Dissertations / Theses on the topic 'Data quality'
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Grillo, Aderibigbe. "Developing a data quality scorecard that measures data quality in a data warehouse." Thesis, Brunel University, 2018. http://bura.brunel.ac.uk/handle/2438/17137.
Full textSýkorová, Veronika. "Data Quality Metrics." Master's thesis, Vysoká škola ekonomická v Praze, 2008. http://www.nusl.cz/ntk/nusl-2815.
Full textYu, Wenyuan. "Improving data quality : data consistency, deduplication, currency and accuracy." Thesis, University of Edinburgh, 2013. http://hdl.handle.net/1842/8899.
Full textPeralta, Veronika. "Data Quality Evaluation in Data Integration Systems." Phd thesis, Université de Versailles-Saint Quentin en Yvelines, 2006. http://tel.archives-ouvertes.fr/tel-00325139.
Full textPeralta, Costabel Veronika del Carmen. "Data quality evaluation in data integration systems." Versailles-St Quentin en Yvelines, 2006. http://www.theses.fr/2006VERS0020.
Full textCette thèse porte sur la qualité des données dans les Systèmes d’Intégration de Données (SID). Nous nous intéressons, plus précisément, aux problèmes de l’évaluation de la qualité des données délivrées aux utilisateurs en réponse à leurs requêtes et de la satisfaction des exigences des utilisateurs en terme de qualité. Nous analysons également l’utilisation de mesures de qualité pour l’amélioration de la conception du SID et la conséquente amélioration de la qualité des données. Notre approche consiste à étudier un facteur de qualité à la fois, en analysant sa relation avec le SID, en proposant des techniques pour son évaluation et en proposant des actions pour son amélioration. Parmi les facteurs de qualité qui ont été proposés, cette thèse analyse deux facteurs de qualité : la fraîcheur et l’exactitude des données
Deb, Rupam. "Data Quality Enhancement for Traffic Accident Data." Thesis, Griffith University, 2017. http://hdl.handle.net/10072/367725.
Full textThesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Information and Communication Technology
Science, Environment, Engineering and Technology
Full Text
He, Ying Surveying & Spatial Information Systems Faculty of Engineering UNSW. "Spatial data quality management." Publisher:University of New South Wales. Surveying & Spatial Information Systems, 2008. http://handle.unsw.edu.au/1959.4/43323.
Full textRedgert, Rebecca. "Evaluating Data Quality in a Data Warehouse Environment." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-208766.
Full textMängden data som ackumulerats av organisationer har ökat betydligt under de senaste åren, vilket har ökat betydelsen för datakvalitet. Att säkerställa datakvalitet för stora mängder data är en komplicerad uppgift, men avgörande för efterföljande analys. Denna studie undersöker hur man underhåller och förbättrar datakvaliteten i ett datalager. En fallstudie av fel i ett datalager på det svenska företaget Kaplan genomfördes och resulterade i riktlinjer för hur datakvaliteten kan förbättras. Undersökningen gjordes genom att manuellt jämföra data från källsystemen med datat integrerat i datalagret och genom att tillämpa ett kvalitetsramverk grundat på semiotisk teori för att kunna identifiera fel. De tre huvudsakliga riktlinjerna som gavs är att (1) implementera ett standardiserat format för källdatat, (2) genomföra en kontroll före integration där källdatat granskas och korrigeras vid behov, och (3) att skapa och implementera specifika databasintegritetsregler. Vidare forskning uppmuntras för att skapa en guide till ramverket om hur man bäst jämför data genom en manuell undersökning, och kvalitetssäkring av källdata.
Bringle, Per. "Data Quality in Data Warehouses: a Case Study." Thesis, University of Skövde, Department of Computer Science, 1999. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-404.
