Traces clustering"
Benestad, Rasmus. Climate in the Barents Region. Oxford University Press, 2018. http://dx.doi.org/10.1093/acrefore/9780190228620.013.655.
Повний текст джерелаЧастини книг з теми "Traces clustering"
Evermann, Joerg, Tom Thaler, and Peter Fettke. "Clustering Traces Using Sequence Alignment." In Business Process Management Workshops, 179–90. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-42887-1_15.
Повний текст джерелаKoschmider, Agnes. "Clustering Event Traces by Behavioral Similarity." In Lecture Notes in Computer Science, 36–42. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70625-2_4.
Повний текст джерелаBrun, Anders, Hans Knutsson, Hae-Jeong Park, Martha E. Shenton, and Carl-Fredrik Westin. "Clustering Fiber Traces Using Normalized Cuts." In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2004, 368–75. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30135-6_45.
Повний текст джерелаRichter, Florian, Ludwig Zellner, Janina Sontheim, and Thomas Seidl. "Model-Aware Clustering of Non-conforming Traces." In Lecture Notes in Computer Science, 193–200. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-33246-4_12.
Повний текст джерелаSun, Yaguang, and Bernhard Bauer. "A Novel Top-Down Approach for Clustering Traces." In Advanced Information Systems Engineering, 331–45. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19069-3_21.
Повний текст джерелаGreco, Gianluigi, Antonella Guzzo, Luigi Pontieri, and Domenico Saccà. "Mining Expressive Process Models by Clustering Workflow Traces." In Advances in Knowledge Discovery and Data Mining, 52–62. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24775-3_8.
Повний текст джерелаMihăescu, Marian Cristian, Alexandru Virgil Tănasie, Mihai Dascalu, and Stefan Trausan-Matu. "Extracting Patterns from Educational Traces via Clustering and Associated Quality Metrics." In Artificial Intelligence: Methodology, Systems, and Applications, 109–18. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-44748-3_11.
Повний текст джерелаDe Weerdt, Jochen. "Trace Clustering." In Encyclopedia of Big Data Technologies, 1–6. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-63962-8_91-1.
Повний текст джерелаWeerdt, Jochen De. "Trace Clustering." In Encyclopedia of Big Data Technologies, 1706–11. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-77525-8_91.
Повний текст джерелаChatain, Thomas, Josep Carmona, and Boudewijn van Dongen. "Alignment-Based Trace Clustering." In Conceptual Modeling, 295–308. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-69904-2_24.
Повний текст джерелаТези доповідей конференцій з теми "Traces clustering"
R. Neubaer, Thais, Marcelo Fantinato, and Sarajane M. Peres. "Interactive trace clustering." In XV Simpósio Brasileiro de Sistemas de Informação. Sociedade Brasileira de Computação (SBC), 2019. http://dx.doi.org/10.5753/sbsi.2019.7438.
Повний текст джерелаSarvani, A., B. Venugopal, and D. Nagaraju. "Clustering the polymorphic malware traces." In 2017 International Conference on Algorithms, Methodology, Models and Applications in Emerging Technologies (ICAMMAET). IEEE, 2017. http://dx.doi.org/10.1109/icammaet.2017.8186641.
Повний текст джерелаLuna, Mateus Alex dos Santos, André Paulino Lima, Thaís Rodrigues Neubauer, Marcelo Fantinato, and Sarajane Marques Peres. "Vector space models for trace clustering: a comparative study." In Encontro Nacional de Inteligência Artificial e Computacional. Sociedade Brasileira de Computação - SBC, 2021. http://dx.doi.org/10.5753/eniac.2021.18274.
Повний текст джерелаBahmani, Amir, and Frank Mueller. "Chameleon: Online Clustering of MPI Program Traces." In 2018 IEEE International Parallel and Distributed Processing Symposium (IPDPS). IEEE, 2018. http://dx.doi.org/10.1109/ipdps.2018.00119.
Повний текст джерелаCunha, Mariana, Ricardo Mendes, and Joao P. Vilela. "Clustering Geo-Indistinguishability for Privacy of Continuous Location Traces." In 2019 4th International Conference on Computing, Communications and Security (ICCCS). IEEE, 2019. http://dx.doi.org/10.1109/cccs.2019.8888111.
Повний текст джерелаWang*, Wanli, Wuyang Yang, Xinjian Wei, and Xin He. "Abnormal traces identification method based on fuzzy clustering analysis." In SEG Technical Program Expanded Abstracts 2015. Society of Exploration Geophysicists, 2015. http://dx.doi.org/10.1190/segam2015-5801583.1.
Повний текст джерелаWang, Jing, Xiaoping Rui, Xianfeng Song, Chaoling Wang, Lingli Tang, Chuanrong Li, and Venkatesh Raghvan. "A weighted clustering algorithm for clarifying vehicle GPS traces." In IGARSS 2011 - 2011 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2011. http://dx.doi.org/10.1109/igarss.2011.6049834.
Повний текст джерелаEl Mandouh, Eman, and Amr G. Wassal. "Accelerating the debugging of FV traces using K-means clustering techniques." In 2016 11th International Design & Test Symposium (IDT). IEEE, 2016. http://dx.doi.org/10.1109/idt.2016.7843055.
Повний текст джерелаTamagnan, Frédéric, Fabrice Bouquet, Alexandre Vernotte, and Bruno Legeard. "Regression Test Generation by Usage Coverage Driven Clustering on User Traces." In 2023 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW). IEEE, 2023. http://dx.doi.org/10.1109/icstw58534.2023.00026.
Повний текст джерелаMeng, Fanzhi, Chunrui Zhang, and Guo Wu. "Protocol reverse based on hierarchical clustering and probability alignment from network traces." In 2018 IEEE 3rd International Conference on Big Data Analysis (ICBDA). IEEE, 2018. http://dx.doi.org/10.1109/icbda.2018.8367724.
Повний текст джерелаЗвіти організацій з теми "Traces clustering"
Wang, Chih-Hao, and Na Chen. Do Multi-Use-Path Accessibility and Clustering Effect Play a Role in Residents' Choice of Walking and Cycling? Mineta Transportation Institute, June 2021. http://dx.doi.org/10.31979/mti.2021.2011.
Повний текст джерелаRusso, Margherita, Fabrizio Alboni, Jorge Carreto Sanginés, Manlio De Domenico, Giuseppe Mangioni, Simone Righi, and Annamaria Simonazzi. The Changing Shape of the World Automobile Industry: A Multilayer Network Analysis of International Trade in Components and Parts. Institute for New Economic Thinking Working Paper Series, January 2022. http://dx.doi.org/10.36687/inetwp173.
Повний текст джерелаPaynter, Robin A., Celia Fiordalisi, Elizabeth Stoeger, Eileen Erinoff, Robin Featherstone, Christiane Voisin, and Gaelen P. Adam. A Prospective Comparison of Evidence Synthesis Search Strategies Developed With and Without Text-Mining Tools. Agency for Healthcare Research and Quality (AHRQ), March 2021. http://dx.doi.org/10.23970/ahrqepcmethodsprospectivecomparison.
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