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Thèses sur le sujet « Discovery from data »

Auteur : Grafiati

Publié le 10 mars 2023

Mis à jour le 31 juillet 2025

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1

Höppner, Frank. "Knowledge discovery from sequential data." [S.l. : s.n.], 2003. http://deposit.ddb.de/cgi-bin/dokserv?idn=96728421X.

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Cao, Huiping. "Pattern discovery from spatiotemporal data." Click to view the E-thesis via HKUTO, 2006. http://sunzi.lib.hku.hk/hkuto/record/B37381520.

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Cao, Huiping, and 曹會萍. "Pattern discovery from spatiotemporal data." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2006. http://hub.hku.hk/bib/B37381520.

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Chau, Tom. "Event level pattern discovery in multivariate continuous data." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk2/tape15/PQDD_0003/NQ30594.pdf.

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El, Sayed Ahmed. "Contributions in knowledge discovery from textual data." Lyon 2, 2008. http://theses.univ-lyon2.fr/documents/lyon2/2008/el-sayed_a.

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This dissertation focuses on two key issues in text mining, namely unsupervised learning and knowledge acquisition. In spite of their relative maturity, both issues still present some major challenges that need to be addressed. First, for unsupervised learning, a well-known, unresolved challenge is to perform clustering with minimal input parameters. One natural way to reach this is to involve validity indices in the clustering process. Although of great interest, validity indices were not extensively explored in the literature, especially when dealing with high-dimensional data like text. Hen

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El, Sayed Ahmed Zighed Djamel Abdelkader. "Contributions in knowledge discovery from textual data." Lyon : Université Lumière Lyon 2, 2008. http://theses.univ-lyon2.fr/sdx/theses/lyon2/2008/el-sayed_a.

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Wang, Yang. "High-order pattern discovery and analysis of discrete-valued data sets." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/nq22245.pdf.

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Amado, Vanessa. "Knowledge discovery and data mining from freeway section traffic data." Diss., Columbia, Mo. : University of Missouri-Columbia, 2008. http://hdl.handle.net/10355/5591.

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Thesis (Ph. D.)--University of Missouri-Columbia, 2008.<br>The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on June 8, 2009) Vita. Includes bibliographical references.

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Caruccio, Loredana. "Relaxed functional dependencies: definition, discovery and applications." Doctoral thesis, Universita degli studi di Salerno, 2018. http://hdl.handle.net/10556/3051.

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2016 - 2017<br>Functional dependencies (FDs) were conceived in the early '70s, and were mainly used to verify database design and assess data quality. However, to solve several issues in emerging application domains, such as the identification of data inconsistencies, patterns of semantically related data, query rewriting, and so forth, it has been necessary to extend the FD definition... [edited by author]<br>XVI n.s.

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PaÌirceÌir, RoÌnaÌn. "Knowledge discovery from distributed aggregate data in data warehouses and statistical databases." Thesis, University of Ulster, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.274398.

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Sun, Feng-Tso. "Nonparametric Discovery of Human Behavior Patterns from Multimodal Data." Research Showcase @ CMU, 2014. http://repository.cmu.edu/dissertations/359.

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Recent advances in sensor technologies and the growing interest in context- aware applications, such as targeted advertising and location-based services, have led to a demand for understanding human behavior patterns from sensor data. People engage in routine behaviors. Automatic routine discovery goes beyond low-level activity recognition such as sitting or standing and analyzes human behaviors at a higher level (e.g., commuting to work). The goal of the research presented in this thesis is to automatically discover high-level semantic human routines from low-level sensor streams. One recent

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Minnen, David. "Unsupervised discovery of activity primitives from multivariate sensor data." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/24623.

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Thesis (Ph.D.)--Computing, Georgia Institute of Technology, 2009.<br>Committee Chair: Thad Starner; Committee Member: Aaron Bobick; Committee Member: Bernt Schiele; Committee Member: Charles Isbell; Committee Member: Irfan Essa

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Liang, Huishi. "Knowledge Discovery from Smart Meter Data and Its Applications." Thesis, The University of Sydney, 2021. https://hdl.handle.net/2123/25982.

