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

Freedom of speech and the right to privacy are maybe the most important elements of a modern society, yet the rights are often violated. This fact was the main reason for writing this thesis covering P2P network models, anonymity, censorship resistance and their use in real P2P networks and publishing systems. We discuss their effectiveness and suitability for specific purposes, but also the security considerations of their use. The second part of this thesis presents the LSPP publishing protocol, which is an library implementation of an anonymous censorship resistant P2P network. Finally, we analyze the proposed protocol and compare it with existing solutions.

The specification of building material is required in multiple phases of engineering and construction projects towards holistic BIM implementations. Building material information plays a vital role in design decisions by enabling different simulation processes, such as energy, acoustic, lighting, etc. Utilization and sharing of building material information between stakeholders are some of the major influencing factors on the practical implementation of the BIM process. Different meta-data schemas (e.g. IFC) are usually available to represent and share material information amongst partners involved in a construction project. However, these schemas have their own constraints to enable efficient data sharing amongst stakeholders. This paper explains these constraints and proposes a methodological approach for the representation of material data using semantic web concepts aiming to support the sharing of BIM data and interoperability enhancements in collaboration workflows. As a result, the DICBM (https://w3id.org/digitalconstruction/BuildingMaterials) ontology was developed which improves the management of building material information in the BIM-based collaboration process.:Abstract 1. Introduction and Background 1.1 Building Information Modeling for collaboration 1.2 Information management in AEC using semantic web technologies 2 DICBM: Digital Construction Building Material Ontology 2.1 Building Material Data in IFC 2.2 Overview of the building material ontology 2.3 Integration of external ontology concepts and roles 2.4 Material Definition 2.5 Material, Material Type, and Material Property 2.6 Data Properties in DICBM 3 Conclusions Acknowledgments References

Smartwatches can collect health data, location data and other sensitive information about users, and privacy concerns arise. This thesis aimed to investigate how older people (50-80 years old) in Sweden behave when it comes to privacy and health data. The data were analyzed according to the privacy paradox, which describes the discrepancy between how people behave and how they intend to behave in relation to risk and trust. The research approach was qualitative, and twelve semi-structured interviews were conducted. The interviews were coded and thematized following the chosen theory. Among the twelve participants in the study, a majority did not see, understand, or behave consciously towards the risks of sharing health data. Instead, trust was related to both the disclosure behavior and the intentional behavior among several of the participants in this study. This study indicates that for some of the participants, there are also other factors that determine their behavior, and the privacy paradox alone is not complete. Four of the findings when it comes to participants' behavior towards their health data and privacy were: trust-based decisions, lack of knowledge, low value of personal data, and value benefits more than privacy. Among several of the participants in this study, when trust towards an actor increase, the participant’s risk awareness decreases. It can be discussed whether the participants in the study value the opportunities more than the risks, and this impacts their behavior. Most of the participants think that sharing location data infringes more on their privacy than sharing health data, and self-education might be a reason the behavior and the level of privacy differ among the participants.

Energy analysis at different stages of a building’s life-cycle allows designers and engineers to make proper design decisions, which will enhance the efficiency and energy saving measures. However, energy analysis of a building using traditional methods at every stage of the project is time-consuming and more labor intensive. Thus, energy simulations of buildings are rarely introduced in all design stages of the project. This study focuses on data transfer process from BIM model (Revit) to energy simulation model (IES ‹VE›) using OpenBIM meta-data model - Industry Foundation Classes (IFC) as an exchangeable file format. This data sharing process simplifies the complexity in energy modeling and allows to investigate different design alternatives in each phase of the building’s life-cycle. To investigate the efficiency and completeness of this data transfer process, a demonstration of data sharing is carried. By evaluating the results from the demonstration, efficiency gaps are identified in the data transferred process. A detailed investigation on the cause of efficiency gaps in data sharing is carried out and incorporated in this paper.:Abstract 1. Introduction 2. Building Energy Simulation 2.1. Categorization of Energy Simulation Models 3. Data Sharing Requirements - IFC 4. Data Sharing Demonstration 4.1. BIM model 4.2. Data investigation with model viewer 4.3. Data quality verification in energy simulation model 4.3.1. Evaluation of Results 5. Conclusion References

