Artículos de revistas: "Metadata mining"

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Sutton, Stuart A. "Mining the Metadata Quarries". Bulletin of the American Society for Information Science and Technology 29, n.º 2 (31 de enero de 2005): 11. http://dx.doi.org/10.1002/bult.267.

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2

Illien, Gildas. "Metadata mining : fouiller les données des catalogues ?" Enrichir pour partager, n.º 76 (1 de octubre de 2014): 15–16. http://dx.doi.org/10.35562/arabesques.890.

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3

Şah, Melike y Vincent Wade. "Automatic metadata mining from multilingual enterprise content". Journal of Web Semantics 11 (marzo de 2012): 41–62. http://dx.doi.org/10.1016/j.websem.2011.11.001.

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4

LI, G., H. SHENG y X. FAN. "Incorporating Metadata into Data Mining with Ontology". IEICE Transactions on Information and Systems E90-D, n.º 6 (1 de junio de 2007): 983–85. http://dx.doi.org/10.1093/ietisy/e90-d.6.983.

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5

Murraças, Adriana, Paula Maria Vaz Martins, Carlos Daniel Cipriani Ferreira, Tiago Marques Godinho y Augusto Marques Ferreira da Silva. "Data Mining of MR Technical Parameters". International Journal of E-Health and Medical Communications 12, n.º 1 (enero de 2021): 16–33. http://dx.doi.org/10.4018/ijehmc.2021010102.

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Exposure to radiofrequency (RF) energy during a magnetic resonance imaging exam is a safety concern related to biological thermal effects. Estimation of the specific absorption rate (SAR) is done by manufacturer scanner integrated tools to monitor RF energy. This work presents an exploratory approach of DICOM metadata focused in whole-body SAR values, patient dependent parameters, and pulse sequences. Previously acquired abdominopelvic and head studies were retrieved from a 3 Tesla scanner. Dicoogle tool was used for metadata indexing, mining, and extraction. Specifically weighted pulse sequences were related with weight, BMI, and gender through boxplot diagrams and effect size analysis. A decrease of SAR values with increasing body weight and BMI categories is observable for abdominopelvic studies. Head studies showed different trends regarding distinct pulse sequences; in addition, underage patients register higher SAR values compared to adults. Male individuals register marginally higher SAR values. Metadata recording practices and standardization need to be improved.

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6

Nurandini, Indri y Arief Fatchul Huda. "Klastering Dokumen dengan Menambahkan Metadata Menggunakan Algoritma COATES". Kubik: Jurnal Publikasi Ilmiah Matematika 2, n.º 2 (30 de noviembre de 2017): 39–44. http://dx.doi.org/10.15575/kubik.v2i2.1859.

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Text mining adalah proses ekstraksi pola berupa informasi dan pengetahuan yang berguna dari sejumlah besar sumber data tak terstruktur. Salah satu perkembangan text mining adalah ruang lingkup perbaikan dari pemanfaatan sebuah “side information” yang digunakan untuk membantu proses klastering yang lebih efisien. “side information” yang dimiliki data dapat membantu proses text mining jika “side information” tersebut bersifat informatif. Di dalam “side information” , metadata merupakan bagian dari “side information” yang dimiliki oleh data. Oleh karena itu, algoritma klastering partisi klasik dan model probabilistik dalam text mining telah dikembangkan untuk memproses data bersama “side information” dengan menggunakan algoritma Content and Auxiliary attribute Based Text Clustering (COATES). Adapun proses klastering ini menggunakan inisialisasi klaster dengan algoritma k-means berdasarkan perhitungan jarak euclidean distance.

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7

Wang, Fei Chao. "A Novel Approach to Mine Knowledge from Social Images". Advanced Materials Research 430-432 (enero de 2012): 1068–71. http://dx.doi.org/10.4028/www.scientific.net/amr.430-432.1068.

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With the popularity of various social media website, currently, lots of social images attached with different kinds of metadata have been uploaded to social media websites. Mining useful knowledge from social images has been an emerging important research topic in web search and data mining. In this paper, we propose a novel approach to find geographical difference of a given concept from social image community. We put a given concept to social image community, and then downloaded social images with metadata, particularly, the place where the photo was taken should be provided in advance. Firstly, concept is submitted to social image community, and then social images with different kinds of metadata are downloaded. Secondly, social images are clustered according to metadata of images. Finally, the information of concept’s geographical difference is found. Experiments conducted on social image community proof the effectiveness of our approach. Keywords: Social Images, Data Mining, Social Image Community, Image Clustering.

