Relevant bibliographies by topics / Structured data

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Structured data'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Structured data"

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Kavita, Ahuja, and N.N.Jani. "A STUDY OF TRADITIONAL DATA ANALYSIS AND SENSOR DATA ANALYTICS." International Journal of Information Sciences and Techniques (IJIST) 6, no. 1/2 (2016): 185–90. https://doi.org/10.5281/zenodo.7743171.

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The growth of smart and intelligent devices known as sensors generate large amount of data. These generated data over a time span takes such a large volume which is designated as big data. The data structure of repository holds unstructured data. The traditional data analytics methods well developed and used widely to analyze structured data and to limit extend the semi-structured data which involves additional processing over heads. The similar methods used to analyze unstructured data are different because of distributed computing approach where as there is a possibility of centralized proce
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Rath, Shonal, and Hardik Agrawal. "Decentralized Computational Structure for the Selection of Dispersed and Non - Structured Data Proposal." International Journal of Science and Research (IJSR) 10, no. 9 (2021): 815–21. https://doi.org/10.21275/sr21916144223.

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Li, Shujie, Liang Li, Ruiying Geng, et al. "Unifying Structured Data as Graph for Data-to-Text Pre-Training." Transactions of the Association for Computational Linguistics 12 (2024): 210–28. http://dx.doi.org/10.1162/tacl_a_00641.

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Abstract Data-to-text (D2T) generation aims to transform structured data into natural language text. Data-to-text pre-training has proved to be powerful in enhancing D2T generation and yields impressive performance. However, previous pre-training methods either oversimplified structured data into a sequence without considering input structures or designed training objectives tailored for a specific data structure (e.g., table or knowledge graph). In this paper, we unify different types of structured data (i.e., table, key-value data, knowledge graph) into the graph format and cast different D2
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Fasihuddin, Mirza. "Integrating with Various Data Sources and Formats, Including Structured, Semi-Structured, and Unstructured Data." Journal of Scientific and Engineering Research 8, no. 2 (2021): 263–68. https://doi.org/10.5281/zenodo.11216190.

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The increasing availability and importance of data in various formats have led to the necessity for efficient integration methods to extract meaningful insights. This academic journal explores the challenges and solutions associated with integrating data from multiple sources, including structured, semi-structured, and unstructured data. The study aims to provide an overview of the techniques and tools available to businesses and researchers for effectively integrating diverse data types, enabling better decision-making and improving overall data-driven processes.
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Kunz, Donald L., and A. Stewart Hopkins. "Structured data in structural analysis software." Computers & Structures 26, no. 6 (1987): 965–78. http://dx.doi.org/10.1016/0045-7949(87)90114-3.

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Roukema, J., A. M. van Ginneken, M. de Wilde, J. van der Lei, and R. K. Los. "Are Structured Data Structured Identically?" Methods of Information in Medicine 44, no. 05 (2005): 631–38. http://dx.doi.org/10.1055/s-0038-1634019.

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Summary Objective: OpenSDE is an application that supports structured recording of narrative patient data to enable use of the data in both clinical practice and clinical research. Reliability and accuracy of collected data are essential for subsequent data use. In this study we analyze the uniformity of data entered with OpenSDE. Our objective is to obtain insight into the consensus and differences of recorded data. Methods: Three pediatricians transcribed 20 paper patient records using OpenSDE. The transcribed records were compared and all recorded findings were classified into one of six ca
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Shin, Kilho, and Dave Shepard. "Morphism-Based Learning for Structured Data." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 5767–75. http://dx.doi.org/10.1609/aaai.v34i04.6033.

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In mathematics, morphism is a term that indicates structure-preserving mappings between mathematical structures of the same type. Linear transformations for linear spaces, homomorphisms for algebraic structures and continuous functions for topological spaces are examples. Many data researched in machine learning, on the other hand, can include mathematical structures in them. Strings are totally ordered sets, and trees can be understood not only as graphs but also as partially ordered sets with respect to an ancestor-to-descendent order and semigroups with respect to the binary operation to de
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Zhang, Y. "Open-access and Structured Data in Drug Discovery." Biomedical Data Journal 01, no. 1 (2015): 39–41. http://dx.doi.org/10.11610/bmdj.01107.

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Coolen, Henny. "Measurement and Analysis of Less Structured Data in Housing Research." Open House International 32, no. 3 (2007): 55–65. http://dx.doi.org/10.1108/ohi-03-2007-b0007.