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Smart meters have experienced a fast development around the globe during the past decade. Millions of smart meters deployed in many utilities have collected a massive amount of fine-grained electricity consumption data, offering opportunities for revealing insights into the energy consumption characteristics of individual customers. Yet to date how to dig useful information from this wealth of data is still far from being fully investigated. This thesis aims to develop methodologies to mine useful information from smart meter data and apply the discovered knowledge to improve the energy effici

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Babbar, Sakshi. "Inferring Anomalies from Data using Bayesian Networks." Thesis, The University of Sydney, 2013. http://hdl.handle.net/2123/9371.

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Existing studies on data mining has largely focused on the design of measures and algorithms to identify outliers in large and high dimensional categorical and numeric databases. However, not much stress has been given on the interestingness of the reported outlier. One way to ascertain interestingness and usefulness of the reported outlier is by making use of domain knowledge. In this thesis, we present measures to discover outliers based on background knowledge, represented by a Bayesian network. Using causal relationships between attributes encoded in the Bayesian framework, we demonstrate

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Durbha, Surya Srinivas. "Semantics-enabled framework for knowledge discovery from Earth observation data." Diss., Mississippi State : Mississippi State University, 2006. http://sun.library.msstate.edu/ETD-db/ETD-browse/browse.

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Nagao, Katashi, Katsuhiko Kaji, and Toshiyuki Shimizu. "Discussion Mining : Knowledge Discovery from Data on the Real World Activities." INTELLIGENT MEDIA INTEGRATION NAGOYA UNIVERSITY / COE, 2004. http://hdl.handle.net/2237/10350.

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Salviato, Elisa. "Computational methods for the discovery of molecular signatures from Omics Data." Doctoral thesis, Università degli studi di Padova, 2018. http://hdl.handle.net/11577/3421961.

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Molecular biomarkers, derived from high-throughput technologies, are the foundations of the "next-generation" precision medicine. Despite a decade of intense efforts and investments, the number of clinically valid biomarkers is modest. Indeed, the "big-data" nature of omics data provides new challenges that require an improvement in the strategies of data analysis and interpretation. In this thesis, two themes are proposed, both aimed at improving the statistical and computational methodology in the field of signatures discovery. The first work aim at identifying serum miRNAs to be used as di

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Cece, Esra Nurten 1984. "Metabolite identification in drug discovery : from data to information and from information to knowledge." Doctoral thesis, Universitat Pompeu Fabra, 2017. http://hdl.handle.net/10803/403648.

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Drug metabolism studies provide the opportunity to enhance metabolic properties of new drugs. The overall aims of the drug metabolism studies are to (1.) optimize pharmacokinetic properties of the drug candidates, (2.) characterize the polymorphic enzyme contribution to clearance, and (3.) support the selection of safe drugs with respect to bioactivation potentials. The ultimate goal in drug metabolism assays in early drug discovery is to translate analytical data to build final knowledge. By contribution of this translation, metabolism scientists can rationalize how structures of new drug

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Venkatasubramanian, Meenakshi. "De novo Population Discovery from Complex Biological Datasets." University of Cincinnati / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1563873297599047.

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Zhou, Mu. "Knowledge Discovery and Predictive Modeling from Brain Tumor MRIs." Scholar Commons, 2015. http://scholarcommons.usf.edu/etd/5809.

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Quantitative cancer imaging is an emerging field that develops computational techniques to acquire a deep understanding of cancer characteristics for cancer diagnosis and clinical decision making. The recent emergence of growing clinical imaging data provides a wealth of opportunity to systematically explore quantitative information to advance cancer diagnosis. Crucial questions arise as to how we can develop specific computational models that are capable of mining meaningful knowledge from a vast quantity of imaging data and how to transform such findings into improved personalized health car

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Le, Van Quoc Anh [Verfasser], and Michael [Akademischer Betreuer] Gertz. "Pattern Discovery from Event Data / Anh Le Van Quoc ; Betreuer: Michael Gertz." Heidelberg : Universitätsbibliothek Heidelberg, 2014. http://d-nb.info/1180032594/34.

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Kavasidis, Isaak. "Multifaceted analysis for medical data understanding: from data acquisition to multidimensional signal processing to knowledge discovery." Doctoral thesis, Università di Catania, 2016. http://hdl.handle.net/10761/3925.

