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Dissertations / Theses on the topic 'Data mining in relational databases'

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Published: 4 June 2021

Last updated: 3 February 2022

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1

Chen, Yu 1979. "Data mining relational databases with probabilistic relational models." Thesis, McGill University, 2006. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=97928.

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Relational databases are a popular method for organizing and storing data. Unfortunately, many machine-learning techniques are unable to handle complex relational models. The Probabilistic Relational Model (PRM) is an extension of the Bayesian Network framework that can express relational structure as well as probabilistic dependencies. In this thesis, we significantly expand and improve an implementation of PRMs that allows defining conditional probability distributions over discrete and continuous variables. The thesis uses as starting point an implementation that has various problems, and r

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2

Toprak, Serkan. "Data Mining For Rule Discovery In Relational Databases." Master's thesis, METU, 2004. http://etd.lib.metu.edu.tr/upload/12605356/index.pdf.

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Data is mostly stored in relational databases today. However, most data mining algorithms are not capable of working on data stored in relational databases directly. Instead they require a preprocessing step for transforming relational data into algorithm specified form. Moreover, several data mining algorithms provide solutions for single relations only. Therefore, valuable hidden knowledge involving multiple relations remains undiscovered. In this thesis, an implementation is developed for discovering multi-relational association rules in relational databases. The implementation is based on

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3

Jafer, Yasser. "Aggregation and Privacy in Multi-Relational Databases." Thèse, Université d'Ottawa / University of Ottawa, 2012. http://hdl.handle.net/10393/22695.

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Most existing data mining approaches perform data mining tasks on a single data table. However, increasingly, data repositories such as financial data and medical records, amongst others, are stored in relational databases. The inability of applying traditional data mining techniques directly on such relational database thus poses a serious challenge. To address this issue, a number of researchers convert a relational database into one or more flat files and then apply traditional data mining algorithms. The above-mentioned process of transforming a relational database into one or more flat fi

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4

Zou, Beibei 1974. "Data mining with relational database management systems." Thesis, McGill University, 2005. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=82456.

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With the increasing demands of transforming raw data into information and knowledge, data mining becomes an important field to the discovery of useful information and hidden patterns in huge datasets. Both machine learning and database research have made major contributions to the field of data mining. However, there is still little effort made to improve the scalability of algorithms applied in data raining tasks. Scalability is crucial for data mining algorithms, since they have to handle large datasets quite often. In this thesis we take a step in this direction by extending a popula

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5

Ma, Xuesong 1975. "Data mining using relational database management system." Thesis, McGill University, 2005. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=98757.

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With the wide availability of huge amounts of data and the imminent demands to transform the raw data into useful information and knowledge, data mining has become an important research field both in the database area and the machine learning areas. Data mining is defined as the process to solve problems by analyzing data already present in the database and discovering knowledge in the data. Database systems provide efficient data storage, fast access structures and a wide variety of indexing methods to speed up data retrieval. Machine learning provides theory support for most of the popular d

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6

Kanodia, Juveria. "Structural advances for pattern discovery in multi-relational databases /." Link to online version, 2005. https://ritdml.rit.edu/dspace/handle/1850/978.

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7

Daglar, Toprak Seda. "A New Hybrid Multi-relational Data Mining Technique." Master's thesis, METU, 2005. http://etd.lib.metu.edu.tr/upload/12606150/index.pdf.

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Multi-relational learning has become popular due to the limitations of propositional problem definition in structured domains and the tendency of storing data in relational databases. As patterns involve multiple relations, the search space of possible hypotheses becomes intractably complex. Many relational knowledge discovery systems have been developed employing various search strategies, search heuristics and pattern language limitations in order to cope with the complexity of hypothesis space. In this work, we propose a relational concept learning technique, which adopts concept descriptio

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8

Collopy, Ethan Richard. "Data mining temporal and indefinite relations with numerical dependencies." Thesis, University College London (University of London), 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.327099.

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9

Lepinioti, Konstantina. "Data mining and database systems : integrating conceptual clustering with a relational database management system." Thesis, Bournemouth University, 2011. http://eprints.bournemouth.ac.uk/17765/.