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Two ideal types of data can be distinguished in housing research: structured and less-structured data. Questionnaires and official statistics are examples of structured data, while less-structured data arise for instance from open interviews and documents. Structured data are sometimes labelled quantitative, while less-structured data are called qualitative. In this paper structured and less-structured data are considered from the perspective of measurement and analysis. Structured data arise when the researcher has an a priori category system or measurement scale available for collecting the
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Malewski, Stefan, Michael Greenberg, and Éric Tanter. "Gradually structured data." Proceedings of the ACM on Programming Languages 5, OOPSLA (2021): 1–29. http://dx.doi.org/10.1145/3485503.

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Dynamically-typed languages offer easy interaction with ad hoc data such as JSON and S-expressions; statically-typed languages offer powerful tools for working with structured data, notably algebraic datatypes , which are a core feature of typed languages both functional and otherwise. Gradual typing aims to reconcile dynamic and static typing smoothly. The gradual typing literature has extensively focused on the computational aspect of types, such as type safety, effects, noninterference, or parametricity, but the application of graduality to data structuring mechanisms has been much less exp
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Dissertations / Theses on the topic "Structured data"

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Amornsinlaphachai, Pensri. "Updating semi-structured data." Thesis, Northumbria University, 2007. http://nrl.northumbria.ac.uk/3422/.

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The Web has had a tremendous success with its support for the rapid and inexpensive exchange of information. A considerable body of data exchange is in the form of semi- structured data such as the eXtensible Markup Language (XML). XML, an effective standard to represent and exchange semi-structured data on the Web, is used ubiquitously in almost all areas of information technology. Most researchers in the XML area have concentrated on storing, querying and publishing XML while not many have paid attention to updating XML; thus the XML update area is not fully developed. We propose a solution
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Yang, Lei. "Querying Graph Structured Data." Case Western Reserve University School of Graduate Studies / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=case1410434109.

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Al-Wasil, Fahad M. "Querying distributed heterogeneous structured and semi-structured data sources." Thesis, Cardiff University, 2007. http://orca.cf.ac.uk/56144/.

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The continuing growth and widespread popularity of the internet means that the collection of useful data available for public access is rapidly increasing both in number and size. These data are spread over distributed heterogeneous data sources like traditional databases or sources of various forms containing unstructured and semi-structured data. Obviously, the value of these data sources would in many cases be greatly enhanced if the data they contain could be combined and queried in a uniform manner. The research work reported in this dissertation is concerned with querying and integrating
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Su, Wei. "Motif Mining On Structured And Semi-structured Biological Data." Case Western Reserve University School of Graduate Studies / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=case1365089538.

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Tripney, Brian Grieve. "Data value storage for compressed semi-structured data." Thesis, University of Strathclyde, 2012. http://oleg.lib.strath.ac.uk:80/R/?func=dbin-jump-full&object_id=18962.

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Growing user expectations of anywhere, anytime access to information require new types of data transfer to be considered. While semi-structured data is a common data exchange format, its verbose nature makes les of this type too large to be transferred quickly, especially where only a small part of that data is required by the user. There is consequently a need to develop new models of data storage to support the sharing of small segments of semi-structured data as existing XML compressors require the transfer of the entire compressed structure as a whole. This thesis examines the potential fo
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Mintram, Robert C. "Vector representations of structured data." Thesis, Southampton Solent University, 2002. http://ssudl.solent.ac.uk/624/.

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The connectionist approach to creating vector representations (VREPs) of structured data is usually implemented by artificial neural network (ANN) architectures. ANNs are trained on a representative corpus and can then demonstrate some degree of generalization to novel data. In this context, structured data are typically trees, the leaf nodes of which are assigned some n-element (often binary) vector representation. The strategy used to encode the leaf data and the width of the consequent vectors can have an impact on the encoding performance of the ANN architecture. In this thesis the archite
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Zhang, Chiyuan Ph D. Massachusetts Institute of Technology. "Deep learning and structured data." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/115643.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 135-150).<br>In the recent years deep learning has witnessed successful applications in many different domains such as visual object recognition, detection and segmentation, automatic speech recognition, natural language proc
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Pan, Jiajun. "Metric learning for structured data." Thesis, Nantes, 2019. http://www.theses.fr/2019NANT4076.

