Relevant bibliographies by topics / Structured data

Academic literature on the topic 'Structured data'

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Published: 4 June 2021
Last updated: 11 March 2023

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Journal articles on the topic "Structured data"

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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 categories of difference. Results: Of all findings 22% were recorded identically; 17% of the findings were recorded differently (predominantly as free text); 61% was omitted, inferred, or in conflict with the paper record. Conclusion: The results of this study show that recording patient data using structured data entry does not necessarily lead to uniformly structured data.
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Kunz, Donald L., and A. Stewart Hopkins. "Structured data in structural analysis software." Computers & Structures 26, no. 6 (January 1987): 965–78. http://dx.doi.org/10.1016/0045-7949(87)90114-3.

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Zhang, Y. "Open-access and Structured Data in Drug Discovery." Biomedical Data Journal 01, no. 1 (January 2015): 39–41. http://dx.doi.org/10.11610/bmdj.01107.

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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 (April 3, 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 determine nearest common ancestor. In this paper, we propose a generic and theoretic framework to investigate similarity of structured data through structure-preserving one-to-one partial mappings, which we call morphisms. Through morphisms, useful and important methods studied in the literature can be abstracted into common concepts, although they have been studied separately. When we study new structures of data, we will be able to extend the legacy methods for the purpose of studying the new structure, if we can define morphisms properly. Also, this view reveals hidden relations between methods known in the literature and can let us understand them more clearly. For example, we see that the center star algorithm, which was originally developed to compute sequential multiple alignments, can be abstracted so that it not only applies to data structures other than strings but also can be used to solve problems of pattern extraction. The methods that we study in this paper include edit distance, multiple alignment, pattern extraction and kernel, but it is sure that there exist much more methods that can be abstracted within our framework.
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Malewski, Stefan, Michael Greenberg, and Éric Tanter. "Gradually structured data." Proceedings of the ACM on Programming Languages 5, OOPSLA (October 20, 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 explored. While row polymorphism and set-theoretic types have been studied in the context of gradual typing, algebraic datatypes in particular have not, which is surprising considering their wide use in practice. We develop, formalize, and prototype a novel approach to gradually structured data with algebraic datatypes. Gradually structured data bridges the gap between traditional algebraic datatypes and flexible data management mechanisms such as tagged data in dynamic languages, or polymorphic variants in OCaml. We illustrate the key ideas of gradual algebraic datatypes through the evolution of a small server application from dynamic to progressively more static checking, formalize a core functional language with gradually structured data, and establish its metatheory, including the gradual guarantees.
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Da San Martino, Giovanni, and Alessandro Sperduti. "Mining Structured Data." IEEE Computational Intelligence Magazine 5, no. 1 (February 2010): 42–49. http://dx.doi.org/10.1109/mci.2009.935308.

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Sorber, Laurent, Marc Van Barel, and Lieven De Lathauwer. "Structured Data Fusion." IEEE Journal of Selected Topics in Signal Processing 9, no. 4 (June 2015): 586–600. http://dx.doi.org/10.1109/jstsp.2015.2400415.

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Liu, Feng Hua. "Research on the Data Model and the Approaches to Data Mining in the Semi-Structured Data." Applied Mechanics and Materials 513-517 (February 2014): 663–66. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.663.

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As an important form of Internet data, semi-structured data in data mining is an important fist conditions. And the data mining was designed to find and extract large database in the implied information of value. This paper first introduced the half structured data concept characteristic, based on the data from each of the half structural said, the data model two half-and-half structured data model are introduced, finally summarizes semi-structured data model and the relationship between the data model before difference [1].
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Coolen, Henny. "Measurement and Analysis of Less Structured Data in Housing Research." Open House International 32, no. 3 (September 1, 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 data. When such an a priori system or scale is not available the data are called less-structured. It will be argued that these less-structured observations can only be used for any further analysis when they contain some minimum level of structure called a category system, which is equivalent to a nominal measurement scale. Once this becomes evident, one realizes that through the necessary process of categorization less-structured data can be analyzed in much the same way as structured data, and that the difference between the two types of data is one of degree and not of kind. In the second part of the paper these ideas are illustrated with examples from my own research on the meaning of preferences for dwelling features in which the concept of a meaning structure plays a central part. Until now these meaning structures have been determined by means of semi-structured interviews which, even with small samples, result in large amounts of less-structured data.
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Mastorci, F., and G. Iervasi. "The Need for Open-access, Structured Data in Endocrine Research." Biomedical Data Journal 01, no. 1 (January 2015): 33–35. http://dx.doi.org/10.11610/bmdj.01105.

