Academic literature on the topic 'Discovery from data'
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Journal articles on the topic "Discovery from data"
Mak, H. Craig. "Discovery from data repositories." Nature Biotechnology 29, no. 1 (January 2011): 46–47. http://dx.doi.org/10.1038/nbt0111-46.
Full textPazzani, M. J. "Knowledge discovery from data?" IEEE Intelligent Systems 15, no. 2 (March 2000): 10–12. http://dx.doi.org/10.1109/5254.850821.
Full textGama, João, and Jesus Aguilar-Ruiz. "Knowledge discovery from data streams." Intelligent Data Analysis 11, no. 1 (March 15, 2007): 1–2. http://dx.doi.org/10.3233/ida-2007-11101.
Full textGama, João, Jesus Aguilar-Ruiz, and Ralf Klinkenberg. "Knowledge discovery from data streams." Intelligent Data Analysis 12, no. 3 (May 30, 2008): 251–52. http://dx.doi.org/10.3233/ida-2008-12301.
Full textGama, João, Auroop Ganguly, Olufemi Omitaomu, Raju Vatsavai, and Mohamed Gaber. "Knowledge discovery from data streams." Intelligent Data Analysis 13, no. 3 (May 27, 2009): 403–4. http://dx.doi.org/10.3233/ida-2009-0372.
Full textMorita, Chie, and Hiroshi Tsukimoto. "Knowledge discovery from numerical data." Knowledge-Based Systems 10, no. 7 (May 1998): 413–19. http://dx.doi.org/10.1016/s0950-7051(98)00040-9.
Full textCook, Diane J., Lawrence B. Holder, and Surnjani Djoko. "Knowledge discovery from structural data." Journal of Intelligent Information Systems 5, no. 3 (November 1995): 229–48. http://dx.doi.org/10.1007/bf00962235.
Full textKalenkova, Anna, Andrea Burattin, Massimiliano de Leoni, Wil van der Aalst, and Alessandro Sperduti. "Discovering high-level BPMN process models from event data." Business Process Management Journal 25, no. 5 (September 2, 2019): 995–1019. http://dx.doi.org/10.1108/bpmj-02-2018-0051.
Full textGottlob, Georg, and Pierre Senellart. "Schema mapping discovery from data instances." Journal of the ACM 57, no. 2 (January 2010): 1–37. http://dx.doi.org/10.1145/1667053.1667055.
Full textVatsavai, Ranga Raju, Olufemi A. Omitaomu, Joao Gama, Nitesh V. Chawla, Mohamed Medhat Gaber, and Auroop R. Ganguly. "Knowledge discovery from sensor data (SensorKDD)." ACM SIGKDD Explorations Newsletter 10, no. 2 (December 20, 2008): 68–73. http://dx.doi.org/10.1145/1540276.1540297.
Full textDissertations / Theses on the topic "Discovery from data"
Höppner, Frank. "Knowledge discovery from sequential data." [S.l. : s.n.], 2003. http://deposit.ddb.de/cgi-bin/dokserv?idn=96728421X.
Full textCao, Huiping. "Pattern discovery from spatiotemporal data." Click to view the E-thesis via HKUTO, 2006. http://sunzi.lib.hku.hk/hkuto/record/B37381520.
Full textCao, 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.
Full textChau, 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.
Full textEl, Sayed Ahmed. "Contributions in knowledge discovery from textual data." Lyon 2, 2008. http://theses.univ-lyon2.fr/documents/lyon2/2008/el-sayed_a.
