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Artykuły w czasopismach na temat "Metadata mining"
Sutton, Stuart A. "Mining the Metadata Quarries". Bulletin of the American Society for Information Science and Technology 29, nr 2 (31.01.2005): 11. http://dx.doi.org/10.1002/bult.267.
Pełny tekst źródłaIllien, Gildas. "Metadata mining : fouiller les données des catalogues ?" Enrichir pour partager, nr 76 (1.10.2014): 15–16. http://dx.doi.org/10.35562/arabesques.890.
Pełny tekst źródłaŞah, Melike, i Vincent Wade. "Automatic metadata mining from multilingual enterprise content". Journal of Web Semantics 11 (marzec 2012): 41–62. http://dx.doi.org/10.1016/j.websem.2011.11.001.
Pełny tekst źródłaLI, G., H. SHENG i X. FAN. "Incorporating Metadata into Data Mining with Ontology". IEICE Transactions on Information and Systems E90-D, nr 6 (1.06.2007): 983–85. http://dx.doi.org/10.1093/ietisy/e90-d.6.983.
Pełny tekst źródłaMurraças, Adriana, Paula Maria Vaz Martins, Carlos Daniel Cipriani Ferreira, Tiago Marques Godinho i Augusto Marques Ferreira da Silva. "Data Mining of MR Technical Parameters". International Journal of E-Health and Medical Communications 12, nr 1 (styczeń 2021): 16–33. http://dx.doi.org/10.4018/ijehmc.2021010102.
Pełny tekst źródłaNurandini, Indri, i Arief Fatchul Huda. "Klastering Dokumen dengan Menambahkan Metadata Menggunakan Algoritma COATES". Kubik: Jurnal Publikasi Ilmiah Matematika 2, nr 2 (30.11.2017): 39–44. http://dx.doi.org/10.15575/kubik.v2i2.1859.
Pełny tekst źródłaWang, Fei Chao. "A Novel Approach to Mine Knowledge from Social Images". Advanced Materials Research 430-432 (styczeń 2012): 1068–71. http://dx.doi.org/10.4028/www.scientific.net/amr.430-432.1068.
Pełny tekst źródłaIntagorn, Suradej, i Kristina Lerman. "Mining Geospatial Knowledge on the Social Web". International Journal of Information Systems for Crisis Response and Management 3, nr 2 (kwiecień 2011): 33–47. http://dx.doi.org/10.4018/jiscrm.2011040103.
Pełny tekst źródłaSu, Shian, Vincent J. Carey, Lori Shepherd, Matthew Ritchie, Martin T. Morgan i Sean Davis. "BiocPkgTools: Toolkit for mining the Bioconductor package ecosystem". F1000Research 8 (29.05.2019): 752. http://dx.doi.org/10.12688/f1000research.19410.1.
Pełny tekst źródłaAlgur, Siddu P., i Prashant Bhat. "Web Video Mining: Metadata Predictive Analysis using Classification Techniques". International Journal of Information Technology and Computer Science 8, nr 2 (8.02.2016): 69–77. http://dx.doi.org/10.5815/ijitcs.2016.02.09.
Pełny tekst źródłaRozprawy doktorskie na temat "Metadata mining"
Demšar, Urška. "Exploring geographical metadata by automatic and visual data mining". Licentiate thesis, KTH, Infrastructure, 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-1779.
Pełny tekst źródłaMetadata are data about data. They describe characteristicsand content of an original piece of data. Geographical metadatadescribe geospatial data: maps, satellite images and othergeographically referenced material. Such metadata have twocharacteristics, high dimensionality and diversity of attributedata types, which present a problem for traditional data miningalgorithms.
Other problems that arise during the exploration ofgeographical metadata are linked to the expertise of the userperforming the analysis. The large amounts of metadata andhundreds of possible attributes limit the exploration for anon-expert user, which results in a potential loss ofinformation that is hidden in metadata.
In order to solve some of these problems, this thesispresents an approach for exploration of geographical metadataby a combination of automatic and visual data mining.
Visual data mining is a principle that involves the human inthe data exploration by presenting the data in some visualform, allowing the human to get insight into the data and torecognise patterns. The main advantages of visual dataexploration over automatic data mining are that the visualexploration allows a direct interaction with the user, that itis intuitive and does not require complex understanding ofmathematical or statistical algorithms. As a result the userhas a higher confidence in the resulting patterns than if theywere produced by computer only.