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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 for updating XML as a representation of semi-structured data. XML is updated through an object-relational database (ORDB) to exploit the maturity of the relational engine and the newer object features of the OR technology. The engine is used to enforce constraints during the updating of the XML whereas the object features are used to handle the XML hierarchical structure. Updating XML via ORDB makes it easier to join XML documents in an update and in turn joins of XML documents make it possible to keep non-redundant data in multiple XML documents. This thesis contributes a solution for the update of XML documents via an ORDB to advance our understanding of the XML update area. Rules for mapping XML structure and constraints to an ORDB schema are presented and a mechanism to handle XML cardinality constraint is provided. An XML update language, an extension to XQuery, has been designed and this language is translated into the standard SQL executed on an ORDB. To handle the recursive nature of XML, a recursive function updating XML data is translated into SQL commands equipped with a programming capability. A method is developed to reflect the changes from the ORDB to XML documents. A prototype of the solution has been implemented to help validate our approach. Experimental study to evaluate the performance of XML update processing based on the prototype has been conducted. The experimental results show that updating multiple XML documents storing non-redundant data yields a better performance than updating a single XML document storing redundant data; an ORDB can take advantage of this by caching data to a greater extent than a native XML database. The solution of updating XML documents via an ORDB can solve some problems in existing update methods as follows. Firstly, the preservation of XML constraints is handled by the ORDB engine. Secondly, non-redundant data is stored in linked XML documents; thus the problem of data inconsistency and low performance caused by data redundancy are solved. Thirdly, joins of XML documents are converted to joins of tables in SQL. Fourthly, fields or tables involved in regular path expressions can be tackled in a short time by using mapping data. Finally, a recursive function is translated into SQL commands equipped with a programming capability.
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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 a multiplicity of distributed heterogeneous structured data residing in relational databases and semi-structured data held in well- formed XML documents produced by internet applications or human- coded. In particular, we have addressed the problems of: (1) specifying the mappings between a global schema and the local data sources' schemas, and resolving the heterogeneity which can occur between data models, schemas or schema concepts (2) processing queries that are expressed on a global schema into local queries. We have proposed an approach to combine and query the data sources through a mediation layer. Such a layer is intended to establish and evolve an XML Metadata Knowledge Base (XMKB) incrementally which assists the Query Processor in mediating between user queries posed over the global schema and the queries on the underlying distributed heterogeneous data sources. It translates such queries into sub-queries -called local queries- which are appropriate to each local data source. The XMKB is built in a bottom-up fashion by extracting and merging incrementally the metadata of the data sources. It holds the data source's information (names, types and locations), descriptions of the mappings between the global schema and the participating data source schemas, and function names for handling semantic and structural discrepancies between the representations. To demonstrate our research, we have designed and implemented a prototype system called SISSD (System to Integrate Structured and Semi- structured Databases). The system automatically creates a GUI tool for meta-users (who do the metadata integration) which they use to describe mappings between the global schema and local data source schemas. These mappings are used to produce the XMKB. The SISSD allows the translation of user queries into sub-queries fitting each participating data source, by exploiting the mapping information stored in the XMKB. The major results of the thesis are: (1) an approach that facilitates building structured and semi-structured data integration systems (2) a method for generating mappings between a global and local schemas' paths, and resolving the conflicts caused by the heterogeneity of the data sources such as naming, structural, and semantic conflicts which, may occur between the schemas (3) a method for translating queries in terms of a global schema into sub-queries in terms of local schemas. Hence, the presented approach shows that: (a) mapping of the schemas' paths can only be partially automated, since the logical heterogeneity problems need to be resolved by human judgment based on the application requirements (b) querying distributed heterogeneous structured and semi-structured data sources is possible.
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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 for bisimilarity-based partitioning (i.e. the grouping of items with similar structural patterns) to be combined with dictionary compression methods to produce a data storage model that remains directly accessible for query processing whilst facilitating the sharing of individual data segments. The use of dictionary compression is shown to compare favourably against Hu mantype compression, especially with regard to real world data sets, while a study of the e ects of di ering types of bisimilarity upon the storage of data values identi ed the use of both forwards and backwards bisimilarity as the most promising basis for a dictionary-compressed structure. Having employed the above in a combined storage model, a query strategy is detailed which takes advantage of the compressed structure to reduce the number of data segments that must be accessed (and therefore transferred) to answer a query. A method to remove redundancy within the data dictionaries is also described and shown to have a positive e ect in terms of disk space usage.