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8

Intagorn, Suradej y Kristina Lerman. "Mining Geospatial Knowledge on the Social Web". International Journal of Information Systems for Crisis Response and Management 3, n.º 2 (abril de 2011): 33–47. http://dx.doi.org/10.4018/jiscrm.2011040103.

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Up-to-date geospatial information can help crisis management community to coordinate its response. In addition to data that is created and curated by experts, there is an abundance of user-generated, user-curated data on Social Web sites such as Flickr, Twitter, and Google Earth. User-generated data and metadata can be used to harvest knowledge, including geospatial knowledge that will help solve real-world problems including information discovery, geospatial information integration and data management. This paper proposes a method for acquiring geospatial knowledge in the form of places and relations between them from the user-generated data and metadata on the Social Web. The key to acquiring geospatial knowledge from social metadata is the ability to accurately represent places. The authors describe a simple, efficient algorithm for finding a non-convex boundary of a region from a sample of points from that region. Used within a procedure that learns part-of relations between places from real-world data extracted from the social photo-sharing site Flickr, the proposed algorithm leads to more precise relations than the earlier method and helps uncover knowledge not contained in expert-curated geospatial knowledge bases.

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9

Su, Shian, Vincent J. Carey, Lori Shepherd, Matthew Ritchie, Martin T. Morgan y Sean Davis. "BiocPkgTools: Toolkit for mining the Bioconductor package ecosystem". F1000Research 8 (29 de mayo de 2019): 752. http://dx.doi.org/10.12688/f1000research.19410.1.

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Motivation: The Bioconductor project, a large collection of open source software for the comprehension of large-scale biological data, continues to grow with new packages added each week, motivating the development of software tools focused on exposing package metadata to developers and users. The resulting BiocPkgTools package facilitates access to extensive metadata in computable form covering the Bioconductor package ecosystem, facilitating downstream applications such as custom reporting, data and text mining of Bioconductor package text descriptions, graph analytics over package dependencies, and custom search approaches. Results: The BiocPkgTools package has been incorporated into the Bioconductor project, installs using standard procedures, and runs on any system supporting R. It provides functions to load detailed package metadata, longitudinal package download statistics, package dependencies, and Bioconductor build reports, all in "tidy data" form. BiocPkgTools can convert from tidy data structures to graph structures, enabling graph-based analytics and visualization. An end-user-friendly graphical package explorer aids in task-centric package discovery. Full documentation and example use cases are included. Availability: The BiocPkgTools software and complete documentation are available from Bioconductor (https://bioconductor.org/packages/BiocPkgTools).

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Algur, Siddu P. y Prashant Bhat. "Web Video Mining: Metadata Predictive Analysis using Classification Techniques". International Journal of Information Technology and Computer Science 8, n.º 2 (8 de febrero de 2016): 69–77. http://dx.doi.org/10.5815/ijitcs.2016.02.09.

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Bhanuse, Shraddha S., Shailesh D. Kamble y Sandeep M. Kakde. "Text Mining Using Metadata for Generation of Side Information". Procedia Computer Science 78 (2016): 807–14. http://dx.doi.org/10.1016/j.procs.2016.02.061.

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12

Davulcu, Hasan, Srinivas Vadrevu y Saravanakumar Nagarajan. "OntoMiner: automated metadata and instance mining from news websites". International Journal of Web and Grid Services 1, n.º 2 (2005): 196. http://dx.doi.org/10.1504/ijwgs.2005.008320.

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13

Mastroianni, Carlo, Domenico Talia y Paolo Trunfio. "Metadata for Managing Grid Resources in Data Mining Applications". Journal of Grid Computing 2, n.º 1 (marzo de 2004): 85–102. http://dx.doi.org/10.1007/s10723-004-2809-x.

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14

Wang, Zichen, Alexander Lachmann y Avi Ma’ayan. "Mining data and metadata from the gene expression omnibus". Biophysical Reviews 11, n.º 1 (29 de diciembre de 2018): 103–10. http://dx.doi.org/10.1007/s12551-018-0490-8.

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15

Goudannavar, Basavaraj A. y Prashant Bhat. "Frequent Itemset Mining A Metadata Based Approach for Knowledge Discovery". International Journal of Computer Sciences and Engineering 6, n.º 3 (30 de marzo de 2018): 316–20. http://dx.doi.org/10.26438/ijcse/v6i3.316320.

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16

Gemmeren, P. van y D. Malon. "Event metadata records as a testbed for scalable data mining". Journal of Physics: Conference Series 219, n.º 4 (1 de abril de 2010): 042057. http://dx.doi.org/10.1088/1742-6596/219/4/042057.