Full textCette thèse se focalise sur deux problématiques clés liées à la fouille de texte, à savoir : la classification et l'acquisition des connaissances. En dépit de leur relative maturité, ces deux problématiques présentent encore certains défis majeurs qui doivent être soulevés. En premier lieu, pour la classification, un défi bien connu et non résolu consiste à effectuer des classifications avec un minimum de paramètres en entrée. Une façon naturelle de parvenir à cette fin, est d'utiliser les indices de validité dans le processus de classification. Bien qu'ils soient d'un grand intérêt, les indices de validité n'ont pas été largement explorés dans la littérature, en particulier lorsqu'il s'agit de données de grande dimension, comme c'est le cas des données textuelles. Ainsi, concernant ce volet, nous proposons trois principales contributions : (1) une large étude expérimentale comparant huit indices de validité, (2) une méthode basée sur le contexte améliorant l'utilisation des indices de validité en tant que critère d'arrêt, (3) I-CBC, une version incrémentale de l'algorithme flou CBC (classification par comités). Ces contributions ont été validées sur deux applications du monde réel : la classification de documents et de mots. En deuxième lieu, pour l’acquisition des connaissances, nous nous sommes intéressés à des problématiques importantes liées à la construction d’ontologies à partir de texte : le faible rappel des approches basées sur les patrons, la faible précision de l’approche distributionnelle, la dépendance au contexte et l’évolution des ontologies. Nous proposons ainsi, un nouveau cadre pour l’apprentissage d’ontologies à partir du texte. Notre proposition est une approche hybride qui combine les avantages suivants par rapport aux autres approches : (1) la capacité de capturer avec plus de flexibilité des relations dans le texte, (2) des concepts qui traduisent mieux le contexte du corpus considéré, (3) des décisions plus fiables prises durant le processus d’apprentissage à travers la considération et l’inclusion de plusieurs relations sémantiques, et, enfin, (4) l’évolution de l’ontologie apprise sans aucun effort manuel considérable, après son inclusion au coeurd’un système de recherche d’information
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.
Full textWang, 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.
Full textAmado, 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.
Full textThe 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.
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.
Full textCaruccio, Loredana. "Relaxed functional dependencies: definition, discovery and applications." Doctoral thesis, Universita degli studi di Salerno, 2018. http://hdl.handle.net/10556/3051.
Full textFunctional 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]
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Books on the topic "Discovery from data"
Gaber, Mohamed Medhat, Ranga Raju Vatsavai, Olufemi A. Omitaomu, João Gama, Nitesh V. Chawla, and Auroop R. Ganguly, eds. Knowledge Discovery from Sensor Data. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12519-5.
Full textR, Ganguly Auroop, ed. Knowledge discovery from sensor data. Boca Raton: Taylor & Francis, 2009.
Find full textKnowledge discovery from data streams. Boca Raton, FL: Chapman & Hall/CRC, 2010.
Find full textYe, Chen, Hongzhi Wang, and Guojun Dai. Knowledge Discovery from Multi-Sourced Data. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1879-7.
Full textBandyopadhyay, Sanghamitra, Ujjwal Maulik, Lawrence B. Holder, and Diane J. Cook. Advanced Methods for Knowledge Discovery from Complex Data. London: Springer London, 2005. http://dx.doi.org/10.1007/1-84628-284-5.
Full textPáircéir, Rónán. Knowledge discovery from distributed aggregate data in data warehouses and statistical databases. [s.l: The Author], 2002.
Find full text1966-, Ghosh Ashish, Dehuri Satchidananda, and Ghosh Susmita, eds. Multi-objective evolutionary algorithms for knowledge discovery from databases. Berlin: Springer, 2008.
Find full textRead, J. F. CTD data from the north east Atlantic, April 1989, collected on RRS Discovery Cruise 181. Wormley: Institute of Oceanographic Sciences, Deacon Laboratory, 1991.
Find full textMarchese, Francis T. Knowledge Visualization Currents: From Text to Art to Culture. London: Springer London, 2013.
Find full textSuit, William T. Lateral and longitudinal stability and control parameters for the space shuttle Discovery as determined from flight test data. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1988.
Find full textBook chapters on the topic "Discovery from data"
Riddle, Pat, Roman Fresnedo, and David Newman. "Framework for a Generic Knowledge Discovery Toolkit." In Learning from Data, 343–52. New York, NY: Springer New York, 1996. http://dx.doi.org/10.1007/978-1-4612-2404-4_33.