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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 architecture of principle interest is called simplified recursive auto associative memory, (S)RAAM, which was devised to provide a theoretical model for abother architecture called recursive auto associative memory, RAAM. Research continues in RAAMs in terms of improving their learning ability, understanding the features that are encoded and improving generalization. (S)RAAM is a mathematical model that lends itself more readily to addressing these issues. Usually ANNs designed to encode structured data will, as a result of training, simultaneously create an encoder function to transform the data into vectors and a decoder function to perform the reverse transformation. (S)RAAM is a mathematical model that lends itself more readily to addressing these issues. Usually ANNs designed to encode structured data will, as a result of training, simultaneously create an encoder function to transform the data into vectors and a decoder function to perform the reverse transformation. (S)RAAM as a model of this process was designed to follow this paradigm. It is shown that this is not strictly necessary and that encoder and decoder functions can be created at separate times, their connection being maintained by the data unpon which they operate. This leads to a new, more versatile model called, in this thesis, the General Encoder Decoder, GED. The GED, like (S)RAAM, is implemented as an algorithm rather than a neural network architecture. The thesis contends that the broad scope of the GED model makes it a versatile experimental vehicle supporting research into key properties of VREPs. In particular these properties include the strategy used to encode the leaf tokens within tree structures and the features of these structures that are preferentially encoded
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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.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 135-150).
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 processing, and reinforcement learning. In this thesis, we will investigate deep learning from a spectrum of different perspectives. First of all, we will study the question of generalization, which is one of the most fundamental notion in machine learning theory. We will show how, in the regime of deep learning, the characterization of generalization becomes different from the conventional way, and propose alternative ways to approach it. Moving from theory to more practical perspectives, we will show two different applications of deep learning. One is originated from a real world problem of automatic geophysical feature detection from seismic recordings to help oil & gas exploration; the other is motivated from a computational neuroscientific modeling and studying of human auditory system. More specifically, we will show how deep learning could be adapted to play nicely with the unique structures associated with the problems from different domains. Lastly, we move to the computer system design perspective, and present our efforts in building better deep learning systems to allow efficient and flexible computation in both academic and industrial worlds.
by Chiyuan Zhang.
Ph. D.
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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. Pour le manque d’algorithmes d’apprentissage métrique pour les bases de données relationnelles dans des bases de données non plates, nous proposons LSCS (sélection de contraintes relationnelles de force relationnelle) pour la sélection de contraintes pour des algorithmes d’apprentissage métrique avec informations parallèles et MRML (Multi-Relation d’apprentissage métrique) qui somme la perte des contraintes relationnelles et les contraintes d’etiquetage. Grâce aux expériences de conception et à la vérification sur la base de données réelle, les algorithmes proposés sont meilleurs que les algorithmes actuels
Metric distance learning is a branch of re-presentation learning in machine learning algorithms. We summarize the development and current situation of the current metric distance learning algorithm from the aspects of the flat database and nonflat database. For a series of algorithms based on Mahalanobis distance for the flat database that fails to make full use of the intersection of three or more dimensions, we propose a metric learning algorithm based on the submodular function. For the lack of metric learning algorithms for relational databases in non-flat databases, we propose LSCS(Relational Link-strength Constraints Selection) for selecting constraints for metric learning algorithms with side information and MRML (Multi-Relation Metric Learning) which sums the loss from relationship constraints and label constraints. Through the design experiments and verification on the real database, the proposed algorithms are better than the current algorithms
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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.
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 63-64).
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; and how to intelligently display data in a way that that optimizes the discovery of "useful" (i.e. relevant and helpful for a user's current use case) data. In this thesis, I investigate multiple methods of addressing both issues, and the results of evaluating these methods qualitatively and quantitatively. Specifically, I implement and evaluate: a novel interface design which combines different aspects of existing interfaces, two methods of diversifying data subsets given a search query, three methods of incorporating relevance in data subsets given a search query and information about the user's historic queries, a novel method of visualizing structured data, and two methods of inducing hierarchy on structured data in the presence of an partial data schema. These implementations and evaluations are shown to be effective in structuring information architecture and user interaction for structured data discovery, but are only partially effective in intelligently displaying data to optimize discovery of useful structured data.
by Lordique Fok.
M. Eng.
M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
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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. Cambridge, Mass: MIT Press, 2007.

