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Relevant bibliographies by topics / Metadata Features

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Academic literature on the topic 'Metadata Features'

Author: Grafiati

Published: 25 June 2021

Last updated: 12 February 2022

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Journal articles on the topic "Metadata Features"

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Odier, Jérôme, Fabian Lambert, and Jérôme Fulachier. "The ATLAS Metadata Interface (AMI) 2.0 metadata ecosystem: new design principles and features." EPJ Web of Conferences 214 (2019): 05046. http://dx.doi.org/10.1051/epjconf/201921405046.

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ATLAS Metadata Interface (AMI) is a generic ecosystem for metadata aggregation, transformation and cataloging. Benefiting from 18 years of feedback in the LHC context, the second major version was recently released. This paper describes the design choices and their benefits for providing high-level metadata-dedicated features. In particular, the Metadata Querying Language (MQL) - a domain-specific language allowing to query databases without knowing the relation between entities - and on the AMI Web framework are described.

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Li, Yaping. "Glowworm Swarm Optimization Algorithm- and K-Prototypes Algorithm-Based Metadata Tree Clustering." Mathematical Problems in Engineering 2021 (February 9, 2021): 1–10. http://dx.doi.org/10.1155/2021/8690418.

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The main objective of this paper is to present a new clustering algorithm for metadata trees based on K-prototypes algorithm, GSO (glowworm swarm optimization) algorithm, and maximal frequent path (MFP). Metadata tree clustering includes computing the feature vector of the metadata tree and the feature vector clustering. Therefore, traditional data clustering methods are not suitable directly for metadata trees. As the main method to calculate eigenvectors, the MFP method also faces the difficulties of high computational complexity and loss of key information. Generally, the K-prototypes algorithm is suitable for clustering of mixed-attribute data such as feature vectors, but the K-prototypes algorithm is sensitive to the initial clustering center. Compared with other swarm intelligence algorithms, the GSO algorithm has more efficient global search advantages, which are suitable for solving multimodal problems and also useful to optimize the K-prototypes algorithm. To address the clustering of metadata tree structures in terms of clustering accuracy and high data dimension, this paper combines the GSO algorithm, K-prototypes algorithm, and MFP together to study and design a new metadata structure clustering method. Firstly, MFP is used to describe metadata tree features, and the key parameter of categorical data is introduced into the feature vector of MFP to improve the accuracy of the feature vector to describe the metadata tree; secondly, GSO is combined with K-prototypes to design GSOKP for clustering the feature vector that contains numeric data and categorical data so as to improve the clustering accuracy; finally, tests are conducted with a set of metadata trees. The experimental results show that the designed metadata tree clustering method GSOKP-FP has certain advantages in respect to clustering accuracy and time complexity.

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SCHERP, ANSGAR, CARSTEN SAATHOFF, and STEFAN SCHEGLMANN. "A PATTERN SYSTEM FOR DESCRIBING THE SEMANTICS OF STRUCTURED MULTIMEDIA DOCUMENTS." International Journal of Semantic Computing 06, no. 03 (2012): 263–88. http://dx.doi.org/10.1142/s1793351x12400089.

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Today's metadata models and metadata standards often focus on a specific media type only, lack combinability with other metadata models, or are limited with respect to the features they support. Thus they are not sufficient to describe the semantics of rich, structured multimedia documents. To overcome these limitations, we have developed a comprehensive model for representing multimedia metadata, the Multimedia Metadata Ontology (M3O). The M3O has been developed by an extensive analysis of related work and abstracts from the features of existing metadata models and metadata standards. It is based on the foundational ontology DOLCE+DnS Ultralight and makes use of ontology design patterns. The M3O serves as generic modeling framework for integrating the existing metadata models and metadata standards rather than replacing them. As such, the M3O can be used internally as semantic data model within complex multimedia applications such as authoring tools or multimedia management systems. To make use of the M3O in concrete multimedia applications, a generic application programming interface (API) has been implemented based on a sophisticated persistence layer that provides explicit support for ontology design patterns. To demonstrate applicability of the M3O API, we have integrated and applied it with our SemanticMM4U framework for the multi-channel generation of semantically annotated multimedia documents.