Full textIkonomovska, Elena, and Joao Gama. "Learning Model Trees from Data Streams." In Discovery Science, 52–63. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-88411-8_8.
Full textHasegawa, Hiroshi H., Takashi Washio, and Yukari Ishimiya. "“Thermodynamics” from Time Series Data Analysis." In Discovery Science, 326–27. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-46846-3_33.
Full textGeorge, Dileep, Kazumi Saito, Pat Langley, Stephen Bay, and Kevin R. Arrigo. "Discovering Ecosystem Models from Time-Series Data." In Discovery Science, 141–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-39644-4_13.
Full textAdhikari, Prem Raj, and Jaakko Hollmén. "Mixture Models from Multiresolution 0-1 Data." In Discovery Science, 1–16. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40897-7_1.
Full textHasegawa, H. H., T. Washio, Y. Ishimiya, and T. Saito. "Nonequilibrium Thermodynamics from Time Series Data Analysis." In Discovery Science, 304–5. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-44418-1_35.
Full textTrabold, Daniel, and Tamás Horváth. "Mining Strongly Closed Itemsets from Data Streams." In Discovery Science, 251–66. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67786-6_18.
Full textRinzivillo, S., F. Turini, V. Bogorny, C. Körner, B. Kuijpers, and M. May. "Knowledge Discovery from Geographical Data." In Mobility, Data Mining and Privacy, 243–65. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-75177-9_10.
Full textMorita, Chie, and Hiroshi Tsukimoto. "Knowledge Discovery from Numerical Data." In Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, 425–30. London: CRC Press, 2022. http://dx.doi.org/10.1201/9780429332111-73.
Full textPazouki, Ehsan. "Knowledge Discovery from Agricultural Data." In Encyclopedia of Smart Agriculture Technologies, 1–8. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-030-89123-7_263-1.
Full textConference papers on the topic "Discovery from data"
Wan, Mengting, Xiangyu Chen, Lance Kaplan, Jiawei Han, Jing Gao, and Bo Zhao. "From Truth Discovery to Trustworthy Opinion Discovery." In KDD '16: The 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2939672.2939837.
Full textMetwally, Ahmed, Jia-Yu Pan, Minh Doan, and Christos Faloutsos. "Scalable community discovery from multi-faceted graphs." In 2015 IEEE International Conference on Big Data (Big Data). IEEE, 2015. http://dx.doi.org/10.1109/bigdata.2015.7363859.
Full textPaun, S., M. Fisher, P. Mckee, M. Poesio, and U. Kruschwitz. "Pattern discovery in big data streams." In IET Seminar on Data Analytics 2013: Deriving Intelligence and Value from Big Data. Institution of Engineering and Technology, 2013. http://dx.doi.org/10.1049/ic.2013.0233.
Full textGuo, Zhen, Zhongfei Zhang, Shenghuo Zhu, Yun Chi, and Yihong Gong. "Knowledge Discovery from Citation Networks." In 2009 Ninth IEEE International Conference on Data Mining (ICDM). IEEE, 2009. http://dx.doi.org/10.1109/icdm.2009.137.
Full textYu, Kui, Xindong Wu, Hao Wang, and Wei Ding. "Causal Discovery from Streaming Features." In 2010 IEEE 10th International Conference on Data Mining (ICDM). IEEE, 2010. http://dx.doi.org/10.1109/icdm.2010.82.
Full textBurl, M. C., D. DeCoste, B. L. Enke, D. Mazzoni, W. J. Merline, and L. Scharenbroich. "Automated Knowledge Discovery from Simulators." In Proceedings of the 2006 SIAM International Conference on Data Mining. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2006. http://dx.doi.org/10.1137/1.9781611972764.8.
Full textHashemi, Ray R., Charles Epperson, Alexander A. Tyler, and John F. Young. "Knowledge discovery from sparse pharmacokinetic data." In the 2000 ACM symposium. New York, New York, USA: ACM Press, 2000. http://dx.doi.org/10.1145/335603.335699.