Data from underlying research has become increasingly important to scientists and the public in recent decades. As a result, funders and journal publishers have become increasingly demanding that scientists share their data. Universities have also been encouraged to advance their data sharing support units as a result of this development. Earlier studies on data sharing among scientists have primarily explored the barriers to data sharing, while motivations and perceptions among scientists that have shared their data have been examined less. To this end, this thesis investigates perceptions and responsibilities on data sharing among ecologists that have shared data in an open data repository within the last 12 months. As a public-funded and university-supported repository, the Swedish National Data Service (SND) is selected for this purpose. Semi-structured interviews with six ecologists are conducted to evaluate their motivations and perceived responsibilities on data sharing, The theory of planned behavior (TBP) serves as theoretical framework. Earlier TPB models are adapted to include new factors that potentially influence data sharing behavior among ecologists. The interviews highlight several individual and institutional factors that influence ecologists' data sharing in the SND repository. On the individual level, the informants perceive a strong personal responsibility to share their data publicly. On the institutional level, they perceive that journals have a large responsibility, and the findings indicate that journal pressure, in isolation, has a positive impact on data sharing. Perceptions about the SND support are ambiguous among the informants. While generally perceived as helpful and quality enhancing, most informants found it stressful and time consuming to share their data via the SND support. Thus, the combination of journal pressure and perceived stress associated with preparing data for SND has a negative influence on the motivation to share the data in the repository. Unless data sharing in SND is facilitated, or the pressure from journals is mitigated, scientists might increasingly opt for using repositories that require less strict metadata descriptions. / Data från underliggande forskning har blivit allt viktigare för forskare och allmänheten under de senaste decennierna. Såväl forskningsfinansiärer som utgivare av vetenskapliga tidskrifter har ställt allt högre krav på att forskare ska dela sina data. Universiteten har också uppmuntrats att utveckla sina stödenheter för datadelning. Tidigare studier om datadelning bland forskare har i första hand undersökt hinder för forskare från att dela sina data, medan uppfattningar bland forskare som har delat sina data inte har undersökts i lika stor utsträckning. Denna avhandling undersöker uppfattningar och ansvar för datadelning bland ekologer som har delat data i ett öppet dataarkiv under de senaste 12 månaderna. Dataarkivet vid Svensk Nationell Datatjänst (SND) väljs för detta ändamål eftersom det stöds av flera universitet och är offentligt finansierad. Semistrukturerade intervjuer genomförs med sex ekologer för att utvärdera drivkrafter och upplevda ansvar kring datadelning. Teorin om planerat beteende (TBP) används som teoretiskt ramverk. Tidigare TPB-modeller anpassas för att inkludera nya faktorer som kan påverka datadelningsbeteendet hos ekologer. Intervjuerna belyser flera individuella och institutionella faktorer som påverkar ekologernas datadelning i SND. På individnivå upplever informanterna ett personligt ansvar att dela sin data offentligt. På institutionell nivå anser de att tidskrifter har stort ansvar och resultaten tyder på att påtryckningar från tidskrifter har en positiv inverkan på datadelning. Uppfattningar om SND-supporten är tvetydiga. Många upplevde stödet hjälpsamt och kvalitetshöjande, men också att det var stressigt och tidskrävande att dela sina data via SND-supporten. Således har kombinationen av påtryckningar från tidskrifter och upplevd stress i samband med förberedelse av data för SND en negativ inverkan på datadelning i arkivet. Såvida inte datadelningen i SND underlättas, eller påtryckningarna från tidskrifter mildras, kan forskare i allt högre grad välja att använda arkiv som kräver mindre strikta metadatabeskrivningar.