In the thesis we present the Visual data mining tool (VDMtool), which was developed for exploration of geographicalmetadata for site planning. The tool provides five differentvisualisations: a histogram, a table, a pie chart, a parallelcoordinates visualisation and a clustering visualisation. Thevisualisations are connected using the interactive selectionprinciple called brushing and linking.
In the VDM tool the visual data mining concept is integratedwith an automatic data mining method, clustering, which finds ahierarchical structure in the metadata, based on similarity ofmetadata items. In the thesis we present a visualisation of thehierarchical structure in the form of a snowflake graph.
Keywords:visualisation, data mining, clustering, treedrawing, geographical metadata.
Tang, Yaobin. "Butterfly -- A model of provenance". Worcester, Mass. : Worcester Polytechnic Institute, 2009. http://www.wpi.edu/Pubs/ETD/Available/etd-031309-095511/.
Pełny tekst źródłaRamakrishnan, Cartic. "Extracting, Representing and Mining Semantic Metadata from Text: Facilitating Knowledge Discovery in Biomedicine". Wright State University / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=wright1222021939.
Pełny tekst źródłaDong, Zheng. "Automated Extraction and Retrieval of Metadata by Data Mining : a Case Study of Mining Engine for National Land Survey Sweden". Thesis, University of Gävle, Department of Technology and Built Environment, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-6811.
Pełny tekst źródłaMetadata is the important information describing geographical data resources and their key elements. It is used to guarantee the availability and accessibility of the data. ISO 19115 is a metadata standard for geographical information, making the geographical metadata shareable, retrievable, and understandable at the global level. In order to cope with the massive, high-dimensional and high-diversity nature of geographical data, data mining is an applicable method to discover the metadata.
This thesis develops and evaluates an automated mining method for extracting metadata from the data environment on the Local Area Network at the National Land Survey of Sweden (NLS). These metadata are prepared and provided across Europe according to the metadata implementing rules for the Infrastructure for Spatial Information in Europe (INSPIRE). The metadata elements are defined according to the numerical formats of four different data entities: document data, time-series data, webpage data, and spatial data. For evaluating the method for further improvement, a few attributes and corresponding metadata of geographical data files are extracted automatically as metadata record in testing, and arranged in database. Based on the extracted metadata schema, a retrieving functionality is used to find the file containing the keyword of metadata user input. In general, the average success rate of metadata extraction and retrieval is 90.0%.
The mining engine is developed in C# programming language on top of the database using SQL Server 2005. Lucene.net is also integrated with Visual Studio 2005 to build an indexing framework for extracting and accessing metadata in database.
Al-Natsheh, Hussein. "Text Mining Approaches for Semantic Similarity Exploration and Metadata Enrichment of Scientific Digital Libraries". Thesis, Lyon, 2019. http://www.theses.fr/2019LYSE2062.
Pełny tekst źródłaFor scientists and researchers, it is very critical to ensure knowledge is accessible for re-use and development. Moreover, the way we store and manage scientific articles and their metadata in digital libraries determines the amount of relevant articles we can discover and access depending on what is actually meant in a search query. Yet, are we able to explore all semantically relevant scientific documents with the existing keyword-based search information retrieval systems? This is the primary question addressed in this thesis. Hence, the main purpose of our work is to broaden or expand the knowledge spectrum of researchers working in an interdisciplinary domain when they use the information retrieval systems of multidisciplinary digital libraries. However, the problem raises when such researchers use community-dependent search keywords while other scientific names given to relevant concepts are being used in a different research community.Towards proposing a solution to this semantic exploration task in multidisciplinary digital libraries, we applied several text mining approaches. First, we studied the semantic representation of words, sentences, paragraphs and documents for better semantic similarity estimation. In addition, we utilized the semantic information of words in lexical databases and knowledge graphs in order to enhance our semantic approach. Furthermore, the thesis presents a couple of use-case implementations of our proposed model
Petersson, Andreas. "Data mining file sharing metadata : A comparison between Random Forests Classificiation and Bayesian Networks". Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-11180.
Pełny tekst źródłaPetersson, Andreas. "Data mining file sharing metadata : A comparison between Random Forests Classification and Bayesian Networks". Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-11285.
Pełny tekst źródłaFerrill, Paul. "REFERENCE DESIGN FOR A SQUADRON LEVEL DATA ARCHIVAL SYSTEM". International Foundation for Telemetering, 2006. http://hdl.handle.net/10150/604259.
