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Dissertations / Theses on the topic 'Structured data'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Miner, andrew S. "Data structures for the analysis of large structured Markov models." W&M ScholarWorks, 2000. https://scholarworks.wm.edu/etd/1539623985.

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High-level modeling formalisms are increasingly popular tools for studying complex systems. Given a high-level model, we can automatically verify certain system properties or compute performance measures about the system. In the general case, measures must be computed using discrete-event simulations. In certain cases, exact numerical analysis is possible by constructing and analyzing the underlying stochastic process of the system, which is a continuous-time Markov chain (CTMC) in our case. Unfortunately, the number of states in the underlying CTMC can be extremely large, even if the high-lev

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12

Schönauer, Stefan. "Efficient similarity search in structured data." [S.l.] : [s.n.], 2004. http://edoc.ub.uni-muenchen.de/archive/00001802.

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13

Schönauer, Stefan. "Efficient Similarity Search in Structured Data." Diss., lmu, 2004. http://nbn-resolving.de/urn:nbn:de:bvb:19-18022.

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14

Wackersreuther, Bianca. "Efficient Knowledge Extraction from Structured Data." Diss., lmu, 2011. http://nbn-resolving.de/urn:nbn:de:bvb:19-138079.

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15

Kashima, Hisashi. "Machine learning approaches for structured data." 京都大学 (Kyoto University), 2007. http://hdl.handle.net/2433/135953.

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16

Maksimovic, Gordana. "Query Languages for Semi-structured Data." Thesis, Blekinge Tekniska Högskola, Institutionen för programvaruteknik och datavetenskap, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-4332.

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Semi-structured data is defined as irregular data with structure that may change rapidly or unpredictably. An example of such data can be found inside the World-Wide Web. Since the data is irregular, the user may not know the complete structure of the database. Thus, querying such data becomes a difficult issue. In order to write meaningful queries on semi-structured data, there is a need for a query language that will support the features that are presented by this data. Standard query languages, such as SQL for relational databases and OQL for object databases, are too constraining for query

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17

Ng, Kee Siong, and kee siong@rsise anu edu au. "Learning Comprehensible Theories from Structured Data." The Australian National University. Research School of Information Sciences and Engineering, 2005. http://thesis.anu.edu.au./public/adt-ANU20051031.105726.

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This thesis is concerned with the problem of learning comprehensible theories from structured data and covers primarily classification and regression learning. The basic knowledge representation language is set around a polymorphically-typed, higher-order logic. The general setup is closely related to the learning from propositionalized knowledge and learning from interpretations settings in Inductive Logic Programming. Individuals (also called instances) are represented as terms in the logic. A grammar-like construct called a predicate rewrite system is used to define features in the for

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18

Thomson, Susan Elizabeth. "A storage service for structured data." Thesis, University of Cambridge, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.385486.

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19

NUNES, BERNARDO PEREIRA. "AUTOMATIC CLASSIFICATION OF SEMI-STRUCTURED DATA." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2009. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=14382@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO<br>O problema da classificação de dados remonta à criação de taxonomias visando cobrir áreas do conhecimento. Com o surgimento da Web, o volume de dados disponíveis aumentou várias ordens de magnitude, tornando praticamente impossível a organização de dados manualmente. Esta dissertação tem por objetivo organizar dados semi-estruturados, representados por frames, sem uma estrutura de classes prévia. A dissertação apresenta um algoritmo, baseado no K-Medóide, capaz de organizar um conjunto de frames em classes, estruturadas sob forma de uma hie

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20

Evanco, Kathleen L. (Kathleen Lee). "Customized data visualization using structured video." Thesis, Massachusetts Institute of Technology, 1996. http://hdl.handle.net/1721.1/29106.

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21

Lee, John Boaz T. "Deep Learning on Graph-structured Data." Digital WPI, 2019. https://digitalcommons.wpi.edu/etd-dissertations/570.

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In recent years, deep learning has made a significant impact in various fields – helping to push the state-of-the-art forward in many application domains. Convolutional Neural Networks (CNN) have been applied successfully to tasks such as visual object detection, image super-resolution, and video action recognition while Long Short-term Memory (LSTM) and Transformer networks have been used to solve a variety of challenging tasks in natural language processing. However, these popular deep learning architectures (i.e., CNNs, LSTMs, and Transformers) can only handle data that can be represented a

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22

Bandyopadhyay, Bortik. "Querying Structured Data via Informative Representations." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1595447189545086.