Full textCompanies today experience problems with poor data quality in their systems. Because of the enormous amount of data in companies, the data has to be of good quality if companies want to take advantage of it. Since the purpose with a data warehouse is to gather information from several databases for decision support, it is absolutely vital that data is of good quality. There exists several ways of determining or classifying data quality in databases. In this work the data quality management in a large Swedish company's data warehouse is examined, through a case study, using a framework specialized for data warehouses. The quality of data is examined from syntactic, semantic and pragmatic point of view. The results of the examination is then compared with a similar case study previously conducted in order to find any differences and similarities.
Li, Lin. "Data quality and data cleaning in database applications." Thesis, Edinburgh Napier University, 2012. http://researchrepository.napier.ac.uk/Output/5788.
Full textWad, Charudatta V. "QoS : quality driven data abstraction for large databases." Worcester, Mass. : Worcester Polytechnic Institute, 2008. http://www.wpi.edu/Pubs/ETD/Available/etd-020508-151213/.
Full textKara, Madjid. "Data quality for the decision of the ambient systems." Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLV009.
Full textData quality is a common condition to all information technology projects; it has become a complex research domain with the multiplicity and expansion of different data sources. Researchers have studied the axis of modeling and evaluating data, several approaches have been proposed but they are limited to a specific use field and did not offer a quality profile enabling us to evaluate a global quality model. The evaluation based on ISO quality models has emerged; however, these models do not guide us for their use, having to adapt them to each scenario without precise methods. Our work focuses on the data quality issues of an ambient system where the time constraints for decision-making is greater compared to traditional applications. The main objective is to provide the decision-making system with a very specific view of the sensors data quality. We identify the quantifiable aspects of sensors data to link them to the appropriate metrics of our specified data quality model. Our work presents the following contributions: (i) creating a generic data quality model based on several existing data quality standards, (ii) formalizing the data quality models under an ontology, which allows integrating them (of i) by specifying various links, named equivalence relations between the criteria composing these models, (iii) proposing an instantiation algorithm to extract the specified data quality model from the generic data quality models, (iv) proposing a global evaluation approach of the specified data quality model using two processes, the first one consists in executing the metrics based on sensors data and the second one recovers the result of the first process and uses the concept of fuzzy logic to evaluate the factors of our specified data quality model. Then, the expert defines weight values based on the interdependence table of the model to take account the interaction between criteria and use the aggregation procedure to get a degree of confidence value. Based on the final result, the decisional component makes an analysis to make a decision
Barker, James M. "Data governance| The missing approach to improving data quality." Thesis, University of Phoenix, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10248424.
Full textIn an environment where individuals use applications to drive activities from what book to purchase, what film to view, to what temperature to heat a home, data is the critical element. To make things work data must be correct, complete, and accurate. Many firms view data governance as a panacea to the ills of systems and organizational challenge while other firms struggle to generate the value of these programs. This paper documents a study that was executed to understand what is being done by firms in the data governance space and why? The conceptual framework that was established from the literature on the subject was a set of six areas that should be addressed for a data governance program including: data governance councils; data quality; master data management; data security; policies and procedures; and data architecture. There is a wide range of experiences and ways to address data quality and the focus needs to be on execution. This explanatory case study examined the experiences of 100 professionals at 41 firms to understand what is being done and why professionals are undertaking such an endeavor. The outcome is that firms need to address data quality, data security, and operational standards in a manner that is organized around business value including strong business leader sponsorship and a documented dynamic business case. The outcome of this study provides a foundation for data governance program success and a guide to getting started.
Wolf, Hilke. "Data Quality Bench-Marking for High Resolution Bragg Data." Doctoral thesis, Niedersächsische Staats- und Universitätsbibliothek Göttingen, 2014. http://hdl.handle.net/11858/00-1735-0000-0022-5DE2-A.
Full textSwapna, B., and R. VijayaPrakash. "Privacy Preserving Data Mining Operations without Disrupting Data Quality." International Journal of Computer Science and Network (IJCSN), 2012. http://hdl.handle.net/10150/271473.