Pełny tekst źródłaAs more aircraft are fitted with solid state memory recording systems, the need for a large data archival storage system becomes increasingly important. In addition, there is a need to keep classified and unclassified data separate but available to the aircrews for training and debriefing along with some type of system for cataloging and searching for specific missions. This paper will present a novel approach along with a reference design for using commercially available hardware and software and a minimal amount of custom programming to help address these issues.
Lockard, Michael T., R. Rajagopalan i James A. Garling. "MINING IRIG-106 CHAPTER 10 AND HDF-5 DATA". International Foundation for Telemetering, 2006. http://hdl.handle.net/10150/604264.
Pełny tekst źródłaRapid access to ever-increasing amounts of test data is becoming a problem. The authors have developed a data-mining methodology solution approach to provide a solution to catalog test files, search metadata attributes to derive test data files of interest, and query test data measurements using a web-based engine to produce results in seconds. Generated graphs allow the user to visualize an overview of the entire test for a selected set of measurements, with areas highlighted where the query conditions were satisfied. The user can then zoom into areas of interest and export selected information.
Srinivasan, Uma Computer Science & Engineering Faculty of Engineering UNSW. "A FRAMEWORK FOR CONCEPTUAL INTEGRATION OF HETEROGENEOUS DATABASES". Awarded by:University of New South Wales. School of Computer Science and Engineering, 1997. http://handle.unsw.edu.au/1959.4/33463.
Pełny tekst źródłaKsiążki na temat "Metadata mining"
Sebastian, Nordhoff, Hellmann Sebastian i SpringerLink (Online service), red. Linked Data in Linguistics: Representing and Connecting Language Data and Language Metadata. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.
Znajdź pełny tekst źródłaMultimedia information extraction: Advances in video, audio, and imagery analysis for search, data mining, surveillance, and authoring. Hoboken, N.J: Wiley, 2012.
Znajdź pełny tekst źródłaGarcía-Barriocanal, Elena. Metadata and Semantic Research: 5th International Conference, MTSR 2011, Izmir, Turkey, October 12-14, 2011. Proceedings. Berlin, Heidelberg: Springer-Verlag GmbH Berlin Heidelberg, 2011.
Znajdź pełny tekst źródłaSartori, Fabio. Metadata and Semantic Research: Third International Conference, MTSR 2009, Milan, Italy, October 1-2, 2009. Proceedings. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009.
Znajdź pełny tekst źródłaSanchez-Alonso, Salvador. Metadata and Semantic Research: 4th International Conference, MTSR 2010, Alcalá de Henares, Spain, October 20-22, 2010. Proceedings. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010.
Znajdź pełny tekst źródłaauthor, Bell Genevieve, Gregg Melissa 1978 author i Seaver Nick 1985 author, red. Data, now bigger and better! Chicago: Prickly Paradigm Press, 2015.
Znajdź pełny tekst źródłaRuleML 2009 (2009 Las Vegas, Nev.). Rule interchange and applications: International symposium, RuleML 2009, Las Vegas, Nevada, USA, November 5-7, 2009 : proceedings. Berlin: Springer, 2009.
Znajdź pełny tekst źródłaDuke, D. J. (David J.), red. Semantic multimedia: Third International Conference on Semantic and Digital Media Technologies, SAMT 2008, Koblenz, Germany, December 3-5, 2008 : proceedings. Berlin: Springer, 2008.
Znajdź pełny tekst źródłaMaybury, Mark T. Multimedia Information Extraction: Advances in Video, Audio, and Imagery Analysis for Search, Data Mining, Surveillance and Authoring. IEEE Computer Society Press, 2012.
Znajdź pełny tekst źródłaMaybury, Mark T. Multimedia Information Extraction: Advances in Video, Audio, and Imagery Analysis for Search, Data Mining, Surveillance and Authoring. IEEE Computer Society Press, 2012.
Znajdź pełny tekst źródłaCzęści książek na temat "Metadata mining"
Hacke, Melanie. "Chapter 4. Metadata mining". W Literary Translation in Periodicals, 95–120. Amsterdam: John Benjamins Publishing Company, 2020. http://dx.doi.org/10.1075/btl.155.04hac.
Pełny tekst źródłaRousidis, Dimitrios, Paraskevas Koukaras i Christos Tjortjis. "Examination of NoSQL Transition and Data Mining Capabilities". W Metadata and Semantic Research, 110–15. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71903-6_11.
Pełny tekst źródłaSegura, Alejandra, Christian Vidal, Victor Menendez, Alfredo Zapata i Manuel Prieto. "Exploring Characterizations of Learning Object Repositories Using Data Mining Techniques". W Metadata and Semantic Research, 215–25. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04590-5_20.