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23

Folkesson, Carl. "Anonymization of directory-structured sensitive data." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-160952.

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Data anonymization is a relevant and important field within data privacy, which tries to find a good balance between utility and privacy in data. The field is especially relevant since the GDPR came into force, because the GDPR does not regulate anonymous data. This thesis focuses on anonymization of directory-structured data, which means data structured into a tree of directories. In the thesis, four of the most common models for anonymization of tabular data, k-anonymity, ℓ-diversity, t-closeness and differential privacy, are adapted for anonymization of directory-structured data. This adapt

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24

Ng, Kee Siong. "Learning comprehensible theories from structured data /." View thesis entry in Australian Digital Theses Program, 2005. http://thesis.anu.edu.au/public/adt-ANU20051031.105726/index.html.

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25

Da, San Martino Giovanni <1979&gt. "Kernel Methods for Tree Structured Data." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2009. http://amsdottorato.unibo.it/1400/1/thesis.pdf.

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Machine learning comprises a series of techniques for automatic extraction of meaningful information from large collections of noisy data. In many real world applications, data is naturally represented in structured form. Since traditional methods in machine learning deal with vectorial information, they require an a priori form of preprocessing. Among all the learning techniques for dealing with structured data, kernel methods are recognized to have a strong theoretical background and to be effective approaches. They do not require an explicit vectorial representation of the data in term

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Da, San Martino Giovanni <1979&gt. "Kernel Methods for Tree Structured Data." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2009. http://amsdottorato.unibo.it/1400/.

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Machine learning comprises a series of techniques for automatic extraction of meaningful information from large collections of noisy data. In many real world applications, data is naturally represented in structured form. Since traditional methods in machine learning deal with vectorial information, they require an a priori form of preprocessing. Among all the learning techniques for dealing with structured data, kernel methods are recognized to have a strong theoretical background and to be effective approaches. They do not require an explicit vectorial representation of the data in term

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27

Tatikonda, Shirish. "Towards Efficient Data Analysis and Management of Semi-structured Data." The Ohio State University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=osu1275414859.

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28

Eichhorn, Jan. "Applications of kernel machines to structured data." [S.l.] : [s.n.], 2006. http://opus.kobv.de/tuberlin/volltexte/2007/1507.

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29

Lipton, Zachary C. "Learning from Temporally-Structured Human Activities Data." Thesis, University of California, San Diego, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10683703.

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<p> Despite the extraordinary success of deep learning on diverse problems, these triumphs are too often confined to large, clean datasets and well-defined objectives. Face recognition systems train on millions of perfectly annotated images. Commercial speech recognition systems train on thousands of hours of painstakingly-annotated data. But for applications addressing human activity, data can be noisy, expensive to collect, and plagued by missing values. In electronic health records, for example, each attribute might be observed on a different time scale. Complicating matters further, decidi

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30

Paaßen, Benjamin [Verfasser]. "Metric Learning for Structured Data / Benjamin Paaßen." Bielefeld : Universitätsbibliothek Bielefeld, 2019. http://d-nb.info/1186887818/34.

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31

Blampied, Paul Alexander. "Structured recursion for non-uniform data-types." Thesis, University of Nottingham, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.342028.

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32

Cottee, Michaela J. "The graphical representation of structured multivariate data." Thesis, Open University, 1996. http://oro.open.ac.uk/57616/.

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During the past two decades or so, graphical representations have been used increasingly for the examination, summarisation and communication of statistical data. Many graphical techniques exist for exploratory data analysis (ie. for deciding which model it is appropriate to fit to the data) and a number of graphical diagnostic techniques exist for checking the appropriateness of a fitted model. However, very few techniques exist for the representation of the fitted model itself. This thesis is concerned with the development of some new and existing graphical representation techniques for the

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33

Sun, Yizhi. "Statistical Analysis of Structured High-dimensional Data." Diss., Virginia Tech, 2018. http://hdl.handle.net/10919/97505.

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High-dimensional data such as multi-modal neuroimaging data and large-scale networks carry excessive amount of information, and can be used to test various scientific hypotheses or discover important patterns in complicated systems. While considerable efforts have been made to analyze high-dimensional data, existing approaches often rely on simple summaries which could miss important information, and many challenges on modeling complex structures in data remain unaddressed. In this proposal, we focus on analyzing structured high-dimensional data, including functional data with important local

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34

Tu, Ying. "Focus-based Interactive Visualization for Structured Data." The Ohio State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=osu1366198735.

