Reconciliação é o processo de prover uma visão consistente de dados provenientes de várias fontes de dados. Embora existam na literatura trabalhos voltados à proposta de soluções de reconciliação baseadas em colaboração assíncrona, o desafio de reconciliar dados quando vários usuários colaborativos trabalham de forma assíncrona sobre as mesmas co´pias locais de dados, compartilhando somente eventualmente as suas decisões de integração particulares, tem recebido menos atenção. Nesta tese de doutorado investiga-se esse desafio, por meio da proposta do modelo AcCORD (Asynchronous COllaborative data ReconcIliation moDel). AcCORD é um modelo colaborativo assíncrono para reconciliação de dados no qual as atualizações dos usuários são mantidas em um repositório de operações na forma de dados de procedência. Cada usuário tem o seu próprio repositório para armazenar a procedência e a sua própria cópia das fontes. Ou seja, quando inconsistências entre fontes importadas são detectadas, o usuário pode tomar decisões de integração para resolvê-las de maneira autônoma, e as atualizações que são executadas localmente são registradas em seu próprio repositório. As atualizações são compartilhadas entre colaboradores quando um usuário importa as operações dos repositórios dos demais usuários. Desde que diferentes usuários podem ter diferentes pontos de vista para resolver o mesmo conflito, seus repositórios podem estar inconsistentes. Assim, o modelo Ac- CORD também inclui a proposta de diferentes políticas de reconciliação multiusuário para resolver conflitos entre repositórios. Políticas distintas podem ser aplicadas por diferentes usuários para reconciliar as suas atualizações. Dependendo da política aplicada, a visão final das fontes importadas pode ser a mesma para todos os usuários, ou seja, um única visão global integrada, ou resultar em distintas visões locais para cada um deles. Adicionalmente, o modelo AcCORD também incorpora um método de propagação de decisões de integração, o qual tem como objetivo evitar que um usuário tome decisões inconsistentes a respeito de um mesmo conflito de dado presente em diferentes fontes, garantindo um processo de reconciliação multiusuário mais efetivo. O modelo AcCORD foi validado por meio de testes de desempenho que avaliaram as políticas propostas, e por entrevistas a usuários que avaliaram não somente as políticas propostas mas também a qualidade da reconciliação multiusuário. Os resultados obtidos demonstraram a eficiência e a eficácia do modelo proposto, além de sua flexibilidade para gerar uma visão integrada ou distintas visões locais. As entrevistas realizadas demonstraram diferentes percepções dos usuários quanto à qualidade do resultado provido pelo modelo AcCORD, incluindo aspectos relacionados à consistência, aceitabilidade, corretude, economia de tempo e satisfacão. / Reconciliation is the process of providing a consistent view of the data imported from different sources. Despite some efforts reported in the literature for providing data reconciliation solutions with asynchronous collaboration, the challenge of reconciling data when multiple users work asyn- chronously over local copies of the same imported data has received less attention. In this thesis we investigate this challenge. We propose AcCORD, an asynchronous collaborative data reconciliation model. It stores users’ integration decision in logs, called repositories. Repositories keep data prove- nance, that is, the operations applied to the data sources that led to the current state of the data. Each user has her own repository for storing the provenance. That is, whenever inconsistencies among im- ported sources are detected, the user may autonomously take decisions to solve them, and integration decisions that are locally executed are registered in her repository. Integration decisions are shared among collaborators by importing each other’s repositories. Since users may have different points of view, repositories may also be inconsistent. Therefore, AcCORD also introduces several policies that can be applied by different users in order to solve conflicts among repositories and reconcile their integration decisions. Depending on the applied policy, the final view of the imported sources may either be the same for all users, that is, a single integrated view, or result in distinct local views for each of them. Furthermore, AcCORD encompasses a decision integration propagation method, which is aimed to avoid that a user take inconsistent decisions over the same data conflict present in different sources, thus guaranteeing a more effective reconciliation process. AcCORD was validated through performance tests that investigated the proposed policies and through users’ interviews that investigated not only the proposed policies but also the quality of the multiuser reconciliation. The re- sults demonstrated the efficiency and efficacy of AcCORD, and highlighted its flexibility to generate a single integrated view or different local views. The interviews demonstrated different perceptions of the users with regard to the quality of the result provided by AcCORD, including aspects related to consistency, acceptability, correctness, time-saving and satisfaction.