Pełny tekst źródłaSeol, Jae-Wook, Won-Jun Choi, Hee-Seok Jeong, Hye-Kyong Hwang i Hwa-Mook Yoon. "Reference Metadata Extraction from Korean Research Papers". W Mining Intelligence and Knowledge Exploration, 42–52. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-05918-7_5.
Pełny tekst źródłaJiang, Tao, i Ah-Hwee Tan. "Mining RDF Metadata for Generalized Association Rules". W Lecture Notes in Computer Science, 223–33. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11827405_22.
Pełny tekst źródłaZhao, Xiaoyong, Yang Yang, Li-li Sun i Han Huang. "Metadata-Aware Small Files Storage Architecture on Hadoop". W Web Information Systems and Mining, 136–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33469-6_20.
Pełny tekst źródłaMerrett, T. H. "Attribute Metadata for Relational OLAP and Data Mining". W Database Programming Languages, 97–118. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-46093-4_6.
Pełny tekst źródłaTejasree, S., i Shaik Naseera. "Improved Clustering Technique Using Metadata for Text Mining". W Innovations in Computer Science and Engineering, 243–50. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-7082-3_29.
Pełny tekst źródłaTan, Rong, Junzhong Gu, Zhou Zhong i Peng Chen. "Metadata Management of Context Resources in Context-Aware Middleware System". W Web Information Systems and Mining, 350–57. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33469-6_46.
Pełny tekst źródłaCui, Binge, i Jie Zhang. "An Intelligent Metadata Extraction Approach Based on Programming by Demonstration". W Web Information Systems and Mining, 678–85. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33469-6_84.
Pełny tekst źródłaStreszczenia konferencji na temat "Metadata mining"
Arotaritei, Dragos. "Data mining in metadata repositories". W AeroSense 2002, redaktor Belur V. Dasarathy. SPIE, 2002. http://dx.doi.org/10.1117/12.460213.
Pełny tekst źródła"Computational Linguistics for Metadata Building (CLiMB) Text Mining for the Automatic Extraction of Subject Terms for Image Metadata". W International Workshop on Metadata Mining for Image Understanding. SciTePress - Science and and Technology Publications, 2008. http://dx.doi.org/10.5220/0002338100030012.
Pełny tekst źródła"Combining Visual and Text Features for Learning in Multimedia Direct Marketing Domain". W International Workshop on Metadata Mining for Image Understanding. SciTePress - Science and and Technology Publications, 2008. http://dx.doi.org/10.5220/0002337200340047.
Pełny tekst źródła"Automatic Image Annotation using Visual Content and Folksonomies". W International Workshop on Metadata Mining for Image Understanding. SciTePress - Science and and Technology Publications, 2008. http://dx.doi.org/10.5220/0002337600580066.
Pełny tekst źródła"Travel Blog Assistant System (TBAS) - An Example Scenario of How to Enrich Text with Images and Images with Text using Online Multimedia Repositories". W International Workshop on Metadata Mining for Image Understanding. SciTePress - Science and and Technology Publications, 2008. http://dx.doi.org/10.5220/0002338200900104.
Pełny tekst źródła"Describing the Where – Improving Image Annotation and Search through Geography". W International Workshop on Metadata Mining for Image Understanding. SciTePress - Science and and Technology Publications, 2008. http://dx.doi.org/10.5220/0002338401050114.
Pełny tekst źródła"Which Strategy to Combine Face Identification Tools with Clothing Similarity: Contesting or Reinforcing?" W International Workshop on Metadata Mining for Image Understanding. SciTePress - Science and and Technology Publications, 2008. http://dx.doi.org/10.5220/0002338500780089.
Pełny tekst źródła"Improved Image Retrieval using Visual Sorting and Semi-Automatic Semantic Categorization of Images". W International Workshop on Metadata Mining for Image Understanding. SciTePress - Science and and Technology Publications, 2008. http://dx.doi.org/10.5220/0002339400670077.
Pełny tekst źródła"Can Feature Information Interaction Help for Information Fusion in Multimedia Problems?" W International Workshop on Metadata Mining for Image Understanding. SciTePress - Science and and Technology Publications, 2008. http://dx.doi.org/10.5220/0002339500230033.
Pełny tekst źródła"Extracting Semantic Meaning from Photographic Annotations using a Hybrid Approach". W International Workshop on Metadata Mining for Image Understanding. SciTePress - Science and and Technology Publications, 2008. http://dx.doi.org/10.5220/0002339700480057.
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