Auswahl der wissenschaftlichen Literatur zum Thema „Incremental Schema Discovery“
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Zeitschriftenartikel zum Thema "Incremental Schema Discovery"
Fotiadis, D. I., A. Likas und K. Blekas. „A Sequential Method for Discovering Probabilistic Motifs in Proteins“. Methods of Information in Medicine 43, Nr. 01 (2004): 9–12. http://dx.doi.org/10.1055/s-0038-1633414.
Der volle Inhalt der QuelleCarpenter, Chris. „Study Examines Incremental Method vs. Split Conditions in Reserves Booking“. Journal of Petroleum Technology 73, Nr. 12 (01.12.2021): 35–36. http://dx.doi.org/10.2118/1221-0035-jpt.
Der volle Inhalt der QuelleKluger, Avraham N., Limor Borut, Michal Lehmann, Tal Nir, Ella Azoulay, Ofri Einy und Galit Gordoni. „A New Measure of the Rogerian Schema of the Good Listener“. Sustainability 14, Nr. 19 (09.10.2022): 12893. http://dx.doi.org/10.3390/su141912893.
Der volle Inhalt der QuelleHan, Zhiyan, und Jian Wang. „A Fault Diagnosis Method Based on Active Example Selection“. Journal of Circuits, Systems and Computers 27, Nr. 01 (23.08.2017): 1850013. http://dx.doi.org/10.1142/s0218126618500135.
Der volle Inhalt der QuelleVenkata Sailaja, N., L. Padmasree und N. Mangathayaru. „Incremental learning for text categorization using rough set boundary based optimized Support Vector Neural Network“. Data Technologies and Applications 54, Nr. 5 (03.07.2020): 585–601. http://dx.doi.org/10.1108/dta-03-2020-0071.
Der volle Inhalt der QuelleGutierrez, Dubert, Vinodh Kumar, Robert G. Moore und Sudarshan A. Mehta. „Air Injection and Waterflood Performance Comparison of Two Adjacent Units in the Buffalo Field“. SPE Reservoir Evaluation & Engineering 11, Nr. 05 (01.10.2008): 848–57. http://dx.doi.org/10.2118/104479-pa.
Der volle Inhalt der QuelleMaw, Aye Aye, Maxim Tyan, Tuan Anh Nguyen und Jae-Woo Lee. „iADA*-RL: Anytime Graph-Based Path Planning with Deep Reinforcement Learning for an Autonomous UAV“. Applied Sciences 11, Nr. 9 (27.04.2021): 3948. http://dx.doi.org/10.3390/app11093948.
Der volle Inhalt der QuelleLeake, Bernard Elgey. „Mechanism of emplacement and crystallisation history of the northern margin and centre of the Galway Granite, western Ireland“. Transactions of the Royal Society of Edinburgh: Earth Sciences 97, Nr. 1 (März 2006): 1–23. http://dx.doi.org/10.1017/s0263593300001371.
Der volle Inhalt der QuelleHagen, Christoph, Christian Weinert, Christoph Sendner, Alexandra Dmitrienko und Thomas Schneider. „Contact Discovery in Mobile Messengers: Low-cost Attacks, Quantitative Analyses, and Efficient Mitigations“. ACM Transactions on Privacy and Security, 30.06.2022. http://dx.doi.org/10.1145/3546191.
Der volle Inhalt der QuellePratap, Aditya, Arpita Das, Shiv Kumar und Sanjeev Gupta. „Current Perspectives on Introgression Breeding in Food Legumes“. Frontiers in Plant Science 11 (21.01.2021). http://dx.doi.org/10.3389/fpls.2020.589189.
Der volle Inhalt der QuelleDissertationen zum Thema "Incremental Schema Discovery"
Bouhamoum, Redouane. „Découverte automatique de schéma pour les données irrégulières et massives“. Electronic Thesis or Diss., université Paris-Saclay, 2021. http://www.theses.fr/2021UPASG081.
Der volle Inhalt der QuelleThe web of data is a huge global data space, relying on semantic web technologies, where a high number of sources are published and interlinked. This data space provides an unprecedented amount of knowledge available for novel applications, but the meaningful usage of its sources is often difficult due to the lack of schema describing the content of these data sources. Several automatic schema discovery approaches have been proposed, but while they provide good quality schemas, their use for massive data sources is a challenge as they rely on costly algorithms. In our work, we are interested in both the scalability and the incrementality of schema discovery approaches for RDF data sources where the schema is incomplete or missing.Furthermore, we extend schema discovery to take into account not only the explicit information provided by a data source, but also the implicit information which can be inferred.Our first contribution consists of a scalable schema discovery approach which extracts the classes describing the content of a massive RDF data source.We have proposed to extract a condensed representation of the source, which will be used as an input to the schema discovery process in order to improve its performances.This representation is a set of patterns, each one representing a combination of properties describing some entities in the dataset. We have also proposed a scalable schema discovery approach relying on a distributed clustering algorithm that forms groups of structurally similar entities representing the classes of the schema.Our second contribution aims at maintaining the generated schema consistent with the data source it describes, as this latter may evolve over time. We propose an incremental schema discovery approach that modifies the set of extracted classes by propagating the changes occurring at the source, in order to keep the schema consistent with its evolutions.Finally, the goal of our third contribution is to extend schema discovery to consider the whole semantics expressed by a data source, which is represented not only by the explicitly declared triples, but also by the ones which can be inferred through reasoning. We propose an extension allowing to take into account all the properties of an entity during schema discovery, represented either by explicit or by implicit triples, which will improve the quality of the generated schema
Buchteile zum Thema "Incremental Schema Discovery"
Fan, Hao. „Using Schema Transformation Pathways for Incremental View Maintenance“. In Data Warehousing and Knowledge Discovery, 126–35. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11546849_13.
Der volle Inhalt der QuelleZhou, Aoying, Jinwen, Zhou Shuigeng und Zenping Tian. „Incremental Mining of Schema for Semistructured Data“. In Methodologies for Knowledge Discovery and Data Mining, 159–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48912-6_22.
Der volle Inhalt der QuelleBouhamoum, Redouane, Zoubida Kedad und Stéphane Lopes. „Incremental Schema Discovery at Scale for RDF Data“. In The Semantic Web, 195–211. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-77385-4_12.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Incremental Schema Discovery"
Chih-Ho Chen, Yung Ting und Jia-Sheng Heh. „Low Overhead Incremental Checkpointing and Rollback Recovery Scheme on Windows Operating System“. In 2010 3rd International Conference on Knowledge Discovery and Data Mining (WKDD 2010). IEEE, 2010. http://dx.doi.org/10.1109/wkdd.2010.135.
Der volle Inhalt der QuelleAzzarone, Eleonora, Roberto Rossi, Giovanni Cirillo und Giacomo Micheletti. „An Enhanced Integrated Asset Model for Offshore Field Development Strategy“. In ADIPEC. SPE, 2022. http://dx.doi.org/10.2118/211476-ms.
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