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Rastogi, Ajay, Monica Mehrotra, and Syed Shafat Ali. "Effective Opinion Spam Detection: A Study on Review Metadata Versus Content." Journal of Data and Information Science 5, no. 2 (2020): 76–110. http://dx.doi.org/10.2478/jdis-2020-0013.

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AbstractPurposeThis paper aims to analyze the effectiveness of two major types of features—metadata-based (behavioral) and content-based (textual)—in opinion spam detection.Design/methodology/approachBased on spam-detection perspectives, our approach works in three settings: review-centric (spam detection), reviewer-centric (spammer detection) and product-centric (spam-targeted product detection). Besides this, to negate any kind of classifier-bias, we employ four classifiers to get a better and unbiased reflection of the obtained results. In addition, we have proposed a new set of features which are compared against some well-known related works. The experiments performed on two real-world datasets show the effectiveness of different features in opinion spam detection.FindingsOur findings indicate that behavioral features are more efficient as well as effective than the textual to detect opinion spam across all three settings. In addition, models trained on hybrid features produce results quite similar to those trained on behavioral features than on the textual, further establishing the superiority of behavioral features as dominating indicators of opinion spam. The features used in this work provide improvement over existing features utilized in other related works. Furthermore, the computation time analysis for feature extraction phase shows the better cost efficiency of behavioral features over the textual.Research limitationsThe analyses conducted in this paper are solely limited to two well-known datasets, viz., YelpZip and YelpNYC of Yelp.com.Practical implicationsThe results obtained in this paper can be used to improve the detection of opinion spam, wherein the researchers may work on improving and developing feature engineering and selection techniques focused more on metadata information.Originality/valueTo the best of our knowledge, this study is the first of its kind which considers three perspectives (review, reviewer and product-centric) and four classifiers to analyze the effectiveness of opinion spam detection using two major types of features. This study also introduces some novel features, which help to improve the performance of opinion spam detection methods.

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Li, Chunqiu, and Shigeo Sugimoto. "Provenance description of metadata application profiles for long-term maintenance of metadata schemas." Journal of Documentation 74, no. 1 (2018): 36–61. http://dx.doi.org/10.1108/jd-03-2017-0042.

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Purpose Provenance information is crucial for consistent maintenance of metadata schemas over time. The purpose of this paper is to propose a provenance model named DSP-PROV to keep track of structural changes of metadata schemas. Design/methodology/approach The DSP-PROV model is developed through applying the general provenance description standard PROV of the World Wide Web Consortium to the Dublin Core Application Profile. Metadata Application Profile of Digital Public Library of America is selected as a case study to apply the DSP-PROV model. Finally, this paper evaluates the proposed model by comparison between formal provenance description in DSP-PROV and semi-formal change log description in English. Findings Formal provenance description in the DSP-PROV model has advantages over semi-formal provenance description in English to keep metadata schemas consistent over time. Research limitations/implications The DSP-PROV model is applicable to keep track of the structural changes of metadata schema over time. Provenance description of other features of metadata schema such as vocabulary and encoding syntax are not covered. Originality/value This study proposes a simple model for provenance description of structural features of metadata schemas based on a few standards widely accepted on the Web and shows the advantage of the proposed model to conventional semi-formal provenance description.

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Kim, Jihyeok, Reinald Kim Amplayo, Kyungjae Lee, Sua Sung, Minji Seo, and Seung-won Hwang. "Categorical Metadata Representation for Customized Text Classification." Transactions of the Association for Computational Linguistics 7 (November 2019): 201–15. http://dx.doi.org/10.1162/tacl_a_00263.