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17

Mathaikutty, Deepak A. y Sandeep K. Shukla. "Mining metadata for composability of IPs from SystemC IP library". Design Automation for Embedded Systems 12, n.º 1-2 (19 de abril de 2008): 63–94. http://dx.doi.org/10.1007/s10617-008-9013-3.

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18

Tolwinska, Anna. "Participation Reports help Crossref members drive research further". Science Editing 8, n.º 2 (20 de agosto de 2021): 180–85. http://dx.doi.org/10.6087/kcse.253.

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This article aims to explain the key metadata elements listed in Participation Reports, why it’s important to check them regularly, and how Crossref members can improve their scores. Crossref members register a lot of metadata in Crossref. That metadata is machine-readable, standardized, and then shared across discovery services and author tools. This is important because richer metadata makes content more discoverable and useful to the scholarly community. It’s not always easy to know what metadata Crossref members register in Crossref. This is why Crossref created an easy-to-use tool called Participation Reports to show editors, and researchers the key metadata elements Crossref members register to make their content more useful. The key metadata elements include references and whether they are set to open, ORCID iDs, funding information, Crossmark metadata, licenses, full-text URLs for text-mining, and Similarity Check indexing, as well as abstracts. ROR IDs (Research Organization Registry Identifiers), that identify institutions will be added in the future. This data was always available through the Crossref ’s REST API (Representational State Transfer Application Programming Interface) but is now visualized in Participation Reports. To improve scores, editors should encourage authors to submit ORCIDs in their manuscripts and publishers should register as much metadata as possible to help drive research further.

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19

Rasaiah, B., C. Bellman, R. D. Hewson, S. D. Jones y T. J. Malthus. "ENHANCED DATA DISCOVERABILITY FOR IN SITU HYPERSPECTRAL DATASETS". ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-4 (3 de junio de 2016): 49–52. http://dx.doi.org/10.5194/isprsannals-iii-4-49-2016.

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Field spectroscopic metadata is a central component in the quality assurance, reliability, and discoverability of hyperspectral data and the products derived from it. Cataloguing, mining, and interoperability of these datasets rely upon the robustness of metadata protocols for field spectroscopy, and on the software architecture to support the exchange of these datasets. Currently no standard for in situ spectroscopy data or metadata protocols exist. This inhibits the effective sharing of growing volumes of in situ spectroscopy datasets, to exploit the benefits of integrating with the evolving range of data sharing platforms. A core metadataset for field spectroscopy was introduced by Rasaiah et al., (2011-2015) with extended support for specific applications. This paper presents a prototype model for an OGC and ISO compliant platform-independent metadata discovery service aligned to the specific requirements of field spectroscopy. In this study, a proof-of-concept metadata catalogue has been described and deployed in a cloud-based architecture as a demonstration of an operationalized field spectroscopy metadata standard and web-based discovery service.

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20

Rasaiah, B., C. Bellman, R. D. Hewson, S. D. Jones y T. J. Malthus. "ENHANCED DATA DISCOVERABILITY FOR IN SITU HYPERSPECTRAL DATASETS". ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-4 (3 de junio de 2016): 49–52. http://dx.doi.org/10.5194/isprs-annals-iii-4-49-2016.

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Field spectroscopic metadata is a central component in the quality assurance, reliability, and discoverability of hyperspectral data and the products derived from it. Cataloguing, mining, and interoperability of these datasets rely upon the robustness of metadata protocols for field spectroscopy, and on the software architecture to support the exchange of these datasets. Currently no standard for in situ spectroscopy data or metadata protocols exist. This inhibits the effective sharing of growing volumes of in situ spectroscopy datasets, to exploit the benefits of integrating with the evolving range of data sharing platforms. A core metadataset for field spectroscopy was introduced by Rasaiah et al., (2011-2015) with extended support for specific applications. This paper presents a prototype model for an OGC and ISO compliant platform-independent metadata discovery service aligned to the specific requirements of field spectroscopy. In this study, a proof-of-concept metadata catalogue has been described and deployed in a cloud-based architecture as a demonstration of an operationalized field spectroscopy metadata standard and web-based discovery service.

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21

Ivanov, Boris V., Pavel N. Sviashchennikov, Danila M. Zhuravskiy, Alexey K. Pavlov, Eirik J. Frland y Ketil Isaksen. "Sea ice metadata for Billefjorden and Grnfjorden, Svalbard". Czech Polar Reports 4, n.º 2 (1 de junio de 2014): 129–39. http://dx.doi.org/10.5817/cpr2014-2-13.