The transport industry is facing major changes in the transition from traditional diesel-powered vehicles to electrified vehicles. The transition to electric vehicles in the transport industry is necessary to reach the environmental goals of the Paris Agreement. Through research, data sharing between actors was identified as a potential factor that could be used in the development of the electromobility sector, but sufficient information on this subject was lacking. This led to the basis for the thesis project. The thesis project was carried out in collaboration with Volvo Group, hereby interchangeably called Volvo, to investigate how data sharing can be used to facilitate the transformation to electromobility in the transport industry. The purpose of the thesis was to: Investigate how Volvos Value Offering can be improved by mapping out potential actors in the electromobility eco-system and how they could benefit from shared data. The thesis was based on the three research questions: What values and offers can be created in the charging infrastructure industry through shared data and what challenges, risks and opportunities do this create for the stakeholders involved? What information gaps hinder the development of the electromobility market, in general and, more specifically, in relation to data sharing? And lastly, how can Volvo take advantage of business opportunities in the electromobility market, in general and, more specifically, in relation to data sharing? The methods used to answer these questions were media analysis, 19 in-depth interviews, and a workshop with Volvo. The media analysis resulted in a mapping of the involved stakeholders in the electromobility development industry, how data sharing is used today and the actors' stance on data sharing. The interviews were held with respondents from the energy industry, tech companies, researchers, haulage companies and the truck manufacturer Volvo Group. The interviews were organized using the Gioia method and resulted in six different global themes on electromobility and data sharing. Results from the media analysis and the interviews were compiled into three scenarios. These were then presented to Volvo in a workshop, where they described how they would act as a major truck manufacturer in each scenario respectively. After compiling the results from the three methods, the research questions could be answered. The first research question was answered by the fact that the transport industry has a low degree of data maturity. The reason being that there are several perceived risks among the actors regarding data sharing in the form of losing competitive advantages, increased risks of cyber-attacks and GDPR violations. Despite the low degree of data maturity, there were also new opportunities that could be identified with data sharing. The biggest identified opportunity in this thesis was that data sharing can accelerate the development and expansion of the charging infrastructure, if vehicle data and energy data can be shared between actors. The second research question was answered simply by the fact that due to the low data degree of maturity, very little data is shared at present. The biggest identified information gap was the “chicken and egg” situation in the industry, where energy actors are waiting for initiatives from the automotive industry before making any decisions, and vice versa. The third research question was answered by identifying that Volvo's greatest opportunities as truck manufacturers exist through collaborations with other companies to establish standards for data sharing and data selling, offering charging solutions for their electric trucks and, finally, logistics optimization services based on real-time data. As the three research questions were answered, the purpose of the study was therefore fulfilled. The initial scope of the thesis was expanded from focusing solely on Volvo's opportunities as a truck manufacturer, to include opportunities for actors in the entire electromobility industry such as energy companies, charging post companies, haulage companies and tech companies. The study concluded by showing that there are great potential business and optimization opportunities and societal benefits with data sharing in the EMOB industry if the actors are willing to collaborate to set standards and drive development together. / Transportindustrin står inför stora förändringar i omställningen från traditionella dieseldrivna fordon till elektrifierade fordon. Omställningen inom transportindustrin är nödvändig för att nå miljömålen inom Parisavtalet. Forskning visar att datadelning mellan aktörer är en potentiell faktor som skulle kunna användas inom utvecklingen av elektromobilitetssektorn, men tillräckligt med information om detta område saknas. Detta blev grunden för examensarbetet. Examensarbetet genomfördes i samarbete med Volvo Group, hädan efter kallat Volvo, för att undersöka hur datadelning kan användas för att underlätta elektromobilitetsomvandlingen inom transportbranschen. Syftet med arbetet var att: Undersöka hur Volvos värdeerbjudanden kan förbättras genom att kartlägga potentiella aktörer i ekosystemet för elektromobilitet och hur de kan dra nytta av delade data. Arbetet utgick ifrån de tre forskningsfrågorna: Vilka värden och erbjudanden kan skapas inom laddinfrastruktur branschen genom delade data, vilka utmaningar, risker och möjligheter skapar detta för de inblandade intressenterna? Vilka informationsluckor hindrar utvecklingen av elektromobilitetsmarknaden, generellt och, mer specifikt, i relation till datadelning? Och slutligen, hur kan Volvo ta vara på affärsmöjligheter inom elektromobilitetsmarknaden, generellt och, mer specifikt, i relation till datadelning? Metoderna som användes för att besvara dessa frågor var mediaanalys, 19 djupintervjuer, samt en workshop med Volvo Group. Mediaanalysen resulterade i en kartläggning av drivande aktörer inom elektromobilitetsbranschen, hur datadelning används i dagsläget och aktörernas inställning till datadelning. Intervjuerna hölls med respondenter från energibranschen, techbolag, forskare, åkerier och Volvo Group. Intervjuerna organiserades med Gioia metoden och resulterade i sex olika globala teman om elektromobilitet och datadelning. Resultat från mediaanalysen och intervjuerna sammanställdes i tre scenarion. Dessa presenterades för Volvo i en workshop där de fick resonera hur de skulle agera som en stor lastbilstillverkare i respektive scenario. Efter sammanställning av resultaten från de tre metoderna kunde forskningsfrågorna besvaras. Den första forskningsfrågan besvarades med att transportbranschen i sig har låg datamognadsgrad. Det eftersom det fanns flera uppfattade risker hos aktörerna kring datadelning i form av förlorade konkurrensfördelar, ökade risker för cyberattacker och GDPR överträdelser. Trots den låga datamognadsgraden så finns det nya möjligheter med datadelning, där den största identifierade möjligheten i detta arbete är att datadelning kan påskynda utvecklingen och utbyggnaden av laddinfrastrukturen om fordonsdata och energidata kan delas mellan aktörer. Den andra forskningsfrågan besvarades med att på grund av den låg data mognadsgraden så delas väldigt lite data i dagsläget. Det största identifierade informationsluckan var “hönan eller ägget” situationen i branschen, där energiaktörer väntar på initiativ från fordonsbranschen innan de tar några beslut, och vice versa. Den tredje forskningsfrågan besvarades med att Volvos största möjligheter som lastbilstillverkare finns genom samarbeten med andra företag för att etablera standarder för datadelning och dataförsäljning, erbjuda laddlösningar till sina elektriska fordon och slutligen logistikoptimeringstjänster baserade på realtidsdata. I och med att de tre forskningsfrågorna besvarades, uppfylldes därmed syftet med studien. Omfattningen av arbetet expanderades dock från att enbart fokusera på Volvos möjligheter som lastbilstillverkare, till att omfatta aktörer inom hela elektromobilitets branschen så som energibolag, laddstolpsbolag, åkerier och techbolag. Studien visar att det finns stora potentiella affärs och optimeringsmöjligheter och samhällsnytta med datadelning inom elektromobilitetsbranschen om aktörer är villiga att samarbeta för att sätta standarder och driva utvecklingen tillsammans.