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The performance of text classification has improved tremendously using intelligently engineered neural-based models, especially those injecting categorical metadata as additional information, e.g., using user/product information for sentiment classification. This information has been used to modify parts of the model (e.g., word embeddings, attention mechanisms) such that results can be customized according to the metadata. We observe that current representation methods for categorical metadata, which are devised for human consumption, are not as effective as claimed in popular classification methods, outperformed even by simple concatenation of categorical features in the final layer of the sentence encoder. We conjecture that categorical features are harder to represent for machine use, as available context only indirectly describes the category, and even such context is often scarce (for tail category). To this end, we propose using basis vectors to effectively incorporate categorical metadata on various parts of a neural-based model. This additionally decreases the number of parameters dramatically, especially when the number of categorical features is large. Extensive experiments on various data sets with different properties are performed and show that through our method, we can represent categorical metadata more effectively to customize parts of the model, including unexplored ones, and increase the performance of the model greatly.

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Li, Fang, and Jie Zhang. "Case study: a metadata scheme for multi-type manuscripts for the T.D. Lee Archives Online." Library Hi Tech 32, no. 2 (2014): 219–28. http://dx.doi.org/10.1108/lht-11-2013-0149.

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Purpose – The purpose of this paper is to propose a solution for designing a metadata scheme for multi-type manuscripts based on a comparison of various existing metadata schemes. Design/methodology/approach – The diversity of manuscript types is analysed. Descriptive scheme based on machine-readable MARC and metadata specifications-based descriptive scheme are compared. User tasks and resource features are analysed. Several challenges are posed and resolved through the design and establishment of a metadata scheme for the T.D. Lee Archives Online. Findings – Clarify an approach for developing a metadata scheme for multi-type manuscripts. Originality/value – From a multi-type perspective, this study designs a metadata scheme, establishes the element set and expands elements by studying a typical practice case. Useful suggestions for libraries, archives and museums are provided.

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Gong, Minseo, Jae-Yoon Cheon, Young-Suk Park, Jeawon Park, and Jaehyun Choi. "User Musical Taste Prediction Technique Using Music Metadata and Features." International Journal of Multimedia and Ubiquitous Engineering 11, no. 8 (2016): 163–70. http://dx.doi.org/10.14257/ijmue.2016.11.8.18.

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Ahmed, Muhammad Waqas, and Muhammad Tanvir Afzal. "FLAG-PDFe: Features Oriented Metadata Extraction Framework for Scientific Publications." IEEE Access 8 (2020): 99458–69. http://dx.doi.org/10.1109/access.2020.2997907.

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Tali, Dmitry, and Oleg Finko. "Cryptographic Recursive Control of Integrity of Metadata Electronic Documents. Part 2. Complex of Algorithms." Voprosy kiberbezopasnosti, no. 6(40) (2020): 32–47. http://dx.doi.org/10.21681/2311-3456-2020-06-32-47.

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The purpose of the study is to develop a set of algorithms to increase the level of security of metadata of electronic documents in conditions of destructive influences from authorized users (insiders). Research methods: the principle of chain data recording technology, methods of the theory of algorithms, theoretical provisions for the construction of automated information systems of legally significant electronic document management. The result of the research: a complex of algorithms for cryptographic recursive 2-D control of the integrity of metadata of electronic documents has been developed. Its feature is the following features: 1. localization of modified (with signs of integrity violation) metadata records of electronic documents; 2. identification of authorized users (insiders) who have carried out unauthorized modifications to the metadata of electronic documents; 3. identification of the fact of collusion of trusted parties through the introduction of mutual control of the results of their actions. The proposed solution allows to implement the functions of cryptographic recursive two-dimensional control of the integrity of metadata of electronic documents. At the same time, the use of the technology of chain data recording, at the heart of the presented solution, is due to the peculiarities of the functioning of departmental automated information systems of electronic document management.

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Dissertations / Theses on the topic "Metadata Features"

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Bogdanov, Dmitry. "From music similarity to music recommendation : computational approaches based on audio features and metadata." Doctoral thesis, Universitat Pompeu Fabra, 2013. http://hdl.handle.net/10803/123776.