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Description of sea ice conditions in the fjords of Svalbard is crucial for sea transport as well as studies of local climate and climate change. Old observations from the Russian Hydrometeorological stations in the mining settlements Barentsburg (Grnfjorden) and Pyramiden (Billefjorden) have now been digitized. These visual and instrumental observations are archived in the State Archive of Arctic and Antarctic Research Institute (AARI) and Murmansk Branch of the Russian Hydrometeorological Service. In this paper, we bring an overview of the sea ice metadata with few examples of yearly changes in sea ice extent.

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22

Fong, J., H. K. Wong y S. M. Huang. "Continuous and incremental data mining association rules using frame metadata model". Knowledge-Based Systems 16, n.º 2 (marzo de 2003): 91–100. http://dx.doi.org/10.1016/s0950-7051(02)00076-x.

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23

Csurka, Gabriela y Katerina Pastra. "Introduction to the special issue on “metadata mining for image understanding”". Multimedia Tools and Applications 42, n.º 1 (12 de noviembre de 2008): 1–4. http://dx.doi.org/10.1007/s11042-008-0248-6.

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24

Li, Zhi Gang, Hui Liu y Wu Nian Yang. "Service-Oriented Sharing Architecture for Mining Area Spatial Information and Key Techniques". Advanced Materials Research 230-232 (mayo de 2011): 501–5. http://dx.doi.org/10.4028/www.scientific.net/amr.230-232.501.

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Mining spatial information sharing platform, as a new type of mining information management systems, will greatly enhance the level of the existing mine information management and their ability to support production operations. Based on the analysis of the current information sharing framework, a new mining information sharing platform which is service-oriented GIS is introduced. Then, this article describes three key techniques to achieve: the SOA-based GIS technology, metadata technology and spatial database technology. Finally, the paper talks about the research way for the development of mining area spatial information sharing architecture in the future.

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Tosaka, Yuji y Cathy Weng. "Reexamining Content-Enriched Access: Its Effect on Usage and Discovery". College & Research Libraries 72, n.º 5 (1 de septiembre de 2011): 412–27. http://dx.doi.org/10.5860/crl-137.

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Content-enriched metadata in bibliographic records is considered helpful to library users in identifying and selecting library materials for their needs. The paper presents a study, using circulation data from a medium-sized academic library, of the effect of content-enriched records on library materials usage. The study also examines OPAC search transactions of circulated items to learn how enriched metadata is used. The findings show that enhanced records were overall associated with higher circulation rates and that keyword search was the most frequently used search option directly associated with circulation. Contents data can play a key role in discovery. Libraries should continue to provide and exploit content-enriched metadata. The combination of optimal library system data mining capability, postsearching evaluation, and OPAC display are crucial to achieve content-enriched access.

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Sinclair, Lucas, Umer Z. Ijaz, Lars Juhl Jensen, Marco J. L. Coolen, Cecile Gubry-Rangin, Alica Chroňáková, Anastasis Oulas et al. "Seqenv: linking sequences to environments through text mining". PeerJ 4 (20 de diciembre de 2016): e2690. http://dx.doi.org/10.7717/peerj.2690.

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Understanding the distribution of taxa and associated traits across different environments is one of the central questions in microbial ecology. High-throughput sequencing (HTS) studies are presently generating huge volumes of data to address this biogeographical topic. However, these studies are often focused on specific environment types or processes leading to the production of individual, unconnected datasets. The large amounts of legacy sequence data with associated metadata that exist can be harnessed to better place the genetic information found in these surveys into a wider environmental context. Here we introduce a software program, seqenv, to carry out precisely such a task. It automatically performs similarity searches of short sequences against the “nt” nucleotide database provided by NCBI and, out of every hit, extracts–if it is available–the textual metadata field. After collecting all the isolation sources from all the search results, we run a text mining algorithm to identify and parse words that are associated with the Environmental Ontology (EnvO) controlled vocabulary. This, in turn, enables us to determine both in which environments individual sequences or taxa have previously been observed and, by weighted summation of those results, to summarize complete samples. We present two demonstrative applications of seqenv to a survey of ammonia oxidizing archaea as well as to a plankton paleome dataset from the Black Sea. These demonstrate the ability of the tool to reveal novel patterns in HTS and its utility in the fields of environmental source tracking, paleontology, and studies of microbial biogeography. To install seqenv, go to: https://github.com/xapple/seqenv.

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27

Chen Huayue. "A Novel Data Mining Metadata Constructing Algorithm based on Formal Logic DLRDM". Journal of Convergence Information Technology 7, n.º 11 (30 de junio de 2012): 132–40. http://dx.doi.org/10.4156/jcit.vol7.issue11.17.