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L’apprentissage à distance métrique est une branche de l’apprentissage par re-présentation des algorithmes d’apprentissage automatique. Nous résumons le développement et la situation actuelle de l’algorithme actuel d’apprentissage à distance métrique à partir des aspects de la base de données plate et de la base de données non plate. Pour une série d’algorithmes basés sur la distance de Mahalanobis pour la base de données plate qui ne parvient pas à exploiter l’intersection de trois dimensions ou plus, nous proposons un algorithme d’apprentissage métrique basé sur la fonction sousmodulaire. Po
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Qiao, Shi. "QUERYING GRAPH STRUCTURED RDF DATA." Case Western Reserve University School of Graduate Studies / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=case1447198654.

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Fok, Lordique(Lordique S. ). "Techniques for structured data discovery." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/121671.

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This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 63-64).<br>The discovery of structured data, or data that is tagged by key-value pairs, is a problem that can be subdivided into two issues: how best to structure information architecture and user interaction for discovery; an
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Books on the topic "Structured data"

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Gökhan, BakIr, and Neural Information Processing Systems Foundation., eds. Predicting structured data. MIT Press, 2007.

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Wu, Lisa K. Accelerating Similarly Structured Data. [publisher not identified], 2014.

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Gärtner, Thomas. Kernels for structured data. World Scientific, 2008.

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J, Kazmier Leonard, ed. Structured COBOL. 3rd ed. McGraw-Hill, 1986.

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Nowozin, Sebastian. Advanced structured prediction. The MIT Press, 2014.

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Philippakis, Andreas S. Structured Cobol. 3rd ed. McGraw-Hill, 1986.

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Philippakis, Andreas S. Structured COBOL. 3rd ed. McGraw-Hill, 1986.

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Ellner, Stephen P., Dylan Z. Childs, and Mark Rees. Data-driven Modelling of Structured Populations. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28893-2.

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Patel, Jay M. Getting Structured Data from the Internet. Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-6576-5.

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Ort, Harry H. Structured data processing design and logic. Addison-Wesley, 1985.

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Book chapters on the topic "Structured data"

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Royce, Tony. "The Data Division." In Structured COBOL. Macmillan Education UK, 1992. http://dx.doi.org/10.1007/978-1-349-12240-0_9.

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Royce, Tony. "The Data Division (level numbers)." In Structured COBOL. Macmillan Education UK, 1992. http://dx.doi.org/10.1007/978-1-349-12240-0_10.

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Hewitt, Jill A., and Raymond J. Frank. "Structured Data Types." In Software Engineering in Modula-2. Macmillan Education UK, 1989. http://dx.doi.org/10.1007/978-1-349-11260-9_6.

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Abiteboul, Serge. "Semi-structured Data." In Encyclopedia of Database Systems. Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4899-7993-3_799-2.

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Abiteboul, Serge. "Semi-Structured Data." In Encyclopedia of Database Systems. Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-39940-9_799.

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Martin, Eric, Samuel Kaski, Fei Zheng, et al. "Structured Data Clustering." In Encyclopedia of Machine Learning. Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_795.

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Allen, Grant, Bob Bryla, and Darl Kuhn. "Tree-Structured Data." In Oracle SQL Recipes. Apress, 2009. http://dx.doi.org/10.1007/978-1-4302-2510-2_13.

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Strekalova, Yulia A., and Mustapha Bouakkaz. "Semi-structured Data." In Encyclopedia of Big Data. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-319-32010-6_183.

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Phillips, Jeff M. "Graph-Structured Data." In Mathematical Foundations for Data Analysis. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-62341-8_10.

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Strekalova, Yulia A., and Mustapha Bouakkaz. "Semi-structured Data." In Encyclopedia of Big Data. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-32001-4_183-1.

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Conference papers on the topic "Structured data"

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Cantón-Croda, Rosa-María, and Damián-Emilio Gibaja-Romero. "Transforming Semi-Structured Data into Structured Data by Using R." In 2024 12th International Conference in Software Engineering Research and Innovation (CONISOFT). IEEE, 2024. https://doi.org/10.1109/conisoft63288.2024.00049.

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Wu, Yalong, Shalini Dhamodharan, Vinuthna Ghattamaneni, et al. "Big Data Quality Scoring for Structured Data Using MapReduce." In 2024 33rd International Conference on Computer Communications and Networks (ICCCN). IEEE, 2024. http://dx.doi.org/10.1109/icccn61486.2024.10637520.