Metabolomics is the study of metabolites and metabolic processes. Due to the diversity of structures and polarities of metabolites, no single analytical technique is able to measure the entire metabolome - instead a varied set of experimental designs and instrumental technologies are used to measure specific portions. This has led to the development of many distinct data analysis and processing methods and software. There is hope that metabolomics can be utilized for clinical applications, in toxicology and to measure the exposome. However, for these applications to be realised data must be high quality, sufficiently standardised and annotated, and FAIR (Findable, Accessible, Interoperable and Reproducible). For this purpose, it is also important that standardised, FAIR software workflows are available. There has also recently been much concern over the reproducibility of scientific research, which FAIR and open data, and workflows can help to address. To this end, this thesis aims to assess current practices and standards of sharing data within the field of metabolomics, using metascientific approaches. The types of functions of software for processing and analysing metabolomics data is also assessed. Reporting standards are designed to ensure that the minimum information required to un- derstand and interpret the results of analysis are reported. However, poor reporting standards are ignored and not complied with. Compliance to the biological context Metabolomics Standards Initiative (MSI) guidelines was examined, in order to investigate their timeliness. The state of open data within the metabolomics community was examined by investigating how much publicly available metabolomics data there is and where has it been deposited. To explore whether journal data sharing policies are driving open metabolomics data, which journals publish articles that have their underlying data made open was also examined. However, open data alone is not inherently useful: if data is incomplete, lacking in quality or missing crucial metadata, it is not valuable. Conversely, if data are reused, this can demonstrate the worth of public data archiving. Levels of reuse of public metabolomics data were therefore examined. With greater than 250 software tools specific for metabolomics, practitioners are faced with a daunting task to select the best tools for data collection and analysis. To help educate researchers about what software is available, a taxonomy of metabolomics software tools and a GitHub pages wiki, which provides extensive details about all included software, have been developed.

Abstract The current practice for information sharing with building information modelling (BIM) is distributed data sharing based on conversions. Conversions are problematic due to data loss, redundancy, and conflicting information. The hypotheses of this research were that i) a conversion-free data exchange is a feasible approach for BIM, ii) benefits can be achieved with a conversion-free information sharing, and iii) no impediment in principle exists for wider industrial use. The use of a single data schema by all applications is a requisite for a conversion-free data collaboration. For enabling the free evolution of the data content, a version free data schema is necessary. A model arrangement implementing partial models is needed for the growing size of models. A single shared model approach eliminates data conflicts and duplicates. For the best availability, the location for the shared model should be on a cloud service. Accessing the cloud model only through a web service, which encapsulates all model handling functionality, will ensure data integration and validity. The validity of IT solutions can only be confirmed with real software. For testing the conversion-free BIM method, a software development kit (SDK) with required functionality was programmed. Three applications and a cloud service for handling the shared model were developed with the help of SDK. In the experiments, Leonardo application was used for modelling walls, 3DTrussme for trusses, and Viewer for viewing the model. All applications were using the same shared model on the cloud. In the experimental test, the information exchange occurred without conversions, and all the data were saved only once on the cloud database. Without conversions, less conflicts and redundancies occurred, which lead to better data integrity and integration. Using SDK, there was no technical barrier for applications to join the single shared model ecosystem, but a drawback was that existing BIM programs are not compatible without remarkable changes. The performance was acceptable in the test run, but in real use, the size of the model and the number of applications and users will be much larger. However, a conversion-free single shared model approach can be a possible trend to the next generation BIM as well as a potential alternative for current data sharing methods using distributed files, conversions, and linked data. / Tiivistelmä Rakentamisen tietomallinnuksen (BIM) nykyisenä tiedonjakamisen käytäntönä on hajautettu tietojärjestelmä, joka perustuu konversioihin. Konversiot ovat ongelmallisia tiedon häviämisen, ristiriitojen ja päällekkäisyyksien vuoksi. Tämän tutkimuksen hypoteesit olivat: i) konversiovapaa tiedonjakaminen on mahdollista, ii) etuja on saavutettavissa ilman konversioita tapahtuvassa tiedonsiirrossa ja iii) laajemmalle teolliselle käytölle ei ole periaatteellisia esteitä. Konversiovapaa tiedon jakaminen edellyttää yhden dataformaatin käyttöä. Alati kasvavien tietomallien koko vaatii tiedon järjestämismenetelmän, joko mahdollistaa osamallit. Datan ristiriidat ja päällekkäisyydet voidaan estää yhden jaetun mallin menetelmällä. Informaatio on parhaiten kaikkien osapuolien saatavilla, kun jaettu malli sijoitetaan pilvipalveluun. Kun tietomallia käsitellään vain web-palvelun rajapintafunktioilla, tiedon eheys ja kelpoisuus säilyvät. Tietoteknisten ratkaisujen kelpoisuus voidaan viime kädessä osoittaa vain toimivilla ohjelmistoilla. Konversiovapaan menetelmän testausta varten kehitettiin vaadittavat ominaisuudet sisältävä ohjelmistokehityspaketti (SDK), joka on edellytys sovellusten yhteensopivuudelle. Kolme sovellusta eli 3DTrussme, Leonardo ja Viewer ohjelmoitiin SDK:n avulla. Ohjelmointirajapinta sisälsi tarvittavat toiminnallisuudet tiedonjakamiseen, ja se toteutettiin pilvipalveluna. Testiajossa Leonardo-sovelluksella mallinnettiin seinät, 3DTrussmellä suunniteltiin naulalevyristikot ja Viewer-sovelluksella tarkasteltiin mallia. Kaikki kolme sovellusta käyttivät samaa jaettua mallia pilvipalvelussa. Suoritetussa testissä kaikki informaatio jaettiin ilman konversioita ja tallennettiin vain kerran. Ilman konversioita ja päällekkäisyyksiä saavutettiin parempi datan eheys ja integraatio. SDK:n avulla uudet sovellukset pystyivät liittymään yhden jaetun mallin ekosysteemiin ilman teknisiä esteitä. Toisaalta, ilman merkittäviä muutoksia nykyiset BIM sovellukset eivät ole yhteensopivia. Testiajossa suorituskyky oli hyväksyttävä, mutta todellisuudessa mallien koko sekä sovellusten ja käyttäjien lukumäärä ovat paljon suurempia. Tutkimus osoitti, että konversiovapaa yhden jaetun mallin menetelmä voi olla seuraava BIM-kehityssuunta ja vaihtoehto nykyisille tiedonsiirtoratkaisuille, jotka käyttävät erillisiä tiedostoja, konversioita ja linkkejä.