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Many clustering algorithms have been developed and improved over the years to cater for large scale data clustering. However, much of this work has been in developing numeric based algorithms that use efficient summarisations to scale to large data sets. There is a growing need for scalable categorical clustering algorithms as, although numeric based algorithms can be adapted to categorical data, they do not always produce good results. This thesis presents a categorical conceptual clustering algorithm that can scale to large data sets using appropriate data summarisations. Data mining is dist

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10

Oyama, Fernando Takeshi [UNESP]. "Mineração multirrelacional de regras de associação em grandes bases de dados." Universidade Estadual Paulista (UNESP), 2010. http://hdl.handle.net/11449/98694.

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Made available in DSpace on 2014-06-11T19:29:40Z (GMT). No. of bitstreams: 0 Previous issue date: 2010-02-22Bitstream added on 2014-06-13T20:39:07Z : No. of bitstreams: 1 oyama_ft_me_sjrp.pdf: 1107324 bytes, checksum: 0977db2af1589dece4aa46b5882d84d6 (MD5)<br>O crescente avanço e a disponibilidade de recursos computacionais viabilizam o armazenamento e a manipulação de grandes bases de dados. As técnicas típicas de mineração de dados possibilitam a extração de padrões desde que os dados estejam armazenados em uma única tabela. A mineração de dados multirrelacional, por sua vez, apresenta-se

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11

Oyama, Fernando Takeshi. "Mineração multirrelacional de regras de associação em grandes bases de dados /." São José do Rio Preto : [s.n.], 2010. http://hdl.handle.net/11449/98694.

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Orientador: Carlos Roberto Valêncio<br>Banca: Cristina Dutra de Aguiar Ciferri<br>Banca: Rogéria Cristiane Gratão de Souza<br>Resumo: O crescente avanço e a disponibilidade de recursos computacionais viabilizam o armazenamento e a manipulação de grandes bases de dados. As técnicas típicas de mineração de dados possibilitam a extração de padrões desde que os dados estejam armazenados em uma única tabela. A mineração de dados multirrelacional, por sua vez, apresenta-se como uma abordagem mais recente que permite buscar padrões provenientes de múltiplas tabelas, sendo indicada para a aplicação em

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12

Salap, Seda. "Development Of A Gis-based Monitoring And Management System For Underground Mining Safety." Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/3/12609815/index.pdf.

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Mine safety is of paramount concern to the mining industry. The generation of a Geographic Information Systems (GIS) which can administrate relevant spatial data and metadata of underground mining safety efficiently is a very vital issue in this sense. In an effort to achieve a balance of safety and productivity, GIS can contribute to the creation of a safe working environment in underground (U/G) mining. Such a system should serve to a continuous risk analysis and be designed for applications in case of emergency. Concept for safety should require three fundamental components, namely (i)

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13

Krásný, Michal. "Systém pro dolování z dat v prostředí Oracle." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2008. http://www.nusl.cz/ntk/nusl-236015.

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This MSc Project deals with the system of Knowledge Discovery in Databases. It is a client application which uses the Oracle Data Mining Server's 10.g Release 2 (10.2) services. The application is implemented in Java, the graphical user interface is built on the NetBeans Rich Client Platform. The theoretical part introduces the Knowledge Discovery in Databases, while the practical part describes functionality of the original system, it's deficiencies, documents sollutions of theese deficiencies and there are proposed improvements for further development. The goal of this project is to modify t

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14

Knobbe, Arno J. "Multi-relational data mining /." Amsterdam [u.a.] : IOS Press, 2007. http://www.loc.gov/catdir/toc/fy0709/2006931539.html.

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15

Hou, Pingyu. "Customer relationship management for banking system." CSUSB ScholarWorks, 2004. https://scholarworks.lib.csusb.edu/etd-project/2635.

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The purpose of this project is to design, build, and implement a Customer Relationship Management (CRM) system for a bank. CRM BANKING is an online application that caters to strengthening and stabilizing customer relationships in a bank.