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Mehra, Pavitra, and El Kindi Rezig. "Leveraging Structured and Unstructured Data for Tabular Data Cleaning." In 2024 IEEE International Conference on Big Data (BigData). IEEE, 2024. https://doi.org/10.1109/bigdata62323.2024.10825257.

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KUNZ, D., and A. HOPKINS. "Structured data in structural analysis software." In 26th Structures, Structural Dynamics, and Materials Conference. American Institute of Aeronautics and Astronautics, 1985. http://dx.doi.org/10.2514/6.1985-742.

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Kettouch, Mohamed Salah, Cristina Luca, Mike Hobbs, and Arooj Fatima. "Data integration approach for semi-structured and structured data (Linked Data)." In 2015 IEEE 13th International Conference on Industrial Informatics (INDIN). IEEE, 2015. http://dx.doi.org/10.1109/indin.2015.7281842.

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Pan, Qi H., Fedja Hadzic, and Tharam S. Dillon. "Conjoint Data Mining of Structured and Semi-structured Data." In 2008 Fourth International Conference on Semantics, Knowledge and Grid (SKG). IEEE, 2008. http://dx.doi.org/10.1109/skg.2008.57.

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Lafourcade, Mathieu. "Structured lexical data." In the 16th conference. Association for Computational Linguistics, 1996. http://dx.doi.org/10.3115/993268.993377.

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Zhaoshun Wang, Guicheng Shen, and Jinjin Huang. "Synthetic retrieval technology for structured data and Non-structured data." In 2010 2nd International Conference on Information Science and Engineering (ICISE). IEEE, 2010. http://dx.doi.org/10.1109/icise.2010.5691394.

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Karnstedt, M., K. Sattler, M. Hauswirth, and R. Schmidt. "Similarity Queries on Structured Data in Structured Overlays." In 22nd International Conference on Data Engineering Workshops (ICDEW'06). IEEE, 2006. http://dx.doi.org/10.1109/icdew.2006.137.

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Fan, Yingjie, Chenghong Zhang, Shuyun Wang, Xiulan Hao, and Yunfa Hu. "An Efficient Structural Index for Graph-Structured Data." In Seventh IEEE/ACIS International Conference on Computer and Information Science (icis 2008). IEEE, 2008. http://dx.doi.org/10.1109/icis.2008.9.

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Reports on the topic "Structured data"

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Wildgrube, M. Structured Data Exchange Format (SDXF). RFC Editor, 2001. http://dx.doi.org/10.17487/rfc3072.

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Kleinberg, Jon. Algorithms for Networks and Link-Structured Data. Defense Technical Information Center, 2002. http://dx.doi.org/10.21236/ada404776.

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Kleinberg, Jon. Algorithms for Networks and Link Structured Data. Defense Technical Information Center, 2001. http://dx.doi.org/10.21236/ada389559.

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Young, Forrest W., and John B. Smith. Structured Data Analysis: A Cognition-Based Design for Data Analysis Software. Defense Technical Information Center, 1989. http://dx.doi.org/10.21236/ada242044.

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Loh, Wei-Yin. Tree-Structured Methods for Prediction and Data Visualization. Defense Technical Information Center, 2009. http://dx.doi.org/10.21236/ada499342.

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Ockerbloom, John. Exploiting Structured Data in Wide-Area Information Systems,. Defense Technical Information Center, 1995. http://dx.doi.org/10.21236/ada302982.

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Wasserman, Larry, and John Lafferty. Statistical Machine Learning for Structured and High Dimensional Data. Defense Technical Information Center, 2014. http://dx.doi.org/10.21236/ada610544.

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Taleisnik, Sergio, ed. OGC Disaster Pilot JSON-LD Structured Data Engineering Report. Open Geospatial Consortium, Inc., 2023. http://dx.doi.org/10.62973/21-054.

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Claise, B., G. Dhandapani, P. Aitken, and S. Yates. Export of Structured Data in IP Flow Information Export (IPFIX). RFC Editor, 2011. http://dx.doi.org/10.17487/rfc6313.

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Saldanha, Ian J., Birol Senturk, Bryant T. Smith, and Karen A. Robinson. Pilot To Promote Entry of Structured Data Into the Systematic Review Data Repository (SRDR). Agency for Healthcare Research and Quality (AHRQ), 2019. http://dx.doi.org/10.23970/ahrqepcmethqualimprsrdr.

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