Due to the rise of digitalization and the growing amount of data, ensuring the integrity and security of patient data has become increasingly vital within the healthcare industry, which has traditionally managed substantial quantities of sensitive patient and personal information. This bachelor's thesis focused on designing and implementing a secure data sharing infrastructure to protect the integrity and confidentiality of patient data. Synthetic data was used to enable access for researchers and students in regulated environments without compromising patient privacy. The project successfully achieved its goals by evaluating different privacy-preserving mechanisms and developing a machine learning-based application to demonstrate the functionality of the secure data sharing infrastructure. Despite some challenges, the chosen algorithms showed promising results in terms of privacy preservation and statistical similarity. Ultimately, the use of synthetic data can promote fair decision-making processes and contribute to secure data sharing practices in the healthcare industry. / Hälso- och sjukvårdsbranschen har länge varit en sektor som hanterar stora mängder känsliga patientdata och personuppgifter. Integriteten och säkerheten hos patientdata har blivit allt viktigare som en följd av ökad datavolym och digitalisering. Detta examensarbete fokuserade på att utforma och implementera en säker datadelning infrastruktur för att skydda integritet och sekretess för patientdata. Syntetisk data användes för att möjliggöra tillgång för forskare och studenter i reglerade miljöer utan att riskera patienters privatliv. Projektet lyckades genom att utvärdera olika integritetsbevarande mekanismer och skapa en maskininlärningsbaserad applikation för att visa den säkra datadelningsinfrastrukturens funktionalitet. Trots vissa utmaningar visade de valda algoritmerna lovande resultat i fråga om integritetsbevarande och statistisk likhet. Slutligen kan användningen av syntetiska data främja rättvisa beslutsprocesser och bidra till säkra datadelningspraxis inom hälso- och sjukvårdsbranschen.