Full textData mining operations help discover business intelligence from historical data. The extracted business intelligence or actionable knowledge helps in taking well informed decisions that leads to profit to the organization that makes use of it. While performing mining privacy of data has to be given utmost importance. To achieve this PPDM (Privacy Preserving Data Mining) came into existence by sanitizing database that prevents discovery of association rules. However, this leads to modification of data and thus disrupting the quality of data. This paper proposes a new technique and algorithms that can perform privacy preserving data mining operations while ensuring that the data quality is not lost. The empirical results revealed that the proposed technique is useful and can be used in real world applications.
Ma, Shuai. "Extending dependencies for improving data quality." Thesis, University of Edinburgh, 2011. http://hdl.handle.net/1842/5045.
Full textAngeles, Maria del Pilar. "Management of data quality when integrating data with known provenance." Thesis, Heriot-Watt University, 2007. http://hdl.handle.net/10399/64.
Full textDiallo, Thierno Mahamoudou. "Discovering data quality rules in a master data management context." Thesis, Lyon, INSA, 2013. http://www.theses.fr/2013ISAL0067.
Full textDirty data continues to be an important issue for companies. The datawarehouse institute [Eckerson, 2002], [Rockwell, 2012] stated poor data costs US businesses $611 billion dollars annually and erroneously priced data in retail databases costs US customers $2.5 billion each year. Data quality becomes more and more critical. The database community pays a particular attention to this subject where a variety of integrity constraints like Conditional Functional Dependencies (CFD) have been studied for data cleaning. Repair techniques based on these constraints are precise to catch inconsistencies but are limited on how to exactly correct data. Master data brings a new alternative for data cleaning with respect to it quality property. Thanks to the growing importance of Master Data Management (MDM), a new class of data quality rule known as Editing Rules (ER) tells how to fix errors, pointing which attributes are wrong and what values they should take. The intuition is to correct dirty data using high quality data from the master. However, finding data quality rules is an expensive process that involves intensive manual efforts. It remains unrealistic to rely on human designers. In this thesis, we develop pattern mining techniques for discovering ER from existing source relations with respect to master relations. In this set- ting, we propose a new semantics of ER taking advantage of both source and master data. Thanks to the semantics proposed in term of satisfaction, the discovery problem of ER turns out to be strongly related to the discovery of both CFD and one-to-one correspondences between sources and target attributes. We first attack the problem of discovering CFD. We concentrate our attention to the particular class of constant CFD known as very expressive to detect inconsistencies. We extend some well know concepts introduced for traditional Functional Dependencies to solve the discovery problem of CFD. Secondly, we propose a method based on INclusion Dependencies to extract one-to-one correspondences from source to master attributes before automatically building ER. Finally we propose some heuristics of applying ER to clean data. We have implemented and evaluated our techniques on both real life and synthetic databases. Experiments show both the feasibility, the scalability and the robustness of our proposal
Gens, Rüdiger. "Quality assessment of SAR interferometric data." Hannover : Fachrichtung Vermessungswesen der Univ, 1998. http://deposit.ddb.de/cgi-bin/dokserv?idn=95607121X.
Full textBerg, Marcus. "Evaluating Quality of Online Behavior Data." Thesis, Stockholms universitet, Statistiska institutionen, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-97524.
Full textKim, Jin Mo. "Name matching for data quality mediator." Thesis, Massachusetts Institute of Technology, 1995. http://hdl.handle.net/1721.1/36588.
Full textViklund, Adam. "Data Quality Study of AMR Systems." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-269465.
Full textAljumaili, Mustafa. "Data Quality Assessment : Applied in Maintenance." Doctoral thesis, Luleå tekniska universitet, Drift, underhåll och akustik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-26088.
Full textEdwards, Matthew. "Data quality measures for identity resolution." Thesis, Lancaster University, 2018. http://eprints.lancs.ac.uk/124402/.
Full textZhu, Zhaochen. "Computational methods in air quality data." HKBU Institutional Repository, 2017. https://repository.hkbu.edu.hk/etd_oa/402.