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Aquest treball es centra en el modelatge d'usuari per la recomanació musical i desenvolupa algoritmes per la comprensió automàtica i visualització de preferències musicals. Primer, es proposa un model d'usuari construït a partir d'un conjunt de peces musicals. En segon lloc, s'estudien mètodes d’estimació de similitud musical, treballant exclusivament en el contingut d'àudio. Es proposen noves mètriques basades en la informació tímbrica, temporal, tonal i semàntica. En tercer lloc, es proposen diversos mètodes de recomanació musical que utilitzen aquestes mètriques i que milloren amb un filtratge addicional basat en metadades. També es proposa un mètode senzill basat en metadades editorials. En quart lloc, es presenten els predictors de preferència rellevants a nivell acústic i semàntic. Finalment, es presenta un mètode de visualització de preferències que millora l'experiència d'usuari en sistemes de recomanació.<br>In this work we focus on user modeling for music recommendation and develop algorithms for computational understanding and visualization of music preferences. Firstly, we propose a user model starting from an explicit set of music tracks provided by the user as evidence of his/her preferences. Secondly, we study approaches to music similarity, working solely on audio content and propose a number of novel measures working with timbral, temporal, tonal, and semantic information about music. Thirdly, we propose distance-based and probabilistic recommendation approaches working with explicitly given preference examples. We employ content-based music similarity measures and propose filtering by metadata to improve results of purely content-based recommenders. Moreover, we propose a lightweight approach working exclusively on editorial metadata. Fourthly, we demonstrate important predictors of preference from both acoustical and semantic perspectives. Finally, we demonstrate a preference visualization approach which allows to enhance user experience in recommender systems.

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Gängler, Thomas. "Metadaten und Merkmale zur Verwaltung von persönlichen Musiksammlungen." Thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2011. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-72442.

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Roxbergh, Linus. "Language Classification of Music Using Metadata." Thesis, Uppsala universitet, Avdelningen för systemteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-379625.

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The purpose of this study was to investigate how metadata from Spotify could be used to identify the language of songs in a dataset containing nine languages. Features based on song name, album name, genre, regional popularity and vectors describing songs, playlists and users were analysed individually and in combination with each other in different classifiers. In addition to this, this report explored how different levels of prediction confidence affects performance and how it compared to a classifier based on audio input. A random forest classifier proved to have the best performance with an accuracy of 95.4% for the whole data set. Performance was also investigated when the confidence of the model was taken into account, and when only keeping more confident predictions from the model, accuracy was higher. When keeping the 70% most confident predictions an accuracy of 99.4% was achieved. The model also proved to be robust to input of other languages than it was trained on, and managed to filter out unwanted records not matching the languages of the model. A comparison was made to a classifier based on audio input, where the model using metadata performed better on the training and test set used. Finally, a number of possible improvements and future work were suggested.

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Gajová, Veronika. "Automatické třídění fotografií podle obsahu." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2012. http://www.nusl.cz/ntk/nusl-236565.

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Purpose of this thesis is to design and implement a tool for automatic categorization of photos. The proposed tool is based on the Bag of Words classification method and it is realized as a plug-in for the XnView image viewer. The plug-in is able to classify a selected group of photos into predefined image categories. Subsequent notation of image categories is written directly into IPTC metadata of the picture as a keyword.

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Borggren, Lukas. "Automatic Categorization of News Articles With Contextualized Language Models." Thesis, Linköpings universitet, Artificiell intelligens och integrerade datorsystem, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-177004.

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This thesis investigates how pre-trained contextualized language models can be adapted for multi-label text classification of Swedish news articles. Various classifiers are built on pre-trained BERT and ELECTRA models, exploring global and local classifier approaches. Furthermore, the effects of domain specialization, using additional metadata features and model compression are investigated. Several hundred thousand news articles are gathered to create unlabeled and labeled datasets for pre-training and fine-tuning, respectively. The findings show that a local classifier approach is superior to a global classifier approach and that BERT outperforms ELECTRA significantly. Notably, a baseline classifier built on SVMs yields competitive performance. The effect of further in-domain pre-training varies; ELECTRA’s performance improves while BERT’s is largely unaffected. It is found that utilizing metadata features in combination with text representations improves performance. Both BERT and ELECTRA exhibit robustness to quantization and pruning, allowing model sizes to be cut in half without any performance loss.

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Kovach, Bob. "Next Generation Feature Roadmap for IP-Based Range Architectures." International Foundation for Telemetering, 2015. http://hdl.handle.net/10150/596390.