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35

Morris, Henry. "Sparse nonlinear methods for predicting structured data." Thesis, Imperial College London, 2012. http://hdl.handle.net/10044/1/9548.

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Gaussian processes are now widely used to perform key machine learning tasks such as nonlinear regression and classification. An attractive feature of Gaussian process models is the behaviour of the error bars, which grow in regions away from observations where there is high uncertainty about the interpolating function. The complexity of these models scales as O(N3) with sample size, which causes difficulties with large data sets. The goals of this work are to develop nonlinear, nonparametric modelling techniques for structure learning and prediction problems in which there are structured depe

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36

Bala, Saimir. "Mining Projects from Structured and Unstructured Data." Jens Gulden, Selmin Nurcan, Iris Reinhartz-Berger, Widet Guédria, Palash Bera, Sérgio Guerreiro, Michael Fellman, Matthias Weidlich, 2017. http://epub.wu.ac.at/7205/1/ProjecMining%2DCamera%2DReady.pdf.

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Companies working on safety-critical projects must adhere to strict rules imposed by the domain, especially when human safety is involved. These projects need to be compliant to standard norms and regulations. Thus, all the process steps must be clearly documented in order to be verifiable for compliance in a later stage by an auditor. Nevertheless, documentation often comes in the form of manually written textual documents in different formats. Moreover, the project members use diverse proprietary tools. This makes it difficult for auditors to understand how the actual project was condu

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37

King, Michael Allen. "Ensemble Learning Techniques for Structured and Unstructured Data." Diss., Virginia Tech, 2015. http://hdl.handle.net/10919/51667.

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This research provides an integrated approach of applying innovative ensemble learning techniques that has the potential to increase the overall accuracy of classification models. Actual structured and unstructured data sets from industry are utilized during the research process, analysis and subsequent model evaluations. The first research section addresses the consumer demand forecasting and daily capacity management requirements of a nationally recognized alpine ski resort in the state of Utah, in the United States of America. A basic econometric model is developed and three classic p

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38

Sundaravadivelu, Rathinasabapathy. "Interoperability between heterogeneous and distributed biodiversity data sources in structured data networks." Thesis, Cardiff University, 2010. http://orca.cf.ac.uk/18086/.

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The extensive capturing of biodiversity data and storing them in heterogeneous information systems that are accessible on the internet across the globe has created many interoperability problems. One is that the data providers are independent of others and they can run systems which were developed on different platforms at different times using different software products to respond to different needs of information. A second arises from the data modelling used to convert the real world data into a computerised data structure which is not conditioned by a universal standard. Most importantly t

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39

Otaki, Keisuke. "Algorithmic Approaches to Pattern Mining from Structured Data." 京都大学 (Kyoto University), 2016. http://hdl.handle.net/2433/215673.

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The contents of Chapter 6 are based on work published in IPSJ Transactions on Mathematical Modeling and Its Applications, vol.9(1), pp.32-42, 2016.<br>Kyoto University (京都大学)<br>0048<br>新制・課程博士<br>博士(情報学)<br>甲第19846号<br>情博第597号<br>新制||情||104(附属図書館)<br>32882<br>京都大学大学院情報学研究科知能情報学専攻<br>(主査)教授山本章博, 教授鹿島久嗣, 教授阿久津達也<br>学位規則第4条第1項該当

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Zhao, Xiaoyan 1966. "Trie methods for structured data on secondary storage." Thesis, McGill University, 2000. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=36855.

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This thesis presents trie organizations for one-dimensional and multidimensional structured data on secondary storage. The new trie structures have several distinctive features: (1) they provide significant storage compression by sharing common paths near the root; (2) they are partitioned into pages and are suitable for secondary storage; (3) they are capable of dynamic insertions and deletions of records; (4) they support efficient multidimensional variable-resolution queries by storing the most significant bits near the root.<br>We apply the trie structures to indexing, storing and querying

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NUNES, IAN MONTEIRO. "CLUSTERING TEXT STRUCTURED DATA BASED ON TEXT SIMILARITY." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2008. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=25796@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO<br>COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR<br>PROGRAMA DE EXCELENCIA ACADEMICA<br>O presente trabalho apresenta os resultados que obtivemos com a aplicação de grande número de modelos e algoritmos em um determinado conjunto de experimentos de agrupamento de texto. O objetivo de tais testes é determinar quais são as melhores abordagens para processar as grandes massas de informação geradas pelas crescentes demandas de data quality em diversos setores da economia. O processo de deduplicação foi acelerado pela divisão dos con

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Gianniotis, Nikolaos. "Visualisation of structured data through generative probabilistic modeling." Thesis, University of Birmingham, 2008. http://etheses.bham.ac.uk//id/eprint/4803/.