Full textNitesh, Varma Rudraraju Nitesh, and Boyanapally Varun Varun. "Data Quality Model for Machine Learning." Thesis, Blekinge Tekniska Högskola, Institutionen för programvaruteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-18498.
Full textIssa, Subhi. "Linked data quality : completeness and conciseness." Electronic Thesis or Diss., Paris, CNAM, 2019. http://www.theses.fr/2019CNAM1274.
Full textThe wide spread of Semantic Web technologies such as the Resource Description Framework (RDF) enables individuals to build their databases on the Web, to write vocabularies, and define rules to arrange and explain the relationships between data according to the Linked Data principles. As a consequence, a large amount of structured and interlinked data is being generated daily. A close examination of the quality of this data could be very critical, especially, if important research and professional decisions depend on it. The quality of Linked Data is an important aspect to indicate their fitness for use in applications. Several dimensions to assess the quality of Linked Data are identified such as accuracy, completeness, provenance, and conciseness. This thesis focuses on assessing completeness and enhancing conciseness of Linked Data. In particular, we first proposed a completeness calculation approach based on a generated schema. Indeed, as a reference schema is required to assess completeness, we proposed a mining-based approach to derive a suitable schema (i.e., a set of properties) from data. This approach distinguishes between essential properties and marginal ones to generate, for a given dataset, a conceptual schema that meets the user's expectations regarding data completeness constraints. We implemented a prototype called “LOD-CM” to illustrate the process of deriving a conceptual schema of a dataset based on the user's requirements. We further proposed an approach to discover equivalent predicates to improve the conciseness of Linked Data. This approach is based, in addition to a statistical analysis, on a deep semantic analysis of data and on learning algorithms. We argue that studying the meaning of predicates can help to improve the accuracy of results. Finally, a set of experiments was conducted on real-world datasets to evaluate our proposed approaches
Sehat, Mahdis, and FLORES RENÉ PAVEZ. "Customer Data Management." Thesis, KTH, Industriell ekonomi och organisation (Avd.), 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-109251.
Full textLandelius, Cecilia. "Data governance in big data : How to improve data quality in a decentralized organization." Thesis, KTH, Industriell ekonomi och organisation (Inst.), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-301258.
Full textDen ökade användningen av internet har ökat mängden data som finns tillgänglig och mängden data som samlas in. Företag påbörjar därför initiativ för att analysera dessa stora mängder data för att få ökad förståelse. Dock är värdet av analysen samt besluten som baseras på analysen beroende av kvaliteten av den underliggande data. Av denna anledning har datakvalitet blivit en viktig fråga för företag. Misslyckanden i datakvalitetshantering är ofta på grund av organisatoriska aspekter. Eftersom decentraliserade organisationsformer blir alltmer populära, finns det ett behov av att förstå hur en decentraliserad organisation kan arbeta med frågor som datakvalitet och dess förbättring. Denna uppsats är en kvalitativ studie av ett företag inom logistikbranschen som i nuläget genomgår ett skifte till att bli datadrivna och som har problem med att underhålla sin datakvalitet. Syftet med denna uppsats är att besvara frågorna: • RQ1: Vad är datakvalitet i sammanhanget logistikdata? • RQ2: Vilka är hindren för att förbättra datakvalitet i en decentraliserad organisation? • RQ3: Hur kan dessa hinder överkommas? Flera datakvalitetsdimensioner identifierades och kategoriserades som kritiska problem, problem och icke-problem. Från den insamlade informationen fanns att dimensionerna, kompletthet, exakthet och konsekvens var kritiska datakvalitetsproblem för företaget. De tre mest förekommande hindren för att förbättra datakvalité var dataägandeskap, standardisering av data samt att förstå vikten av datakvalitet. För att överkomma dessa hinder är de viktigaste åtgärderna att skapa strukturer för dataägandeskap, att implementera praxis för hantering av datakvalitet samt att ändra attityden hos de anställda gentemot datakvalitet till en datadriven attityd. Generaliseringsbarheten av en enfallsstudie är låg. Dock medför denna studie flera viktiga insikter och trender vilka kan användas för framtida studier och för företag som genomgår liknande transformationer.