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ITC/USA 2015 Conference Proceedings / The Fifty-First Annual International Telemetering Conference and Technical Exhibition / October 26-29, 2015 / Bally's Hotel & Convention Center, Las Vegas, NV<br>The initial efforts that resulted in the migration of range application traffic to an IP infrastructure largely focused on the challenge of obtaining reliable transport for range application streams including telemetry and digital video via IP packet-based network technology. With the emergence of architectural elements that support robust Quality of Service, multicast routing, and redundant operation, these problems have largely been resolved, and a large number of ranges are now successfully utilizing IP-based network topology to implement their backbone transport infrastructure. The attention now turns to the need to provide supplemental features that provide enhanced functionality in addition to raw stream transport. These features include: *Stream monitoring and native test capability, usually called Service Assurance *Extended support for Ancillary Data / Metadata *Archive and Media Asset Management integration into the workflow *Temporal alignment of application streams This paper will describe a number of methods to implement these features utilizing an approach that leverages the features offered by IP-based technology, emphasizes the use of standards-based COTS implementations, and supports interworking between features.

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"Leveraging Metadata for Extracting Robust Multi-Variate Temporal Features." Master's thesis, 2013. http://hdl.handle.net/2286/R.I.18794.

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abstract: In recent years, there are increasing numbers of applications that use multi-variate time series data where multiple uni-variate time series coexist. However, there is a lack of systematic of multi-variate time series. This thesis focuses on (a) defining a simplified inter-related multi-variate time series (IMTS) model and (b) developing robust multi-variate temporal (RMT) feature extraction algorithm that can be used for locating, filtering, and describing salient features in multi-variate time series data sets. The proposed RMT feature can also be used for supporting multiple analysis tasks, such as visualization, segmentation, and searching / retrieving based on multi-variate time series similarities. Experiments confirm that the proposed feature extraction algorithm is highly efficient and effective in identifying robust multi-scale temporal features of multi-variate time series.<br>Dissertation/Thesis<br>M.S. Computer Science 2013

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Gängler, Thomas. "Metadaten und Merkmale zur Verwaltung von persönlichen Musiksammlungen." Thesis, 2009. https://tud.qucosa.de/id/qucosa%3A25671.

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Feng, Kuan-Jen, and 馮冠仁. "An Efficient Hierarchical Metadata Classifier based on SVM and Feature Selection Methods." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/71704686561687980607.

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碩士<br>國立暨南國際大學<br>資訊工程學系<br>94<br>Constructing a Web portal via integrating different contents from various information systems is crucial for providing public, popular and friendly services. In this thesis, we propose a hierarchical classifier system toward to fusing heterogeneous categories from various information systems. Employing traditional text classification methods that classify documents into predefined categories to deal with the problem is a possible solution. However, traditional methods suffer from drawbacks of huge text features and flat classification without considering hierarchical structures. Feature selection methods tend to select features from large-sized classes so that the classification performance for small-sized classes is poor. Flat classification regards hierarchical classes as flat-structured classes. In this way, each category corresponds to a single classifier that tends to select features to distinguish the class from all of remains. Therefore, discriminative features are hard to be effectively selected since the hierarchical knowledge is not applied to enhance the classification task. To deal with above problems, we propose feature selection methods to avoid the process being dominated by large-size classes. Based on the SVM classification method, we propose a hierarchical classification method to support classifications on hierarchical portal objects with metadata. We also employ domain concept hierarchies as the background knowledge to improve feature selection and classification processes by using the portal’s hierarchical knowledge. The NMNS portal is used as the test bed. Experiments show that our hierarchical classifier, with outstanding 98.5% F-measure, is more efficient than traditional flat classifier.

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Mohamed, Ghouse S. M. Z. S. "Modeling spatial variation of data quality in databases." 2008. http://repository.unimelb.edu.au/10187/3544.