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28

T. "Significant Term List Based Metadata Conceptual Mining Model for Effective Text Clustering". Journal of Computer Science 8, n.º 10 (1 de octubre de 2012): 1660–66. http://dx.doi.org/10.3844/jcssp.2012.1660.1666.

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29

Sun, Li, Li Guo y Huan Tian. "Research on Distributed Vertical Frequent Pattern Mining Method Based on Metadata Integration". Journal of Physics: Conference Series 1449 (enero de 2020): 012062. http://dx.doi.org/10.1088/1742-6596/1449/1/012062.

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30

Malon, D., J. Cranshaw y Q. Zhang. "An extensible infrastructure for querying and mining event-level metadata in ATLAS". Journal of Physics: Conference Series 396, n.º 5 (13 de diciembre de 2012): 052053. http://dx.doi.org/10.1088/1742-6596/396/5/052053.

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31

Guerrero, Juan I., Antonio García, Enrique Personal, Joaquín Luque y Carlos León. "Heterogeneous data source integration for smart grid ecosystems based on metadata mining". Expert Systems with Applications 79 (agosto de 2017): 254–68. http://dx.doi.org/10.1016/j.eswa.2017.03.007.

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32

Moreels, Dries. "Mining the databases of the Vlaams Theater Instituut". Art Libraries Journal 33, n.º 3 (2008): 39–43. http://dx.doi.org/10.1017/s0307472200015479.

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In 1987 the Vlaams Theater Instituut (VTi) was born, as a result of the need to support and identify the ambitions of a new generation of performing artists in Flanders and Brussels, to document and investigate the context of this turbulent but artistically exceptional period, and to develop appropriate policy instruments for this burgeoning practice. Twenty years on, and the artistic and social context has changed radically. Initiatives that were played out on the fringes ‘back then’ we now see right at the centre of things. Today the need to keep documenting, investigating and reflecting is just as relevant as it was in those days. Moreover, the VTi can now explore the value of 20 years of metadata creation in new ways.

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Jalal, Ahmed Adeeb. "ENGINEERING MINING A LARGE SCALE DATA BASED ON FEATURE ENGINEERING, METADATA, AND ONTOLOGIES". International Journal of Digital Information and Wireless Communications 6, n.º 4 (2016): 219–29. http://dx.doi.org/10.17781/p002091.

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Klavans, Judith L., Carolyn Sheffield, Eileen Abels, Jimmy Lin, Rebecca Passonneau, Tandeep Sidhu y Dagobert Soergel. "Computational linguistics for metadata building (CLiMB): using text mining for the automatic identification, categorization, and disambiguation of subject terms for image metadata". Multimedia Tools and Applications 42, n.º 1 (8 de noviembre de 2008): 115–38. http://dx.doi.org/10.1007/s11042-008-0253-9.

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Maiah, Lax, DR A. GOVARDHAN DR.A.GOVARDHAN y DR C. SUNIL KUMAR. "A FRAMEWORK FOR SPATIO-TEMPORAL DATA WAREHOUSE". INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 4, n.º 1 (1 de febrero de 2013): 146–50. http://dx.doi.org/10.24297/ijct.v4i1c.3114.

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Data Warehouse (DW) is topic-oriented, integrated, static datasets which are used to support decision-making. Driven by the constraint of mass spatio-temporal data management and application, Spatio-Temporal Data Warehouse (STDW) was put forward, and many researchers scattered all over the world focused their energy on it.Although the research on STDW is going in depth , there are still many key difficulties to be solved, such as the design principle, system framework, spatio-temporal data model (STDM), spatio-temporal data process (STDP), spatial data mining (SDM) and etc. In this paper, the concept of STDW is discussed, and analyzes the organization model of spatio-temporal data. Based on the above, a framework of STDW is composed of data layer, management layer and application layer. The functions of STDW should include data analysis besides data process and data storage. When users apply certain kind of data services, STDW identifies the right data by metadata management system, then start data processing tool to form a data product which serves the data mining and OLAP. All varieties of distributed databases (DDBs) make up data sources of STDW, including Digital Elevation Model (DEM), Diagnosis-Related Group (DRG), Data Locator Group (DLG), Data Objects Management (DOM), Place Name and other databases in existence. The management layer implements heterogeneous data processing, metadata management and spatio-temporal data storage. The application layer provides data products service, multidimensional data cube, data mining tools and on-line analytical process.

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Ivanova, Svetlana, Elena Sant’eva, Maxim Bakanov, Leszek Sobik y Leonid Lopukhinsky. "Integration of Environmental Information in a Mining Region Using a Geoportal". E3S Web of Conferences 278 (2021): 01013. http://dx.doi.org/10.1051/e3sconf/202127801013.