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Large quantities of medical data are routinely generated each day in the form of text, images and time signals, making evident the need to develop new methodologies not only for the automatization of the processing and management of such data, but also for the deeper un- derstanding of the concepts hidden therein. The main problem that arises is that the acquired data cannot always be in an appropriate state or quality for quantitative analysis, and further processing is often necessary in order to enable automatic processing and manage- ment as well as to increase the accuracy of the results.

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Radovanovic, Aleksandar. "Concept Based Knowledge Discovery from Biomedical Literature." Thesis, Online access, 2009. http://etd.uwc.ac.za/usrfiles/modules/etd/docs/etd_gen8Srv25Nme4_9861_1272229462.pdf.

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Elsilä, U. (Ulla). "Knowledge discovery method for deriving conditional probabilities from large datasets." Doctoral thesis, University of Oulu, 2007. http://urn.fi/urn:isbn:9789514286698.

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Abstract In today's world, enormous amounts of data are being collected everyday. Thus, the problems of storing, handling, and utilizing the data are faced constantly. As the human mind itself can no longer interpret the vast datasets, methods for extracting useful and novel information from the data are needed and developed. These methods are collectively called knowledge discovery methods. In this thesis, a novel combination of feature selection and data modeling methods is presented in order to help with this task. This combination includes the methods of basic statistical analysis, linear

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Piekenbrock, Matthew J. "Discovering Intrinsic Points of Interest from Spatial Trajectory Data Sources." Wright State University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=wright1527160689990512.

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26

Hahn, Jasper. "From Discovery to Purchase: Improving the User Experience for Buyers in eCommerce." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-177473.

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The Internet has revolutionized many areas of our lives. New forms of exchanging and retrieving information, making business and communication in general have been made possible with the Internet and have gone through a rapid development since its creation. In an age of nearly ubiquitous access to the Internet and a majority of the western world actively using social media, retail markets have changed, too. But compared to the rapidly changing services in other sectors, retail businesses have only converted an existing model to a new technology rather than coming up with a new one. Social Comm

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Deirmenci, Hazim. "Enabling Content Discovery in an IPTV System : Using Data from Online Social Networks." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-200922.

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Internet Protocol television (IPTV) is a way of delivering television over the Internet, which enables two-way communication between an operator and its users. By using IPTV, users have freedom to choose what content they want to consume and when they want to consume it. For example, users are able to watch TV shows after they have been aired on TV, and they can access content that is not part of any linear TV broadcasts, e.g. movies that are available to rent. This means that, by using IPTV, users can get access to more video content than is possible with the traditional TV distribution forma

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Bakhtyar, Shoaib. "A Knowledge Graph Approach Towards Hidden Patterns Discovery From Biomedical Publications." Thesis, Luleå tekniska universitet, Institutionen för system- och rymdteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-87267.

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Biomedical research publications oen include significant scientific advancements in the biomedical domain. These publications can be interlinked in dierent aspects, such as having common keywords, covering a similar theme/topic, published by the same author(s), funded by a common funding agency, or belonging to a commonproject. Further, the visibility of links between dierent publications can be significantly useful to researchers, e.g., the keywords that interlink dierent authors can be useful to researchers for finding relevant biomedical researchers with common interests. However, it is dii

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Parvinzamir, Farzad. "A visual analytics approach for visualisation and knowledge discovery from time-varying personal life data." Thesis, University of Bedfordshire, 2018. http://hdl.handle.net/10547/622697.

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Today, the importance of big data from lifestyles and work activities has been the focus of much research. At the same time, advances in modern sensor technologies have enabled self-logging of a signi cant number of daily activities and movements. Lifestyle logging produces a wide variety of personal data along the lifespan of individuals, including locations, movements, travel distance, step counts and the like, and can be useful in many areas such as healthcare, personal life management, memory recall, and socialisation. However, the amount of obtainable personal life logging data has enormo

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Smith, Tynan S. "Unsupervised discovery of human behavior and dialogue patterns in data from an online game." Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/76999.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (p. 121-126).<br>A content authoring bottleneck in AI, coupled with improving technology, has lead to increasing efforts in using large datasets to power Al systems directly. This idea is being used to create Al agents in video games, using logs of human-played games as the dataset. This new approach to AI brings its own challenges, particularly the need to annotate the datasets used. This thesis exp

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Ciani, Yari. "Regulatory modules discovery and mesenchymal stem cells characterization from high-throughput cancer genomics data." Doctoral thesis, Università degli studi di Trieste, 2015. http://hdl.handle.net/10077/11111.