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16

Guo, Shishan. "Data mining in crystallographic databases." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape3/PQDD_0012/NQ52854.pdf.

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17

Fešar, Marek. "Analýza dat na sociálních sítích s využitím dolování dat." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2014. http://www.nusl.cz/ntk/nusl-236086.

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The thesis presents general principles of data mining and it also focuses on specific needs of social networks. Certain social networks, chosen with respect to popularity and availability to Czech users, are discussed from various points of view. The benefits and drawbacks of each are also mentioned. Afterwards, one suitable API is selected for futher analysis. The project explains harvesting data via Twitter API and the process of mining of data from this particular network. Design of a mining algorithm inspired by density based clustering methods is described. The implementation is explained

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18

Markowetz, Alexander. "Keyword search over relational data /." View abstract or full-text, 2008. http://library.ust.hk/cgi/db/thesis.pl?CSED%202008%20MARKOW.

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19

Karlapalem, Kamalakar. "Redesign of distributed relational databases." Diss., Georgia Institute of Technology, 1992. http://hdl.handle.net/1853/9173.

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20

Spyropoluou, Eirini. "Local pattern mining in multi-relational data." Thesis, University of Bristol, 2013. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.654116.

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Multi-relational data mining has so far been synonym to methods based on Inductive Logic Programming (ILP) , which discover frequent first-order logic rules in the data. This is due to the fact that ILP conveniently captures the multi-relational structure, while there has not been a suitable pattern syntax extension of an itemset for the case of multi-relational data. Local pattern mining methods have mostly focused on mining a single relation. A common strategy for mining multi-relational data (MRD) has been to apply frequent items et mining on the join of all database relations. However, whe

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21

Moatassem, Nawal N. "A Study of Migrating Biological Data from Relational Databases to NoSQL Databases." Youngstown State University / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ysu1442486094.

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22

Fang, Cheng-Hung. "Application for data mining in manufacturing databases." Ohio : Ohio University, 1996. http://www.ohiolink.edu/etd/view.cgi?ohiou1178653424.

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23

Mulchandani, Mukesh K. "Updating XML views of relational data." Link to electronic thesis, 2003. http://www.wpi.edu/Pubs/ETD/Available/etd-0429103-200545.

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24

Chen, Jianzhong. "Probabilistic relational data mining with aggregates and hierarchies." Thesis, University of Ulster, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.407761.

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25

Cawley, Benjamin Matthew. "Automated generation of personal data reports from relational databases." Thesis, Manchester Metropolitan University, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.485987.

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This thesis presents a novel approach for extracting personal data and automatically generating Personal Data Reports (PDRs) from relational databases. Such PDRs can be used among other purposes for compliance with Subject Access Requests (SARs) of Data Protection Acts (DPAs). The proposed approach combines the use of graphs and SQL for the construction of PDRs and its rationale is based on the fact that some relations in a database, which we denote as RDS relations, hold information about Data Subjects (DSs) and relations linked around RDSs contain additional information about the particular

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26

Patki, Neha (Neha R. ). "The Synthetic Data Vault : generative modeling for relational databases." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/109616.

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Thesis: M. Eng. in Computer Science and Engineering, Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.<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 79-80).<br>The goal of this thesis is to build a system that automatically creates synthetic data for enabling data science endeavors. To meet this goal, we present the Synthetic Data Vaul

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27

Wang, Liang, and 王亮. "Frequent itemsets mining on uncertain databases." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2010. http://hub.hku.hk/bib/B4590215X.

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28

Ordońẽz, Carlos. "Mining complex databases using the EM algorithm." Diss., Georgia Institute of Technology, 2000. http://hdl.handle.net/1853/8232.

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29

Young, Barrington R. St A. "Efficient Algorithms for Data Mining with Federated Databases." University of Cincinnati / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1179332091.

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30

Pasupa, Kitsuchart. "Data mining and decision support in pharmaceutical databases." Thesis, University of Sheffield, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.487615.