Full textPanigrahi, Sangram, Kesari Verma, Priyanka Tripathi, and Rika Sharma. "Knowledge Discovery from Earth Science Data." In 2014 International Conference on Communication Systems and Network Technologies (CSNT). IEEE, 2014. http://dx.doi.org/10.1109/csnt.2014.85.
Full textOsipov, Gennady S. "Workflows and Their Discovery from Data." In 2007 International Conference on Integration of Knowledge Intensive Multi-Agent Systems. IEEE, 2007. http://dx.doi.org/10.1109/kimas.2007.369835.
Full textSHIMIZU, AKIFUMI, and KENTARO YANO. "GENE DISCOVERY METHODS FROM LARGE-SCALE GENE EXPRESSION DATA." In From Quantum Information to Bio-Informatics. WORLD SCIENTIFIC, 2010. http://dx.doi.org/10.1142/9789814304061_0040.
Full textReports on the topic "Discovery from data"
Ng, Andrew Y., and Christopher D. Manning. Discovery of Deep Structure from Unlabeled Data. Fort Belvoir, VA: Defense Technical Information Center, November 2014. http://dx.doi.org/10.21236/ada614158.
Full textCapraro, Gerard T., and Gerald B. Berdan. Intensive Knowledge Discovery from Heterogeneous Distributed Data and Knowledge. Fort Belvoir, VA: Defense Technical Information Center, October 2001. http://dx.doi.org/10.21236/ada397959.
Full textPeterka, Tom, Deborah Bard, Janine Bennett, E. Wes Bethel, Ron Oldfield, Line Pouchard, Christine Sweeney, and Matthew Wolf. ASCR Workshop on In Situ Data Management: Enabling Scientific Discovery from Diverse Data Sources. Office of Scientific and Technical Information (OSTI), February 2019. http://dx.doi.org/10.2172/1493245.
Full textDassanayake, Wajira, Xiaoming Li, and Klaus Buhr. A Revisit of Price Discovery Dynamics Across Australia and New Zealand. Unitec ePress, August 2015. http://dx.doi.org/10.34074/rsrp.039.
Full textDassanayake, Wajira, Xiaoming Li, and Klaus Buhr. A Revisit of Price Discovery Dynamics Across Australia and New Zealand. Unitec ePress, August 2015. http://dx.doi.org/10.34074/rsrp.039.
Full textBrosh, Arieh, Gordon Carstens, Kristen Johnson, Ariel Shabtay, Joshuah Miron, Yoav Aharoni, Luis Tedeschi, and Ilan Halachmi. Enhancing Sustainability of Cattle Production Systems through Discovery of Biomarkers for Feed Efficiency. United States Department of Agriculture, July 2011. http://dx.doi.org/10.32747/2011.7592644.bard.
Full textSloan, Steven, Shelby Peterie, Richard Miller, Julian Ivanov, J. Schwenk, and Jason McKenna. Detecting clandestine tunnels by using near-surface seismic techniques. Engineer Research and Development Center (U.S.), April 2021. http://dx.doi.org/10.21079/11681/40419.
Full textIdakwo, Gabriel, Sundar Thangapandian, Joseph Luttrell, Zhaoxian Zhou, Chaoyang Zhang, and Ping Gong. Deep learning-based structure-activity relationship modeling for multi-category toxicity classification : a case study of 10K Tox21 chemicals with high-throughput cell-based androgen receptor bioassay data. Engineer Research and Development Center (U.S.), July 2021. http://dx.doi.org/10.21079/11681/41302.
Full textFloyd, Jason, and Daniel Madrzykowski. Analysis of a Near Miss in a Garden Apartment Fire – Georgia 2022. UL's Fire Safety Research Institute, October 2022. http://dx.doi.org/10.54206/102376/rsfd6862.
Full textMa, Yunxing, Julia Brettschneider, and Joanna Collingwood. A systematic review and meta-analysis of cerebrospinal fluid amyloid and tau levels in patients progressing from Mild Cognitive Impairment to Alzheimer’s Disease. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, July 2022. http://dx.doi.org/10.37766/inplasy2022.7.0020.
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