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2013/2014<br>Il tumore è una malattia caratterizzata da un’estrema complessità molecolare. Gli approcci di tipo “omic”, collezionando dati sull’intero genoma, sui trascritti e proteine in dataset pubblici, permettono di superare questa complessità e di trovare moduli funzionali che eseguono le funzioni coinvolte nei processi tumorali. Ad esempio, i profili di espressione genica da tessuti vengono usati per definire firme di geni e testarne la rilevanza clinica. Ho usato questo tipo di informazione per caratterizzare specifici geni di interesse in modelli di tumore al seno. Uno dei più recenti

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Iorio, Francesco. "Automatic discovery of drug mode of action and drug repositioning from gene expression data." Doctoral thesis, Universita degli studi di Salerno, 2011. http://hdl.handle.net/10556/976.

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2009 - 2010<br>The identification of the molecular pathway that is targeted by a compound, combined with the dissection of the following reactions in the cellular environment, i.e. the drug mode of action, is a key challenge in biomedicine. Elucidation of drug mode of action has been attempted, in the past, with different approaches. Methods based only on transcriptional responses are those requiring the least amount of information and can be quickly applied to new compounds. On the other hand, they have met with limited success and, at the present, a general, robust and efficient gene-

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Queyrel, Maxence. "End-to-End Deep Learning and Subgroup discovery approaches to learn from metagenomics data." Electronic Thesis or Diss., Sorbonne université, 2021. http://www.theses.fr/2021SORUS470.

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Les progrès technologiques ont rendu possible le séquençage haute résolution du matériel génétique à faible coût. Dans ce contexte, le microbiome humain a démontré sa grande capacité à stratifier diverses maladies humaines. En tant que "super-intégrateur" de l'état du patient, le microbiote intestinal est appelé à jouer un rôle clé dans la médecine de précision. L'identification de biomarqueurs omiques est devenue un objectif majeur en métagénomique, et permet de comprendre les diversités microbiennes qui induisent la stratification des patients. Il reste de nombreux défis associés aux pipelin

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Wickramarathne, Thanuka Lakmal. "A Belief Theoretic Approach for Automated Collaborative Filtering." Scholarly Repository, 2008. http://scholarlyrepository.miami.edu/oa_theses/182.

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WICKRAMARATHNE, T. L. (M.S., Electrical and Computer Engineering) A Belief Theoretic Approach for Automated Collaborative Filtering (May 2008) Abstract of a thesis at the University of Miami. Thesis supervised by Professor Kamal Premaratne. No. of pages in text. (84) Automated Collaborative Filtering (ACF) is one of the most successful strategies available for recommender systems. Application of ACF in more sensitive and critical applications however has been hampered by the absence of better mechanisms to accommodate imperfections (ambiguities and uncertainties in ratings, missing ratings

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De, Wilde Max. "From Information Extraction to Knowledge Discovery: Semantic Enrichment of Multilingual Content with Linked Open Data." Doctoral thesis, Universite Libre de Bruxelles, 2015. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/218774.

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Discovering relevant knowledge out of unstructured text in not a trivial task. Search engines relying on full-text indexing of content reach their limits when confronted to poor quality, ambiguity, or multiple languages. Some of these shortcomings can be addressed by information extraction and related natural language processing techniques, but it still falls short of adequate knowledge representation. In this thesis, we defend a generic approach striving to be as language-independent, domain-independent, and content-independent as possible. To reach this goal, we offer to disambiguate terms w

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Roquencourt, Camille. "Signal processing and analysis of PTR-TOF-MS data from exhaled breath for biomarker discovery." Electronic Thesis or Diss., université Paris-Saclay, 2022. http://www.theses.fr/2022UPASG024.