When an astronomical observer discovers a transient event in the sky, how can the information be immediately shared and delivered to others? Not too long time ago, people shared the information about what they discovered in the sky by books, telegraphs, and telephones. The new generation of transferring the event data is the way by the Internet. The information of astronomical events is able to be packed and put online as an Internet feed. For receiving these packed data, an Internet feed listener software would be required in a terminal computer. In other applications, the listener would connect to an intelligent robotic telescope network and automatically drive a telescope to capture the instant Astrophysical phenomena. However, because the technologies of transferring the astronomical event data are in the initial steps, the only resource available is the Perl-based Internet feed listener developed by the team of eSTAR. In this research, a Java-based Internet feed listener was developed. The application supports more features than the Perl-based application. After applying the rich Java benefits, the application is able to receive, parse and manage the Internet feed data in an efficient way with the friendly user interface. Keywords: Java, socket programming, VOEvent, real-time astronomy

Cloud computing allows users to tap into a massive pool of shared computing resources such as servers, storage, and network. These resources are provided as a service to the users allowing them to “plug into the cloud” similar to a utility grid. The promise of the cloud is to free users from the tedious and often complex task of managing and provisioning computing resources to run applications. At the same time, the cloud brings several additional benefits including: a pay-as-you-go cost model, easier deployment of applications, elastic scalability, high availability, and a more robust and secure infrastructure. One important class of applications that users are increasingly deploying in the cloud is database management systems. Database management systems differ from other types of applications in that they manage large amounts of state that is frequently updated, and that must be kept consistent at all scales and in the presence of failure. This makes it difficult to provide scalability and high availability for database systems in the cloud. In this thesis, we show how we can exploit cloud technologies and relational database systems to provide a highly available and scalable database service in the cloud. The first part of the thesis presents RemusDB, a reliable, cost-effective high availability solution that is implemented as a service provided by the virtualization platform. RemusDB can make any database system highly available with little or no code modifications by exploiting the capabilities of virtualization. In the second part of the thesis, we present two systems that aim to provide elastic scalability for database systems in the cloud using two very different approaches. The three systems presented in this thesis bring us closer to the goal of building a scalable and reliable transactional database service in the cloud.

Mémoire numérisé par la Division de la gestion de documents et des archives de l'Université de Montréal

Cloud computing allows users to tap into a massive pool of shared computing resources such as servers, storage, and network. These resources are provided as a service to the users allowing them to “plug into the cloud” similar to a utility grid. The promise of the cloud is to free users from the tedious and often complex task of managing and provisioning computing resources to run applications. At the same time, the cloud brings several additional benefits including: a pay-as-you-go cost model, easier deployment of applications, elastic scalability, high availability, and a more robust and secure infrastructure. One important class of applications that users are increasingly deploying in the cloud is database management systems. Database management systems differ from other types of applications in that they manage large amounts of state that is frequently updated, and that must be kept consistent at all scales and in the presence of failure. This makes it difficult to provide scalability and high availability for database systems in the cloud. In this thesis, we show how we can exploit cloud technologies and relational database systems to provide a highly available and scalable database service in the cloud. The first part of the thesis presents RemusDB, a reliable, cost-effective high availability solution that is implemented as a service provided by the virtualization platform. RemusDB can make any database system highly available with little or no code modifications by exploiting the capabilities of virtualization. In the second part of the thesis, we present two systems that aim to provide elastic scalability for database systems in the cloud using two very different approaches. The three systems presented in this thesis bring us closer to the goal of building a scalable and reliable transactional database service in the cloud.