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This thesis lies in the area of chemoinformatics, known as virtual screening (VS). VS describes a set of computational methods that provide a fast and cheap alternative to biological screening which involves the selection, synthesis and testing of molecules to ascertain their biological activity in a particular domain, e.g. pain relief, reduction of inflammation. This is important because reducing the cost and, crucially, time in the early stages of compound development can have a disproportionate benefit in profitability in a cycle that has a short patent lifetime. Machine learning methods ar

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31

Jiang, Fan. "Frequent pattern mining of uncertain data streams." Springer-Verlag, 2011. http://hdl.handle.net/1993/5233.

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When dealing with uncertain data, users may not be certain about the presence of an item in the database. For example, due to inherent instrumental imprecision or errors, data collected by sensors are usually uncertain. In various real-life applications, uncertain databases are not necessarily static, new data may come continuously and at a rapid rate. These uncertain data can come in batches, which forms a data stream. To discover useful knowledge in the form of frequent patterns from streams of uncertain data, algorithms have been developed to use the sliding window model for processing and

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32

Cheng, Hong. "Mining high-utility plans from plan databases /." View Abstract or Full-Text, 2003. http://library.ust.hk/cgi/db/thesis.pl?COMP%202003%20CHENG.

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Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2003.<br>Includes bibliographical references (leaves 67-69). Also available in electronic version. Access restricted to campus users.

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33

Anderson, Grant. "Random Relational Rules." The University of Waikato, 2008. http://hdl.handle.net/10289/2562.

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In the field of machine learning, methods for learning from single-table data have received much more attention than those for learning from multi-table, or relational data, which are generally more computationally complex. However, a significant amount of the world's data is relational. This indicates a need for algorithms that can operate efficiently on relational data and exploit the larger body of work produced in the area of single-table techniques. This thesis presents algorithms for learning from relational data that mitigate, to some extent, the complexity normally associated with such

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34

Ponsan, Christiane. "Computing with words for data mining." Thesis, University of Bristol, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.310744.

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35

PaÌirceÌir, RoÌnaÌn. "Knowledge discovery from distributed aggregate data in data warehouses and statistical databases." Thesis, University of Ulster, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.274398.

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36

Stanier, C. F. "A model integrity based object-relational data model and complex data model definition framework." Thesis, Staffordshire University, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.522119.

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37

Anand, Sumit. "Novel applications of data mining methodologies to incident databases." Texas A&M University, 2003. http://hdl.handle.net/1969.1/3998.

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Incident databases provide an excellent opportunity to study the repeated situations of incidents in the process industry. The databases give an insight into the situation which led to an incident, and if studied properly can help monitor the process, equipment and chemical involved more closely, and reduce the number of incidents in the future. This study examined a subset of incidents from National Response CenterÂs Incident database, focusing mainly on fixed facility incidents in Harris County, Texas from 1990 to 2002. Data mining has been used in the financial and marketing arena for many

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38

Lozano, Aparicio Jose Martin. "Data exchange from relational databases to RDF with target shape schemas." Thesis, Lille 1, 2020. http://www.theses.fr/2020LIL1I063.

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Resource Description Framework (RDF) est un modèle de graphe utilisé pour publier des données sur le Web à partir de bases de données relationnelles. Nous étudions l'échange de données depuis des bases de données relationnelles vers des graphes RDF avec des schémas de formes cibles. Essentiellement, échange de données modélise un processus de transformation d'une instance d'un schéma relationnel, appelé schéma source, en un graphe RDF contraint par un schéma cible, selon un ensemble de règles, appelé tuple source-cible générant des dépendances. Le graphe RDF obtenu est appelé une solution. Éta

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39

Ciolek, Thomas S. "Meeting the challenges of met data with MySQL X /." [Denver, Colo.] : Regis University, 2006. http://165.236.235.140/lib/TCiolek2006.pdf.

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40

Böck, Daniel. "Frequent itemset discovery using SQL and relational division." [S.l. : s.n.], 2003. http://www.bsz-bw.de/cgi-bin/xvms.cgi?SWB10678409.

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41

Hinneburg, Alexander. "Density based clustering in large databases using projections and visualizations." [S.l. : s.n.], 2002. http://deposit.ddb.de/cgi-bin/dokserv?idn=967390583.