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At present, the complex nature of the impact on the ecosystem in regions with intensive mining creates a multidimensional information “plume” consisting of data on mineral reserves, the state of mining operations, accumulated, current and future environmental pollution. The transition to the lean use of the subsoil and the reasonable disposal of mining waste requires fundamentally new forms of environmental information accumulation and processing during designing new enterprises and regulating the activities of existing ones. The most promising form of information support for the greening of mining is a geoportal. It is a complex of software and technological support for working with spatial data. Its key task is to provide the users with tools and services for storing and cataloging, publishing and loading spatial and environmental data, searching and filtering by metadata, interactive web visualization, direct access to geodata based on map web services.

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Wang, Xiao Bin y Qing Jun Wang. "Study on Personalized Recommendation Technology of Digital TV Programs". Applied Mechanics and Materials 347-350 (agosto de 2013): 3035–38. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.3035.

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This paper aims at one of key technologies in digital television development ---intelligent personalized recommendation technology of digital TV programs for study. This paper proposes to take advantage of ample TV-Anytime to describe metadata so as to perform specific plans of guide service for TV programs based on TV-Anytime metadata specification. It combines technology such as data mining and artificial intelligence etc with a view of building a personalized TV program recommendation system on the framework of the multi-agent. Besides, a hybrid algorithm with content filtering and collaborative filtering based on the systematical recommendation algorithm has been put forward. In order to overcome the deficiencies of traditional collaborative filtering algorithm which relies on users explicit evaluation, the paper represents an improved algorithm with the footing of content collaborative filtering.

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Han, Jun, Yu Huang, Kuldeep Kumar y Sukanto Bhattacharya. "Time-Varying Dynamic Topic Model". Journal of Global Information Management 26, n.º 1 (enero de 2018): 104–19. http://dx.doi.org/10.4018/jgim.2018010106.

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In this paper the authors build on prior literature to develop an adaptive and time-varying metadata-enabled dynamic topic model (mDTM) and apply it to a large Weibo dataset using an online Gibbs sampler for parameter estimation. Their approach simultaneously captures the maximum number of inherent dynamic features of microblogs thereby setting it apart from other online document mining methods in the extant literature. In summary, the authors' results show a better performance of mDTM in terms of the quality of the mined information compared to prior research and showcases mDTM as a promising tool for the effective mining of microblogs in a rapidly changing global information space.

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D.A., Olubukola, Stephen O.M., Funmilayo A.K., Ayokunle O., Oyebola A., Oduroye A., Wumi A. y Yaw M. "Movie Success Prediction Using Data Mining". British Journal of Computer, Networking and Information Technology 4, n.º 2 (22 de septiembre de 2021): 22–30. http://dx.doi.org/10.52589/bjcnit-cqocirec.

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The movie industry is arguably one of the biggest entertainment sectors. Nollywood, the Nigerian movie industry produces tons of movies for public consumption, but only a few make it to box-office or end up becoming blockbusters. The introduction of movie success prediction can play an important role in the industry not only to predict movie success but to help directors and producers make better decisions for the purpose of profit. This study proposes a movie prediction model that applies data mining techniques and machine learning algorithms to predict the success or failure of an upcoming movie (based on predefined parameters). The parameters needed for predicting the success or failure of a movie include dataset needed for the process of data mining such as the historical data of actors, actresses, writers, directors, marketing and production budget, audience, location, release date, and competing movies on same release date. This model also helps movie consumers to determine a blockbuster, hit, success rating and quality of upcoming movies before deciding on a movie ticket. The data mining techniques was applied to Internet Movie Database MetaData which was initially passed through cleaning and integration process.

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Pika, Anastasiia, Moe T. Wynn, Stephanus Budiono, Arthur H. M. ter Hofstede, Wil M. P. van der Aalst y Hajo A. Reijers. "Privacy-Preserving Process Mining in Healthcare". International Journal of Environmental Research and Public Health 17, n.º 5 (2 de marzo de 2020): 1612. http://dx.doi.org/10.3390/ijerph17051612.