Reconciliação é o processo de prover uma visão consistente de dados provenientes de várias fontes de dados. Embora existam na literatura trabalhos voltados à proposta de soluções de reconciliação baseadas em colaboração assíncrona, o desafio de reconciliar dados quando vários usuários colaborativos trabalham de forma assíncrona sobre as mesmas cópias locais de dados, compartilhando somente eventualmente as suas decisões de integração particulares, tem recebido menos atenção. Nesta tese de doutorado investiga-se esse desafio, por meio da proposta do modelo AcCORD (Asynchronous COllaborative data ReconcIliation moDel). AcCORD é um modelo colaborativo assíncrono para reconciliação de dados no qual as atualizações dos usuários são mantidas em um repositório de operações na forma de dados de procedência. Cada usuário tem o seu próprio repositório para armazenar a procedência e a sua própria cópia das fontes. Ou seja, quando inconsistências entre fontes importadas são detectadas, o usuário pode tomar decisões de integração para resolvê-las de maneira autônoma, e as atualizações que são executadas localmente são registradas em seu próprio repositório. As atualizações são compartilhadas entre colaboradores quando um usuário importa as operações dos repositórios dos demais usuários. Desde que diferentes usuários podem ter diferentes pontos de vista para resolver o mesmo conflito, seus repositórios podem estar inconsistentes. Assim, o modelo AcCORD também inclui a proposta de diferentes políticas de reconciliação multiusuário para resolver conflitos entre repositórios. Políticas distintas podem ser aplicadas por diferentes usuários para reconciliar as suas atualizações. Dependendo da política aplicada, a visão final das fontes importadas pode ser a mesma para todos os usuários, ou seja, um única visão global integrada, ou resultar em distintas visões locais para cada um deles. Adicionalmente, o modelo AcCORD também incorpora um método de propagação de decisões de integração, o qual tem como objetivo evitar que um usuário tome decisões inconsistentes a respeito de um mesmo conflito de dado presente em diferentes fontes, garantindo um processo de reconciliação multiusuário mais efetivo. O modelo AcCORD foi validado por meio de testes de desempenho que avaliaram as políticas propostas, e por entrevistas a usuários que avaliaram não somente as políticas propostas mas também a qualidade da reconciliação multiusuário. Os resultados obtidos demonstraram a eficiência e a eficácia do modelo proposto, além de sua flexibilidade para gerar uma visão integrada ou distintas visões locais. As entrevistas realizadas demonstraram diferentes percepções dos usuários quanto à qualidade do resultado provido pelo modelo AcCORD, incluindo aspectos relacionados à consistência, aceitabilidade, corretude, economia de tempo e satisfação. / Reconciliation is the process of providing a consistent view of the data imported from different sources. Despite some efforts reported in the literature for providing data reconciliation solutions with asynchronous collaboration, the challenge of reconciling data when multiple users work asynchronously over local copies of the same imported data has received less attention. In this thesis we investigate this challenge. We propose AcCORD, an asynchronous collaborative data reconciliation model. It stores users integration decision in logs, called repositories. Repositories keep data provenance, that is, the operations applied to the data sources that led to the current state of the data. Each user has her own repository for storing the provenance. That is, whenever inconsistencies among imported sources are detected, the user may autonomously take decisions to solve them, and integration decisions that are locally executed are registered in her repository. Integration decisions are shared among collaborators by importing each others repositories. Since users may have different points of view, repositories may also be inconsistent. Therefore, AcCORD also introduces several policies that can be applied by different users in order to solve conflicts among repositories and reconcile their integration decisions. Depending on the applied policy, the final view of the imported sources may either be the same for all users, that is, a single integrated view, or result in distinct local views for each of them. Furthermore, AcCORD encompasses a decision integration propagation method, which is aimed to avoid that a user take inconsistent decisions over the same data conflict present in different sources, thus guaranteeing a more effective reconciliation process. AcCORD was validated through performance tests that investigated the proposed policies and through users interviews that investigated not only the proposed policies but also the quality of the multiuser reconciliation. The results demonstrated the efficiency and efficacy of AcCORD, and highlighted its flexibility to generate a single integrated view or different local views. The interviews demonstrated different perceptions of the users with regard to the quality of the result provided by AcCORD, including aspects related to consistency, acceptability, correctness, time-saving and satisfaction.