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42

Tang, Hing-fan Anthony. "A hybrid relational data structure for virtual reality modelling /." Hong Kong : University of Hong Kong, 2001. http://sunzi.lib.hku.hk/hkuto/record.jsp?B25101249.

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43

Sarafis, Ioannis. "Data mining clustering of high dimensional databases with evolutionary algorithms." Thesis, Heriot-Watt University, 2005. http://hdl.handle.net/10399/255.

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44

Kavurucu, Yusuf. "An Ilp-based Concept Discovery System For Multi-relational Data Mining." Phd thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/12610688/index.pdf.

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Multi Relational Data Mining has become popular due to the limitations of propositional problem definition in structured domains and the tendency of storing data in relational databases. However, as patterns involve multiple relations, the search space of possible hypothesis becomes intractably complex. In order to cope with this problem, several relational knowledge discovery systems have been developed employing various search strategies, heuristics and language pattern limitations. In this thesis, Inductive Logic Programming (ILP) based concept discovery is studied and two systems based on

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45

Josefsson, André. "Comparing the performance of relational and document databases for hierarchical geospatial data." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-231884.

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The aim of this degree project is to investigate alternatives to the relational database paradigm when storing hierarchical geospatial data. The document paradigm is found suitable and is therefore further examined. A benchmark suite is developed in order to test the relative performance of the paradigms for the relevant type of data. MongoDB and Microsoft SQL Server are chosen to represent the two paradigms in the benchmark. The results indicate that the document paradigm has potential when working with hierarchical structures. When adding geospatial elements to the data, the results are inco

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46

Hines, Gary L. "From entities to objects : reverse engineering a relational data model into an object-oriented design." Virtual Press, 2000. http://liblink.bsu.edu/uhtbin/catkey/1164846.

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In many software applications, an object-oriented design (OOD) is generated first, then persistent storage is implemented by mapping the objects to a relational database. This thesis explores the "reverse engineering" of an OOD out of an existing relational data model. Findings from the current literature are presented, and a case study is undertaken using the model and research process published by GENTECH, a nonprofit organization promoting genealogical computing. The model is mapped into an OOD and captured in Unified Modeling Language (UML) class diagrams and object collaboration diagrams.

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47

鄧興汎 and Hing-fan Anthony Tang. "A hybrid relational data structure for virtual reality modelling." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2001. http://hub.hku.hk/bib/B31225184.

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48

Bourbonnais, Richard Joseph II. "Visual assessment and relational database management." Thesis, Virginia Tech, 1994. http://hdl.handle.net/10919/43671.

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Protection of the visual environment begins with a comprehensive documentation and evaluation of existing conditions followed by the development of guidelines pertaining to future alterations. This thesis examines existing methods of visual assessment and the needs of the land planner for the purpose of understanding the necessary components of evaluating the visual environment effectively. The objective has been to develop a new method of visual documentation and evaluation that can be utilized by land planners for the visual assessment of road corridors. In order to achieve this objective,

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49

MacKinnon, Richard Kyle. "Seeing the forest for the trees: tree-based uncertain frequent pattern mining." Springer International Publishing, 2014. http://hdl.handle.net/1993/31059.

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Many frequent pattern mining algorithms operate on precise data, where each data point is an exact accounting of a phenomena (e.g., I have exactly two sisters). Alas, reasoning this way is a simplification for many real world observations. Measurements, predictions, environmental factors, human error, &ct. all introduce a degree of uncertainty into the mix. Tree-based frequent pattern mining algorithms such as FP-growth are particularly efficient due to their compact in-memory representations of the input database, but their uncertain extensions can require many more tree nodes. I propose new

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50

Ramani, Ramasubramanian. "A toolkit for managing XML data with a relational database management system." [Gainesville, Fla.] : University of Florida, 2001. http://etd.fcla.edu/etd/uf/2001/anp1308/Thesis.pdf.

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Thesis (M.S.)--University of Florida, 2001.<br>Title from first page of PDF file. Document formatted into pages; contains x, 54 p.; also contains graphics. Vita. Includes bibliographical references (p. 50-53).

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