Huang, Shiping. "Exploratory visualization of data with variable quality." Link to electronic thesis, 2005. http://www.wpi.edu/Pubs/ETD/Available/etd-01115-225546/.
Full textDill, Robert W. "Data warehousing and data quality for a Spatial Decision Support System." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1997. http://handle.dtic.mil/100.2/ADA336886.
Full textThesis advisors, Daniel R. Dolk, George W. Thomas, and Kathryn Kocher. Includes bibliographical references (p. 203-206). Also available online.
Reinert, Olof, and Tobias Wiesinger. "DATA QUALITY CONSEQUENCES OF MANDATORY CYBER DATA SHARING BETWEEN DUOPOLY INSURERS." Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-175180.
Full textAlkharboush, Nawaf Abdullah H. "A data mining approach to improve the automated quality of data." Thesis, Queensland University of Technology, 2014. https://eprints.qut.edu.au/65641/1/Nawaf%20Abdullah%20H_Alkharboush_Thesis.pdf.
Full textSPAHIU, BLERINA. "Profiling Linked Data." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2017. http://hdl.handle.net/10281/151645.
Full textRecently, the increasing diffusion of Linked Data (LD) as a standard way to publish and structure data on the Web has received a growing attention from researchers and data publishers. LD adoption is reflected in different domains such as government, media, life science, etc., building a powerful Web available to anyone. Despite the high number of datasets published as LD, their usage is still not exploited as they lack comprehensive metadata. Data consumers need to obtain information about datasets content in a fast and summarized form to decide if they are useful for their use case at hand or not. Data profiling techniques offer an efficient solution to this problem as they are used to generate metadata and statistics that describe the content of the dataset. Existing profiling techniques do no cover a wide range of use cases. Many challenges due to the heterogeneity nature of Linked Data are still to overcome. This thesis presents the doctoral research which tackles the problems related to Profiling Linked Data. Even though the term of data profiling is the umbrella term for diverse descriptive information that describes a dataset, in this thesis we cover three aspects of profiling; topic-based, schema-based and linkage-based. The profile provided in this thesis is fundamental for the decision-making process and is the basic requirement towards the dataset understanding. In this thesis we present an approach to automatically classify datasets in one of the topical categories used in the LD cloud. Moreover, we investigate the problem of multi-topic profiling. For the schema-based profiling we propose a schema-based summarization approach, that provides an overview about the relations in the data. Our summaries are concise and informative enough to summarize the whole dataset. Moreover, they reveal quality issues and can help users in the query formulation tasks. Many datasets in the LD cloud contain similar information for the same entity. In order to fully exploit its potential LD should made this information explicit. Linkage profiling provides information about the number of equivalent entities between datasets and reveal possible errors. The techniques of profiling developed during this work are automatic and can be applied to different datasets independently of the domain.
Cui, Qingguang. "Measuring data abstraction quality in multiresolution visualizations." Worcester, Mass. : Worcester Polytechnic Institute, 2007. http://www.wpi.edu/Pubs/ETD/Available/etd-041107-224152/.
Full textMueller, G. "Data Consistency Checks on Flight Test Data." International Foundation for Telemetering, 2014. http://hdl.handle.net/10150/577405.
Full textThis paper reflects the principal results of a study performed internally by Airbus's flight test centers. The purpose of this study was to share the body of knowledge concerning data consistency checks between all Airbus business units. An analysis of the test process is followed by the identification of the process stakeholders involved in ensuring data consistency. In the main part of the paper several different possibilities for improving data consistency are listed; it is left to the discretion of the reader to determine the appropriateness these methods.