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The spatial data community relies on the quality of its data. This research investigates new ways of storing and retrieving spatial data quality information in databases. Given the importance of features and sub-feature variation, three different data quality models of spatial variation in quality have been identified and defined: per-feature, feature-independent and feature-hybrid. Quality information is stored against each feature in the per-feature model. In the feature-independent model, quality information is independent of the feature. The feature-hybrid is derived from a combination of the other two models. In general, each model of spatial variation is different in its representational and querying capabilities. However, no model is entirely superior in storing and retrieving spatially varying quality. Hence, an integrated data model called as RDBMS for Spatial Variation in Quality (RSVQ) was developed by integrating per-feature, feature-independent and feature-hybrid data quality models. The RSVQ data model provides flexible representation of SDQ, which can be stored alongside individual features or parts of features in the database, or as an independent spatial data layer.<br>The thesis reports on how Oracle 10g spatial RDBMS was used to implement this model. An investigation into the different querying mechanisms resulted in the development of a new WITHQUALITY keyword as an extension to SQL. The WITHQUALITY keyword has been designed in such a way that it can perform automatic query optimization, which leads to faster retrieval of quality when compared to existing query mechanism. A user interface was built using Oracle Forms 10g which enables the user to perform single and multiple queries in addition to conversion between models (example, per-feature to feature-independent). The evaluation, which includes an industry case study, shows how these techniques can improve the spatial data community’s ability to represent and record data quality information.

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Book chapters on the topic "Metadata Features"

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Zinke-Wehlmann, Christian, Amit Kirschenbaum, Raul Palma, et al. "Linked Data and Metadata." In Big Data in Bioeconomy. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71069-9_7.

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AbstractData is the basis for creating information and knowledge. Having data in a structured and machine-readable format facilitates the processing and analysis of the data. Moreover, metadata—data about the data, can help discovering data based on features as, e.g., by whom they were created, when, or for which purpose. These associated features make the data more interpretable and assist in turning it into useful information. This chapter briefly introduces the concepts of metadata and Linked Data—highly structured and interlinked data, their standards and their usages, with some elaboration on the role of Linked Data in bioeconomy.

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Sawadogo, Pegdwendé N., Étienne Scholly, Cécile Favre, Éric Ferey, Sabine Loudcher, and Jérôme Darmont. "Metadata Systems for Data Lakes: Models and Features." In Communications in Computer and Information Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30278-8_43.

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Yadav, Asmita. "Bug Assignment-Utilization of Metadata Features Along with Feature Selection and Classifiers." In Lecture Notes in Electrical Engineering. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3067-5_7.

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Adler, B. Thomas, Luca de Alfaro, Santiago M. Mola-Velasco, Paolo Rosso, and Andrew G. West. "Wikipedia Vandalism Detection: Combining Natural Language, Metadata, and Reputation Features." In Computational Linguistics and Intelligent Text Processing. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19437-5_23.

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Wen, Jingran, Ramkiran Gouripeddi, and Julio C. Facelli. "Metadata Discovery of Heterogeneous Biomedical Datasets Using Token-Based Features." In IT Convergence and Security 2017. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6451-7_8.

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Hirota, Masaharu, Shohei Yokoyama, Naoki Fukuta, and Hiroshi Ishikawa. "Constraint-Based Clustering of Image Search Results Using Photo Metadata and Low-Level Image Features." In Computer and Information Science 2010. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15405-8_14.

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Elhadad, Mohamed K., Kin Fun Li, and Fayez Gebali. "A Novel Approach for Selecting Hybrid Features from Online News Textual Metadata for Fake News Detection." In Advances on P2P, Parallel, Grid, Cloud and Internet Computing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-33509-0_86.

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Consoli, Sergio, Luca Tiozzo Pezzoli, and Elisa Tosetti. "Using the GDELT Dataset to Analyse the Italian Sovereign Bond Market." In Machine Learning, Optimization, and Data Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-64583-0_18.