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L'analyse des composés organiques volatils (COVs) dans l'air expiré est une méthode non invasive prometteuse en médecine pour le diagnostic précoce, le phénotypage, le suivi de la maladie et du traitement et le dépistage à grande échelle. La spectrométrie de masse à temps de vol par réaction de transfert de protons (PTR-TOF-MS) présente un intérêt majeur pour l'analyse en temps réel des COVs et la découverte de nouveaux biomarqueurs. Le manque de méthodes et d'outils logiciels pour le traitement des données PTR-TOF-MS provenant de cohortes représente actuellement un verrou pour le développemen

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Tomczak, Jakub. "Algorithms for knowledge discovery using relation identification methods." Thesis, Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2563.

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In this work a coherent survey of problems connected with relational knowledge representation and methods for achieving relational knowledge representation were presented. Proposed approach was shown on three applications: economic case, biomedical case and benchmark dataset. All crucial definitions were formulated and three main methods for relation identification problem were shown. Moreover, for specific relational models and observations’ types different identification methods were presented.<br>Double Diploma Programme, polish supervisor: prof. Jerzy Świątek, Wrocław University of Technol

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Trávníček, Petr. "Aplikace data miningu v podnikové praxi." Master's thesis, Vysoká škola ekonomická v Praze, 2011. http://www.nusl.cz/ntk/nusl-164048.

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Throughout last decades, knowledge discovery from databases as one of the information and communicaiton technologies' disciplines has developed into its current state being showed increasing interest not only by major business corporates. Presented diploma thesis deals with problematique of data mining while paying prime attention to its practical utilization within business environment. Thesis objective is to review possibilities of data mining applications and to decompose implementation techniques focusing on specific data mining methods and algorithms as well as adaptation of business proc

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Tuovinen, L. (Lauri). "From machine learning to learning with machines:remodeling the knowledge discovery process." Doctoral thesis, Oulun yliopisto, 2014. http://urn.fi/urn:isbn:9789526205243.

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Abstract Knowledge discovery (KD) technology is used to extract knowledge from large quantities of digital data in an automated fashion. The established process model represents the KD process in a linear and technology-centered manner, as a sequence of transformations that refine raw data into more and more abstract and distilled representations. Any actual KD process, however, has aspects that are not adequately covered by this model. In particular, some of the most important actors in the process are not technological but human, and the operations associated with these actors are interactiv

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Maini, Vincenzo. "Price and liquidity discovery, jumps and co-jumps using high frequency data from the foreign exchange markets." Thesis, City University London, 2012. http://openaccess.city.ac.uk/2382/.

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The thesis provides a novel contribution to the literature of microstructural theory and discovery models. The main contributions are twofolds. First, we move from price to liquidity discovery and explicitly study the dynamic behavior of a direct measure of liquidity observed from the foreign exchange markets. We extend the framework presented by Hasbrouck (1991) and Dufour and Engle (2000) by allowing the coefficients of both liquidity and trade activity to be time dependent. We find that liquidity time is characterized by a strong stochastic component and that liquidity shocks tend to have t

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Sundaramurthy, Gopinath. "A Probabilistic Approach for Automated Discovery of Biomarkers using Expression Data from Microarray or RNA-Seq Datasets." University of Cincinnati / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1459528594.

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Prichard, Paul Michael. "An investigation into the discovery potential for SUperSYmmetry at the LHC with early data from the ATLAS detector." Thesis, University of Liverpool, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.533931.

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Romero, Bustamante Elkin Giovanni. "Introducing the Water Data Explorer Web Application and Python Library: Uniform Means for Data Discovery and Access from CUAHSI and the WMO WHOS Systems." BYU ScholarsArchive, 2021. https://scholarsarchive.byu.edu/etd/8915.

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There has been a growing recognition in recent years of the need for a standardized means for sharing water data on the web. One response to this need was the development of the Consortium of Universities for the Advancement of Hydrologic Science (CUAHSI) Hydrologic Information System (HIS) and its accompanying WaterOneFlow and WaterML protocols. To date, the primary means for accessing data shared using these protocols has been limited to the Microsoft Windows HydroDesktop software, the WaterML R package, and the web based CUAHSI HydroClient which serves as an access point to the CUAHSI HIS d

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Pettersson, Max, and Viktor Jansson. "Predicting rifle shooting accuracy from context and sensor data : A study of how to perform data mining and knowledge discovery in the target shooting domain." Thesis, Tekniska Högskolan, Högskolan i Jönköping, JTH, Datateknik och informatik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-45396.