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This thesis is concerned with the construction of topographic maps of structured data. A probabilistic generative model-based approach is taken, inspired by the GTM algorithm. De- pending on the data at hand, the form of a probabilistic generative model is specified that is appropriate for modelling the probability density of the data. A mixture of such models is formulated which is topographically constrained on a low-dimensional latent space. By con- strained, we mean that each point in the latent space determines the parameters of one model via a smooth non-linear mapping; by topographic, w

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Stachowiak, Maciej 1976. "Automated extraction of structured data from HTML documents." Thesis, Massachusetts Institute of Technology, 1998. http://hdl.handle.net/1721.1/9896.

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Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1998.<br>Includes bibliographical references (leaf 45).<br>by Maciej Stachowiak.<br>M.Eng.

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44

Zhang, Peng. "Structured sensing for estimation of high-dimensional data." Thesis, Imperial College London, 2016. http://hdl.handle.net/10044/1/49415.

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Efficient estimation and processing of high-dimensional data is important in many scientic and engineering domains. In this thesis, we explore structured sensing methods for high-dimensional signal in three different perspectives: structured random matrices for compressed sensing and corrupted sensing, atomic norm regularization for massive multiple-input-multiple-output (MIMO) systems and variable density sampling for random field. Designing efficient sensing systems for high-dimensional data by appealing to the prior knowledge that their intrinsic information is usually small has become popu

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45

Forshaw, Gareth William. "Semi-automatic matching of semi-structured data updates." Master's thesis, University of Cape Town, 2014. http://hdl.handle.net/11427/12930.

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Includes bibliographical references.<br>Data matching, also referred to as data linkage or field matching, is a technique used to combine multiple data sources into one data set. Data matching is used for data integration in a number of sectors and industries; from politics and health care to scientific applications. The motivation for this study was the observation of the day-to-day struggles of a large non-governmental organisation (NGO) in managing their membership database. With a membership base of close to 2.4 million, the challenges they face with regard to the capturing and processing

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46

Ni, Weizeng. "Ontology-based Feature Construction on Non-structured Data." University of Cincinnati / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1439309340.

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47

Herrmann, Kai, Hannes Voigt, and Wolfgang Lehner. "Cinderella - Adaptive Online Partitioning of Irregularly Structured Data." IEEE, 2014. https://tud.qucosa.de/id/qucosa%3A75273.

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In an increasing number of use cases, databases face the challenge of managing irregularly structured data. Irregularly structured data is characterized by a quickly evolving variety of entities without a common set of attributes. These entities do not show enough regularity to be captured in a traditional database schema. A common solution is to centralize the diverse entities in a universal table. Usually, this leads to a very sparse table. Although today's techniques allow efficient storage of sparse universal tables, query efficiency is still a problem. Queries that reference only a subset

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48

Weng, Daiyue. "Extracting structured data from Web query result pages." Thesis, Queen's University Belfast, 2016. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.709858.

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A rapidly increasing number of Web databases are now become accessible via their HTML form- based query interfaces only. Comparing various services or products from a number of web sites in a specific domain is time-consuming and tedious. There is a demand for value-added Web applications that integrate data from multiple sources. To facilitate the development of such applications, we need to develop techniques for automating the process of providing integrated access to a multitude of database-driven Web sites, and integrating data from their underlying databases. This presents three challeng

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49

Doan, AnHai. "Learning to map between structured representations of data /." Thesis, Connect to this title online; UW restricted, 2002. http://hdl.handle.net/1773/6968.

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Bui, Dang Bach. "Mining complex structured data: Enhanced methods and applications." Thesis, Curtin University, 2015. http://hdl.handle.net/20.500.11937/480.

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Conventional approaches to analysing complex business data typically rely on process models, which are difficult to construct and use. This thesis addresses this issue by converting semi-structured event logs to a simpler flat representation without any loss of information, which then enables direct applications of classical data mining methods. The thesis also proposes an effective and scalable classification method which can identify distinct characteristics of a business process for further improvements.

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