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AbstractThe Global Data on Events, Location, and Tone (GDELT) is a real time large scale database of global human society for open research which monitors worlds broadcast, print, and web news, creating a free open platform for computing on the entire world’s media. In this work, we first describe a data crawler, which collects metadata of the GDELT database in real-time and stores them in a big data management system based on Elasticsearch, a popular and efficient search engine relying on the Lucene library. Then, by exploiting and engineering the detailed information of each news encoded in GDELT, we build indicators capturing investor’s emotions which are useful to analyse the sovereign bond market in Italy. By using regression analysis and by exploiting the power of Gradient Boosting models from machine learning, we find that the features extracted from GDELT improve the forecast of country government yield spread, relative that of a baseline regression where only conventional regressors are included. The improvement in the fitting is particularly relevant during the period government crisis in May-December 2018.

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Rajaram, Gangothri, and K. R. Manjula. "Multi-standard Schema-Based Classification of Geospatial Metadata in Spatial Data Infrastructures Using Feature Weight Induced Probabilistic Learning Scheme." In Lecture Notes in Networks and Systems. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1941-0_66.

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"Metadata and Features." In Cultural Analytics. The MIT Press, 2020. http://dx.doi.org/10.7551/mitpress/11214.003.0011.

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Conference papers on the topic "Metadata Features"

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Gollapalli, Sujatha Das, Yanjun Qi, Prasenjit Mitra, and C. Lee Giles. "Extracting Researcher Metadata with Labeled Features." In Proceedings of the 2014 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics, 2014. http://dx.doi.org/10.1137/1.9781611973440.85.

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"Combining Visual and Text Features for Learning in Multimedia Direct Marketing Domain." In International Workshop on Metadata Mining for Image Understanding. SciTePress - Science and and Technology Publications, 2008. http://dx.doi.org/10.5220/0002337200340047.

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Smutz, Charles, and Angelos Stavrou. "Malicious PDF detection using metadata and structural features." In the 28th Annual Computer Security Applications Conference. ACM Press, 2012. http://dx.doi.org/10.1145/2420950.2420987.

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Safadi, Bahjat, Philippe Mulhem, Georges Quenot, and Jean-Pierre Chevallet. "Lifelog Semantic Annotation using deep visual features and metadata-derived descriptors." In 2016 14th International Workshop on Content-Based Multimedia Indexing (CBMI). IEEE, 2016. http://dx.doi.org/10.1109/cbmi.2016.7500247.

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Jony, Rabiul Islam, Alan Woodley, and Dimitri Perrin. "Flood Detection in Social Media Images using Visual Features and Metadata." In 2019 Digital Image Computing: Techniques and Applications (DICTA). IEEE, 2019. http://dx.doi.org/10.1109/dicta47822.2019.8946007.

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Wang, Xiaolan, K. Selcuk Candan, and Maria Luisa Sapino. "Leveraging metadata for identifying local, robust multi-variate temporal (RMT) features." In 2014 IEEE 30th International Conference on Data Engineering (ICDE). IEEE, 2014. http://dx.doi.org/10.1109/icde.2014.6816667.

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Leung, John, Igor Griva, and William Kennedy. "Using Affective Features from Media Content Metadata for Better Movie Recommendations." In 12th International Conference on Knowledge Discovery and Information Retrieval. SCITEPRESS - Science and Technology Publications, 2020. http://dx.doi.org/10.5220/0010056201610168.

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Korovesis, Konstantinos, Georgios Alexandridis, George Caridakis, Pavlos Polydoras, and Panagiotis Tsantilas. "Leveraging aspect-based sentiment prediction with textual features and document metadata." In SETN 2020: 11th Hellenic Conference on Artificial Intelligence. ACM, 2020. http://dx.doi.org/10.1145/3411408.3411433.

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Jony, Rabiul Islam, Alan Woodley, and Dimitri Perrin. "Fusing Visual Features and Metadata to Detect Flooding in Flickr Images." In 2020 Digital Image Computing: Techniques and Applications (DICTA). IEEE, 2020. http://dx.doi.org/10.1109/dicta51227.2020.9363418.

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Leung, John, Igor Griva, and William Kennedy. "Using Affective Features from Media Content Metadata for Better Movie Recommendations." In 12th International Conference on Knowledge Discovery and Information Retrieval. SCITEPRESS - Science and Technology Publications, 2020. http://dx.doi.org/10.5220/0010056201550162.

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