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The purpose of this thesis is to develop an interpretable model that gives predictions for what factors impacted a shooter’s results. Experiment is our chosen research method. Our three independent variables are weapon movement, trigger pull force and heart rate. Our dependent variable is shooting accuracy. A random forest regression model is trained with the experiment data to produce predictions of shooting accuracy and to show correlation between independent and dependent variables. Our method shows that an increase in weapon movement, trigger pull force and heart rate decrease the predicte

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Severini, Nicola. "Analysis, Development and Experimentation of a Cognitive Discovery Pipeline for the Generation of Insights from Informal Knowledge." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/21013/.

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The purpose of this thesis project is to bring the application of Cognitive Discovery to an informal type of knowledge. Cognitive Discovery is a term coined by IBM Research to indicate a series of Information Extraction (IE) processes in order to build a knowledge graph capable of representing knowledge from highly unstructured data such as text. Cognitive Discovery is typically applied to a type of formal knowledge, i.e. of the documented text such as academic papers, business reports, patents, etc. While informal knowledge is provided, for example, by recording a conversation within a meeti

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Sengstock, Christian [Verfasser], and Michael [Akademischer Betreuer] Gertz. "Geographic Feature Mining: Framework and Fundamental Tasks for Geographic Knowledge Discovery from User-generated Data / Christian Sengstock ; Betreuer: Michael Gertz." Heidelberg : Universitätsbibliothek Heidelberg, 2015. http://d-nb.info/1180395662/34.

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Nettling, Arthur Martin [Verfasser]. "New approaches for de-novo motif discovery using phylogenetic footprinting : from data acquisition to motif visualization ; [kumulative Dissertation] / Arthur Martin Nettling." Halle, 2017. http://d-nb.info/113307412X/34.

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ABATE, NICODEMO. "Towards an operational use of remote sensing data (satellite, drone, and ground) for Cultural Heritage: from discovery to documentation, monitoring, and valorisation." Doctoral thesis, Università degli studi della Basilicata, 2022. http://hdl.handle.net/11563/158570.

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The research work was developed starting from the idea of an operational and practical use of remote sensing data from satellite, drone, and ground, as suggested by the title. “Towards an operational use of remote sensing data (satellite, drone, and ground) for Cultural Heritage: from discovery o documentation, monitoring and valorisation” was carried out as part of a PhD at the University of Basilicata, but a fundamental contribution was made by the possibility offered by the National Research Council (CNR) - Institute of Methodologies for Environmental Analysis (IMAA), and Institute of Her

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Fihn, John, and Johan Finndahl. "A Framework for How to Make Use of an Automatic Passenger Counting System." Thesis, Uppsala universitet, Datorteknik, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-158139.

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Most of the modern cities are today facing tremendous traffic congestions, which is a consequence of an increasing usage of private motor vehicles in the cities. Public transport plays a crucial role to reduce this traffic, but to be an attractive alternative to the use of private motor vehicles the public transport needs to provide services that suit the citizens requirements for travelling. A system that can provide transit agencies with rapid feedback about the usage of their transport network is the Automatic Passenger Counting (APC) system, a system that registers the number of passengers

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Elleuch, Marwa. "Business process discovery from emails, a first step towards business process management in less structured information systems." Electronic Thesis or Diss., Institut polytechnique de Paris, 2021. http://www.theses.fr/2021IPPAS014.

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La fouille de processus vise à analyser les traces d'exécution des systèmes d'information (SI), utilisés dans le cadre des activités métiers, pour découvrir des connaissances sur les processus métiers (PM). D'importants travaux de recherche ont été menés dans ce domaine. Cependant, ils supposent généralement que ces traces d'exécution ont un niveau de structuration élevé. Cela signifie que: (i) ils sont composés d'enregistrements structurés, chacun capturant l'exécution d'une activité, et (ii) une partie des attributs des événements d'exécution (comme le nom de l'activité, l'horodatage) sont e

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