Schmidt, Sven. "Quality of service aware data stream processing." Doctoral thesis, [S.l.] : [s.n.], 2007. http://deposit.ddb.de/cgi-bin/dokserv?idn=983780625.
Full textTardif, Geneviève. "Multivariate Analysis of Canadian Water Quality Data." Thesis, Université d'Ottawa / University of Ottawa, 2015. http://hdl.handle.net/10393/32245.
Full textSmith, Sonya K. "Assessing the quality of deep seismic data." Thesis, University of Cambridge, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.361690.
Full textKaramancı, Kaan. "Exploratory data analysis for preemptive quality control." Thesis, Massachusetts Institute of Technology, 2009. http://hdl.handle.net/1721.1/53126.
Full textIncludes bibliographical references (p. 113).
In this thesis, I proposed and implemented a methodology to perform preemptive quality control on low-tech industrial processes with abundant process data. This involves a 4 stage process which includes understanding the process, interpreting and linking the available process parameter and quality control data, developing an exploratory data toolset and presenting the findings in a visual and easily implementable fashion. In particular, the exploratory data techniques used rely on visual human pattern recognition through data projection and machine learning techniques for clustering. The presentation of finding is achieved via software that visualizes high dimensional data with Chernoff faces. Performance is tested on both simulated and real industry data. The data obtained from a company was not suitable, but suggestions on how to collect suitable data was given.
by Kaan Karamancı.
M.Eng.
Schnetzer, Matthias, Franz Astleithner, Predrag Cetkovic, Stefan Humer, Manuela Lenk, and Mathias Moser. "Quality Assessment of Imputations in Administrative Data." De Gruyter, 2015. http://dx.doi.org/10.1515/JOS-2015-0015.
Full textLaw, Eugene L. "CORRELATION BETWEEN TAPE DROPOUTS AND DATA QUALITY." International Foundation for Telemetering, 1990. http://hdl.handle.net/10150/613460.
Full textThis paper will present the results of a study to correlate tape dropouts and data quality. A tape dropout is defined in the Telemetry Standards as “a reproduced signal of abnormally 1 low amplitude caused by tape imperfections severe enough to produce a data error” Bit errors were chosen as the measure of data quality. Signals were recorded on several tracks of a wideband analog instrumentation magnetic tape recorder. The tape tracks were 50 mils wide. The signal characteristics were analyzed when bit errors or low reproduce amplitudes were detected.
Nilsson, Petter. "Improving Data Quality in Swedbank Swedish DataWarehouse." Thesis, Umeå universitet, Institutionen för datavetenskap, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-128236.
Full textVeiga, Allan Koch. "A conceptual framework on biodiversity data quality." Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/3/3141/tde-17032017-085248/.
Full textA crescente disponibilização de dados digitalizados sobre a biodiversidade em todo o mundo, fornecidos por um crescente número de fontes, e o aumento da utilização desses dados para uma variedade de propósitos, tem gerado preocupações relacionadas a \"adequação ao uso\" desses dados e ao impacto da qualidade de dados (QD) sobre resultados de análises, relatórios e tomada de decisões. Uma abordagem consistente para avaliar e gerenciar a QD é atualmente crítica para usuários de dados sobre a biodiversidade. No entanto, atingir esse objetivo tem sido particularmente desafiador devido à idiossincrasia inerente ao conceito de qualidade. A avaliação e a gestão da QD não podem ser adequadamente realizadas sem definir claramente o significado de qualidade de acordo com o ponto de vista do usuário dos dados. Esta tese apresenta um arcabouço conceitual formal para apoiar a comunidade de Informática para Biodiversidade (IB) a descrever