The concept of grid computing has permeated all areas of distributed computing, changing the way in which distributed systems are designed, developed and implemented. At the same time ‘geobrowsers’, such as Google Earth, NASA World Wind and Virtual Earth, along with in-vehicle navigation, handheld GPS devices and maps on mobile phones, have made interactive maps and geographic information an everyday experience. Behind these maps lies a wealth of spatial data that is collated from a vast number of different sources. A spatial data infrastructure (SDI) aims to make spatial data from multiple sources available to as wide an audience as possible. Current research indicates that, due to a number of reasons, data sharing in these SDIs is still not common. This dissertation presents an analysis of the data grid approach for SDIs. Starting off, two imaginary scenarios spell out for the first time how data grids can be applied to enable the sharing of address data in an SDI. The work in this dissertation spans two disciplines: Computer Science (CS) and Geographic Information Science (GISc). A study of related work reveals that the data grid approach in SDIs is both a novel application for data grids (CS), as well as a novel technology in SDI environments (GISc), and this dissertation advances mutual understanding between the two disciplines. The novel evaluation framework for national address databases in an SDI is used to evaluate existing information federation models against the data grid approach. This evaluation, as well as an analysis of address data in an SDI, confirms that there are similarities between the data grid approach and the requirement for consolidated address data in an SDI. The evaluation further shows that where a large number of organizations are involved, such as for a national address database, and where there is a lack of a single organization tasked with the management of a national address database, the data grid is an attractive alternative to other models. The Compartimos (Spanish for ‘we share’) reference model was developed to identify the components with their capabilities and relationships that are required to grid-enable address data sharing in an SDI. The definition of an address in the broader sense (i.e. not only for postal delivery), the notion of an address as a reference and the definition of an addressing system and its comparison to a spatial reference system contribute towards the understanding of what an address is. A novel address data model shows that it is possible to design a data model for sharing and exchange of address data, despite diverse addressing systems and without impacting on, or interfering with, local laws for address allocation. The analysis in this dissertation confirms the need for standardization of domain specific geographic information, such as address data, and their associated services in order to integrate data from distributed heterogeneous sources. In conclusion, results are presented and recommendations for future work, drawn from the experience on the work in this dissertation, are made. / Thesis (PhD)--University of Pretoria, 2009. / Computer Science / unrestricted

abstract: Sensitive data sharing presents many challenges in case of unauthorized disclosures, including stigma and discrimination for patients with behavioral health conditions (BHCs). Sensitive information (e.g. mental health) warrants consent-based sharing to achieve integrated care. As many patients with BHCs receive cross-organizational behavioral and physical health care, data sharing can improve care quality, patient-provider experiences, outcomes, and reduce costs. Granularity in data sharing further allows for privacy satisfaction. Though the subjectivity in information patients consider sensitive and related sharing preferences are rarely investigated. Research, federal policies, and recommendations demand a better understanding of patient perspectives of data sensitivity and sharing. The goal of this research is to enhance the understanding of data sensitivity and related sharing preferences of patients with BHCs. The hypotheses are that 1) there is a diversity in medical record sensitivity and sharing preferences of patients with BHCs concerning the type of information, information recipients, and purpose of sharing; and 2) there is a mismatch between the existing sensitive data categories and the desires of patients with BHCs. A systematic literature review on methods assessing sensitivity perspectives showed a lack of methodologies for characterizing patient perceptions of sensitivity and assessing the variations in perceptions from clinical interpretations. Novel informatics approaches were proposed and applied using patients’ medical records to assess data sensitivity, sharing perspectives and comparing those with healthcare providers’ views. Findings showed variations in perceived sensitivity and sharing preferences. Patients’ sensitivity perspectives often varied from standard clinical interpretations. Comparison of patients’ and providers’ views on data sensitivity found differences in sensitivity perceptions of patients. Patients’ experiences (family history as genetic data), stigma towards category definitions or labels (drug “abuse”), and self-perceptions of information applicability (alcohol dependency) were influential factors in patients’ sensitivity determination. This clinical informatics research innovation introduces new methods using medical records to study data sensitivity and sharing. The outcomes of this research can guide the development of effective data sharing consent processes, education materials to inform patients and providers, granular technologies segmenting electronic health data, and policies and recommendations on sensitive data sharing. / Dissertation/Thesis / Doctoral Dissertation Biomedical Informatics 2020