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Process mining has been successfully applied in the healthcare domain and has helped to uncover various insights for improving healthcare processes. While the benefits of process mining are widely acknowledged, many people rightfully have concerns about irresponsible uses of personal data. Healthcare information systems contain highly sensitive information and healthcare regulations often require protection of data privacy. The need to comply with strict privacy requirements may result in a decreased data utility for analysis. Until recently, data privacy issues did not get much attention in the process mining community; however, several privacy-preserving data transformation techniques have been proposed in the data mining community. Many similarities between data mining and process mining exist, but there are key differences that make privacy-preserving data mining techniques unsuitable to anonymise process data (without adaptations). In this article, we analyse data privacy and utility requirements for healthcare process data and assess the suitability of privacy-preserving data transformation methods to anonymise healthcare data. We demonstrate how some of these anonymisation methods affect various process mining results using three publicly available healthcare event logs. We describe a framework for privacy-preserving process mining that can support healthcare process mining analyses. We also advocate the recording of privacy metadata to capture information about privacy-preserving transformations performed on an event log.

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Ryberg, Martin, R. Henrik Nilsson, Erik Kristiansson, Mats Töpel, Stig Jacobsson y Ellen Larsson. "Mining metadata from unidentified ITS sequences in GenBank: A case study in Inocybe (Basidiomycota)". BMC Evolutionary Biology 8, n.º 1 (2008): 50. http://dx.doi.org/10.1186/1471-2148-8-50.

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42

Djordjevic, Djordje, Joshua Y. S. Tang, Yun Xin Chen, Shu Lun Shannon Kwan, Raymond W. K. Ling, Gordon Qian, Chelsea Y. Y. Woo, Samuel J. Ellis y Joshua W. K. Ho. "Discovery of perturbation gene targets via free text metadata mining in Gene Expression Omnibus". Computational Biology and Chemistry 80 (junio de 2019): 152–58. http://dx.doi.org/10.1016/j.compbiolchem.2019.03.014.

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Li, Yun, Yongyao Jiang, Juan Gu, Mingyue Lu, Manzhu Yu, Edward Armstrong, Thomas Huang et al. "A Cloud-Based Framework for Large-Scale Log Mining through Apache Spark and Elasticsearch". Applied Sciences 9, n.º 6 (16 de marzo de 2019): 1114. http://dx.doi.org/10.3390/app9061114.

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The volume, variety, and velocity of different data, e.g., simulation data, observation data, and social media data, are growing ever faster, posing grand challenges for data discovery. An increasing trend in data discovery is to mine hidden relationships among users and metadata from the web usage logs to support the data discovery process. Web usage log mining is the process of reconstructing sessions from raw logs and finding interesting patterns or implicit linkages. The mining results play an important role in improving quality of search-related components, e.g., ranking, query suggestion, and recommendation. While researches were done in the data discovery domain, collecting and analyzing logs efficiently remains a challenge because (1) the volume of web usage logs continues to grow as long as users access the data; (2) the dynamic volume of logs requires on-demand computing resources for mining tasks; (3) the mining process is compute-intensive and time-intensive. To speed up the mining process, we propose a cloud-based log-mining framework using Apache Spark and Elasticsearch. In addition, a data partition paradigm, logPartitioner, is designed to solve the data imbalance problem in data parallelism. As a proof of concept, oceanographic data search and access logs are chosen to validate performance of the proposed parallel log-mining framework.

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44

Shrestha, Sushil y Manish Pokharel. "Data Mining Applications Used in Education Sector". Journal of Education and Research 10, n.º 2 (6 de noviembre de 2020): 27–51. http://dx.doi.org/10.3126/jer.v10i2.32721.

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The purpose of this work is to study the usage trends of Data Mining (DM) methods in education. It discusses different data mining techniques used for different types of educational data. The related papers were initially selected from the metadata containing words like Online Learning (OL) and Educational Data Mining (EDM). The papers were then filtered on the basis of DM algorithms, the purpose of study, and the types of data used. The findings suggested that EDM is the most commonly used technique for the prediction of students’ academic success, and the most used purpose is classification, followed by clustering and association. Further, this research also contains the study conducted on moodle data to find anomalies. K-means clustering was applied to find the optimal number of clusters on moodle data that consists of log and quiz dataset. The growth in the number of Internet users has increased learning through the online process. Hence, several activities are performed in OL systems, which generate a massive amount of data to be analysed to obtain useful information. Therefore, this type of research is very beneficial to academicians and instructors to identify the learner’s behaviors and develop suitable models.

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45

Barbosa, Flávio, Arthur Vidal y Flávio Mello. "Machine Learning for Cryptographic Algorithm Identification". Journal of Information Security and Cryptography (Enigma) 3, n.º 1 (3 de septiembre de 2016): 3. http://dx.doi.org/10.17648/enig.v3i1.55.