consistentemente o significado de \"adequação ao uso\" de dados. Princípios relacionados à adequação ao uso são usados para estabelecer uma base formal e comum para a definição colaborativa de necessidades, soluções e relatórios de QD úteis para a avaliação e gestão de QD. Baseado no estudo do domínio de QD e sua contextualização no domínio de IB, que envolveu discussões com especialistas em QD e IB em um processo iterativo, foi projetado e formalizado um arcabouço conceitual abrangente. Ele define oito conceitos fundamentais e vinte e um conceitos derivados organizados em três classes: Necessidades de QD, Soluções de QD e Relatório de QD. Os conceitos de cada classe descrevem, respectivamente, o significado de QD em um dado contexto, métodos e ferramentas que podem servir como soluções para atender necessidades de QD, e relatórios que apresentam o estado atual da qualidade de um recurso de dado. A formalização do arcabouço foi apresentada usando notação de mapas conceituais e notação de teoria dos conjuntos. Para a validação do arcabouço, nós apresentamos uma prova de conceito baseada em um estudo de caso conduzido no Museu de Zoologia Comparativa da Universidade de Harvard. As ferramentas FP-Akka Kurator e BDQ Toolkit foram usadas no estudo de caso para realizar medidas, validações e melhorias da QD em um conjunto de dados da Coleção de Insetos Hasbrouck da Universidade do Estado do Arizona. Os resultados ilustram como o arcabouço permite a usuários de dados avaliarem e gerenciarem a QD de conjunto de dados e registros isolados usando as abordagens de controle de qualidade a garantia de qualidade. A prova de conceito demonstrou que o arcabouço é adequadamente formalizado e flexível, e suficientemente completo para definir necessidades, soluções e relatórios de QD no domínio da IB. O arcabouço é capaz de formalizar o pensamento humano em componentes bem definidos para fazer possível compartilhar e reutilizar definições de QD em diferentes cenários, descrever e encontrar ferramentas de QD e comunicar o estado atual da qualidade dos dados em um formato padronizado entre as partes interessadas da comunidade de IB. Além disso, o arcabouço apoia atores da comunidade de IB a unirem esforços na identificação e desenvolvimento colaborativo de componentes necessários para a avaliação e gestão da QD. O arcabouço é também o fundamento de um Grupos de Trabalho em Qualidade de Dados, sob os auspícios do Biodiversity Information Standard (TDWG) e do Biodiversity Information Facility (GBIF) e está sendo utilizado para coletar as necessidades de qualidade de dados de usuários de dados de agrobiodiversidade e de modelagem de distribuição de espécies, inicialmente. Em trabalhos futuros, planejamos usar o arcabouço apresentado para engajar a comunidade de IB para formalizar e compartilhar perfis de QD relacionados a inúmeros outros usos de dados, recomendar métodos, diretrizes, protocolos, esquemas de metadados e vocabulários controlados para apoiar a avaliação e gestão da adequação ao uso de dados em ambiente de sistemas e dados distribuídos. Além disso, nós planejamos construir uma plataforma baseada no arcabouço para servir como uma central integrada comum para o registro e recuperação de conceitos de QD, tais como perfis, métodos, ferramentas e relatórios de QD.
Schmidt, Sven. "Quality-of-Service-Aware Data Stream Processing." Doctoral thesis, Technische Universität Dresden, 2006. https://tud.qucosa.de/id/qucosa%3A23955.
Full textHillhouse, Linden, and Ginette Blackhart. "Data Quality: Does Time of Semester Matter?" Digital Commons @ East Tennessee State University, 2019. https://dc.etsu.edu/asrf/2019/schedule/84.
Full textSilberbauer, Michael John. "Methods for visualising complex water quality data." Doctoral thesis, University of Cape Town, 2009. http://hdl.handle.net/11427/12148.
Full textIncludes bibliographical references (leaves 157-173).