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Data structured program design. Englewood Cliffs, NJ: Prentice-Hall, 1986.

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Wu, Lisa K. Accelerating Similarly Structured Data. [New York, N.Y.?]: [publisher not identified], 2014.

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Developing data structured databases. Englewood Cliffs, N.J: Prentice-Hall, 1987.

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Kernels for structured data. Singapore: World Scientific, 2008.

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Gärtner, Thomas. Kernels for structured data. Hackensack, NJ: World Scientific, 2008.

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Computer analysis of structures: Matrix structural analysis structured programming. New York: Elsevier, 1985.

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Structured regression for categorical data. Cambridge: Cambridge University Press, 2011.

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Nowozin, Sebastian. Advanced structured prediction. Cambridge, MA: The MIT Press, 2014.

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

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Book chapters on the topic "Structured data"

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Royce, Tony. "The Data Division." In Structured COBOL, 10. London: 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, 11. London: 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, 74–93. London: 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, 1–3. New York, NY: 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, 2599–601. Boston, MA: 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, Geoffrey I. Webb, Xiaojin Zhu, Ion Muslea, Kai Ming Ting, et al. "Structured Data Clustering." In Encyclopedia of Machine Learning, 930. Boston, MA: 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, 313–33. Berkeley, CA: 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, 816–19. Cham: 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, 237–60. Cham: 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, 1–3. Cham: 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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KUNZ, D., and A. HOPKINS. "Structured data in structural analysis software." In 26th Structures, Structural Dynamics, and Materials Conference. Reston, Virigina: 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. Morristown, NJ, USA: 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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Armbrust, Michael, Tathagata Das, Joseph Torres, Burak Yavuz, Shixiong Zhu, Reynold Xin, Ali Ghodsi, Ion Stoica, and Matei Zaharia. "Structured Streaming." In SIGMOD/PODS '18: International Conference on Management of Data. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3183713.3190664.

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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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Shams, Montasir, Sophie Pavia, Rituparna Khan, Anna Pyayt, and Michael Gubanov. "Towards Unveiling Dark Web Structured Data." In 2021 IEEE International Conference on Big Data (Big Data). IEEE, 2021. http://dx.doi.org/10.1109/bigdata52589.2021.9671367.

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Cheng, Jiefeng, and Jeffrey Xu Yu. "Querying Graph-Structured Data." In 2007 IFIP International Conference on Network and Parallel Computing Workshops (NPC 2007). IEEE, 2007. http://dx.doi.org/10.1109/npc.2007.166.

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Reports on the topic "Structured data"

1

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

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

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Kleinberg, Jon. Algorithms for Networks and Link Structured Data. Fort Belvoir, VA: Defense Technical Information Center, April 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. Fort Belvoir, VA: Defense Technical Information Center, August 1989. http://dx.doi.org/10.21236/ada242044.

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

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Ockerbloom, John. Exploiting Structured Data in Wide-Area Information Systems,. Fort Belvoir, VA: Defense Technical Information Center, August 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. Fort Belvoir, VA: Defense Technical Information Center, September 2014. http://dx.doi.org/10.21236/ada610544.

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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, July 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), October 2019. http://dx.doi.org/10.23970/ahrqepcmethqualimprsrdr.

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Arnold, Zachary, Joanne Boisson, Lorenzo Bongiovanni, Daniel Chou, Carrie Peelman, and Ilya Rahkovsky. Using Machine Learning to Fill Gaps in Chinese AI Market Data. Center for Security and Emerging Technology, February 2021. http://dx.doi.org/10.51593/20200064.

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Abstract:
In this proof-of-concept project, CSET and Amplyfi Ltd. used machine learning models and Chinese-language web data to identify Chinese companies active in artificial intelligence. Most of these companies were not labeled or described as AI-related in two high-quality commercial datasets. The authors' findings show that using structured data alone—even from the best providers—will yield an incomplete picture of the Chinese AI landscape.
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