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This paper aims to study encrypted text files in order to identify their encoding algorithm. Plain texts were encoded with distinct cryptographic algorithms and then some metadata were extracted from these codifications. Afterward, the algorithm identification is obtained by using data mining techniques. Firstly, texts in Portuguese, English and Spanish were encrypted using DES, Blowfish, RSA, and RC4 algorithms. Secondly, the encrypted files were submitted to data mining techniques such as J48, FT, PART, Complement Naive Bayes, and Multilayer Perceptron classifiers. Charts were created using the confusion matrices generated in step two and it was possible to perceive that the percentage of identification for each of the algorithms is greater than a probabilistic bid. There are several scenarios where algorithm identification reaches almost 97, 23% of correctness.

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Alshameri, Faleh y Abdul Karim Bangura. "Generating metadata to study and teach about African issues". Information Technology & People 27, n.º 3 (29 de julio de 2014): 341–65. http://dx.doi.org/10.1108/itp-06-2013-0112.

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Purpose – After almost three centuries of employing western educational approaches, many African societies are still characterized by low western literacy rates, civil conflicts, and underdevelopment. It is obvious that these western educational paradigms, which are not indigenous to Africans, have done relatively little good for Africans. Thus, the purpose of this paper is to argue that the salvation for Africans hinges upon employing indigenous African educational paradigms which can be subsumed under the rubric of ubuntugogy, which the authors define as the art and science of teaching and learning undergirded by humanity toward others. Design/methodology/approach – Therefore, ubuntugogy transcends pedagogy (the art and science of teaching), andragogy (the art and science of helping adults learn), ergonagy (the art and science of helping people learn to work), and heutagogy (the study of self-determined learning). That many great African minds, realizing the debilitating effects of the western educational systems that have been forced upon Africans, have called for different approaches. Findings – One of the biggest challenges for studying and teaching about Africa in Africa at the higher education level, however, is the paucity of published material. Automated generation of metadata is one way of mining massive data sets to compensate for this shortcoming. Originality/value – Thus, the authors address the following major research question in this paper: What is automated generation of metadata and how can the technique be employed from an African-centered perspective? After addressing this question, conclusions and recommendations are offered.

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Algur, Siddu P. y Prashant Bhat. "Web Video Object Mining: Expectation Maximization and Density Based Clustering of Web Video Metadata Objects". International Journal of Information Engineering and Electronic Business 8, n.º 1 (8 de enero de 2016): 69–77. http://dx.doi.org/10.5815/ijieeb.2016.01.08.

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48

Yang, Haishui, Yajun Dai, Mingmin Xu, Qian Zhang, Xinmin Bian, Jianjun Tang y Xin Chen. "Metadata-mining of 18S rDNA sequences reveals that “everything is not everywhere” for glomeromycotan fungi". Annals of Microbiology 66, n.º 1 (25 de junio de 2015): 361–71. http://dx.doi.org/10.1007/s13213-015-1116-z.

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Wang, Jiangping. "Extracting Value from Unstructured Data – Implementing Text Analytics on the Voice of Student". Transactions on Machine Learning and Artificial Intelligence 8, n.º 4 (1 de agosto de 2020): 14–22. http://dx.doi.org/10.14738/tmlai.84.8456.

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Unstructured data is chaotic and messy with little or no metadata and lacks of traditional organization structure. However, same as any structured data, unstructured data is also part of valuable business asset. Many times, it is text heavy and needs extensive preprocessing before data mining algorithm can apply for building models in order to reveal value hidden in the data. Text as a form of data is widely used in business operations as a major way of communication, generating increasing volumes of data. Text data in its raw form is relatively dirty. The embedded business value can be extracted through approaches in text mining and text analytics. This paper presents a case study in this general process of revealing value in unstructured data and applying on data collected to support online learning and student assistance.

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Forghani, M. y F. Karimipour. "EXTRACTING HUMAN BEHAVIORAL PATTERNS BY MINING GEO-SOCIAL NETWORKS". ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-2/W3 (22 de octubre de 2014): 115–20. http://dx.doi.org/10.5194/isprsarchives-xl-2-w3-115-2014.

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Accessibility of positioning technologies such as GPS offer the opportunity to store one’s travel experience and publish it on the web. Using this feature in web-based social networks and considering location information shared by users as a bridge connecting the users’ network to location information layer leads to the formation of Geo-Social Networks. The availability of large amounts of geographical and social data on these networks provides rich sources of information that can be utilized for studying human behavior through data analysis in a spatial-temporal-social context. This paper attempts to investigate the behavior of around 1150 users of Foursquare network by making use of their check-ins. The authors analyzed the metadata associated with the whereabouts of the users, with an emphasis on the type of places, to uncover patterns across different temporal and geographical scales for venue category usage. The authors found five groups of meaningful patterns that can explore region characteristics and recognize a number of major crowd behaviors that recur over time and space.

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