The quality of South Africa’s over-stretched water resources is a matter of concern for all who depend on them for their survival and prosperity, so access to the relevant monitoring data is essential. Visualisation is a powerful method for analysing these data and communicating the results, because it unloads complex cognitive processes from the fairly restricted human numerical processing structures onto the highly developed visual perception system. Developments in the field of visualisation during the past two decades have yielded many practical methods that are applicable to the analysis and presentation of water quality data. Judicious use of visualisation aids aquatic scientists, water resource managers and ordinary consumers in assessing the quality of their water and deciding on remedial measures. To provide some insight into the possibilities of visualisation techniques, I analyse and discuss five visual methods that I have developed or contributed to: multivariate time-series inventory plots; multivariate map symbols; spatially-referenced inventory of water quality data; mass transfer summary plots; and the use of visual methods in communicating the ecological status of rivers to a wide audience.
RULA, ANISA. "Time-related quality dimensions in linked data." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2014. http://hdl.handle.net/10281/81717.
Full textPozzoli, Alice. "Data and quality metadata for continuous fields." Lyon, INSA, 2008. http://theses.insa-lyon.fr/publication/2008ISAL0024/these.pdf.
Full textLe sujet principal de ma thèse est le traitement des données en géomatique allant de l’acquisition des données photogrammétriques à la représentation cartographique. L’objectif de ma recherche est ainsi l’utilisation des techniques statistiques pour le traitement des données géomatiques afin de créer des modèles numériques des terrains en partant des données photogrammetriques. La fonction principale de la Photogrammétrie est la transformation des données en partant de l’espace-image à l’espace-objet. Nous avons proposé une solution pratique pour l’orientation automatique à partir de trois images. Cette méthodologie d’orientation présente de nombreux avantages pour les applications environnementales et de surveillance, et elle est un puissant instrument que l’on peut utiliser à côté de méthodologies plus traditionnelles. Parmi diverses applications possibles, on a choisi de construire le relief d’un modèle hydraulique 3D qui représente la confluence de deux torrents dans une région montagneuse. D’un point de vue informatique, nous avons proposé une description de données photogrammétriques basée sur le format XML pour les données géographiques (extension de GML, Geographic Markup Language). L’objectif est d’optimiser l’archivage et la gestion des données géomatiques. Enfin, un logiciel original a été produit, qui permet de modéliser les terrains en utilisant la photogrammétrie à trois images
Mehanna, Souheir. "Data quality issues in mobile crowdsensing environments." Electronic Thesis or Diss., université Paris-Saclay, 2023. http://www.theses.fr/2023UPASG053.
Full textMobile crowdsensing has emerged as a powerful paradigm for harnessing the collective sensing capabilities of mobile devices to gather diverse data in real-world settings. However, ensuring the quality of the collected data in mobile crowdsensing environments (MCS) remains a challenge because low-cost nomadic sensors can be prone to malfunctions, faults, and points of failure. The quality of the collected data can significantly impact the results of the subsequent analyses. Therefore, monitoring the quality of sensor data is crucial for effective analytics.In this thesis, we have addressed some of the issues related to data quality in mobile crowdsensing environments. First, we have explored issues related to data completeness. The mobile crowdsensing context has specific characteristics that are not all captured by the existing factors and metrics. We have proposed a set of quality factors of data completeness suitable for mobile crowdsensing environments. We have also proposed a set of metrics to evaluate each of these factors. In order to improve data completeness, we have tackled the problem of generating missing values.Existing data imputation techniques generate missing values by relying on existing measurements without considering the disparate quality levels of these measurements. We propose a quality-aware data imputation approach that extends existing data imputation techniques by taking into account the quality of the measurements.In the second part of our work, we have focused on anomaly detection, which is another major problem that sensor data face. Existing anomaly detection approaches use available data measurements to detect anomalies, and are oblivious of the quality of the measurements. In order to improve the detection of anomalies, we propose an approach relying on clustering algorithms that detects pattern anomalies while integrating the quality of the sensor into the algorithm.Finally, we have studied the way data quality could be taken into account for analyzing sensor data. We have proposed some contributions which are the first step towards quality-aware sensor data analytics, which consist of quality-aware aggregation operators, and an approach that evaluates the quality of a given aggregate considering the data used in its computation