Cette thèse questionne, d’un point de vue compréhensif et critique, la notion de traces numériques à l’heure du Big Data et de la relation entre les notions de la surveillance et la confiance. Le « Big Data » fait référence à la production massive de données qui représentent une manne précieuse de bénéfices. En effet, la quantité massive de données produites dans le monde atteint des volumes si importants qu’il est indéniablement impossible de les analyser par l’humain sans l’aide d’outils technologiques et statistiques adéquats. Parmi les secteurs concernés par cette révolution technologique et sociétale, le secteur aéroportuaire est aujourd’hui confronté à une importante transformation, nourrie par l’explosion des données au sein de sa structure. Les données générées, collectées et stockées au cours du parcours du passager sont désormais massives et leur gestion est un important levier pour la sécurité, l’amélioration de services et le confort du passager. Pour autant, les avantages attendus n’en soulèvent pas moins une grande question : où vont ces données ? Difficile d’y répondre. Et tant qu’on ne sait pas, comment peut-on faire confiance ? Ces réflexions sont mises en examen à l’aéroport de Larnaca à Chypre. Les différents angles d’approche ainsi que la diversité des acteurs ont nécessité la constitution d’un corpus multidimensionnel, issu d’une méthodologie mixte, afin d’avoir une approche compréhensive du sujet. Ce corpus comprend à la fois des entretiens, des questionnaires et des récits de vie des passagers et des professionnels du terrain. L’analyse qualitative et quantitative qui a suivi était basée sur un cadre précédemment élaboré afin de croiser les représentations des acteurs à propos de la surveillance et la confiance et mettre en évidence les différentes visions inhérentes à cette question. / This research project questions, in a comprehensive and critical way, the presence of digital traces in the era of Big Data. This reflection opens up in the relation between Surveillance and Trust. In recent years, “Big Data” has massively and repeatedly been used in order to describe a new societal dynamic that would be characterized by the production of massive quantities of data. Furthermore, enormous potential benefits from using new statistical tools to analyze these data generated from connected objects and tools in more and more human actions. The airport sector is currently facing a major transformation, fueled by the explosion of data within its structure. The data generated during a passenger's journey are now extremely massive. There is no doubt that the management of this data is an important lever for the safety, the improvement of services and the comfort of the passenger. However, the expected benefits raise a great question: Where do these data go? We do not know. And as long as we do not know, how can we trust? These considerations are being examined at Larnaca airport in Cyprus. The different angles of approach as well as the diversity of the actors required the creation of a multidimensional corpus, resulting from a mixed methodology, in order to have a comprehensive approach to the subject. This corpus includes interviews, questionnaires and life stories of passengers and professionals. The qualitative and quantitative analysis that followed was based on a theoretical framework previously elaborated, in order to cross the representations of the actors concerning the surveillance and the trust and finally, highlight the different inherent visions to this issue.

Linked Data have emerged as a successful publication format which could mean to structured data what Web meant to documents. The strength of Linked Data is in its fitness for integration of data from multiple sources. Linked Data integration opens door to new opportunities but also poses new challenges. New algorithms and tools need to be developed to cover all steps of data integration. This thesis examines the established data integration proceses and how they can be applied to Linked Data, with focus on data fusion and conflict resolution. Novel algorithms for Linked Data fusion are proposed and the task of supporting trust with provenance information and quality assessment of fused data is addressed. The proposed algorithms are implemented as part of a Linked Data integration framework ODCleanStore.

One of the important tasks during software testing is the generation of test cases. Unfortunately, existing approaches to test case generation often have problems limiting their use. A problem of dynamic test case generation approaches, for instance, is that a large number of iterations can be necessary to obtain test cases. This article introduces a formal framework for the application of the well-known search strategy of binary search in path-oriented test case generation and explains the binary search-based test case generation (BINTEST) algorithm.

Die wachsende Bedeutung von Data Warehouse-Lösungen zur Entscheidungsunterstützung in großen Unternehmen hat zu einer unüberschaubaren Vielfalt von Software-Produkten geführt. Aktuelle Data Warehouse- Projekte zeigen, daß der Erfolg auch von der Wahl der passenden Werkzeuge für diese komplexe und kostenintensive Umgebung abhängt. Wir präsentieren eine Methode zur Evaluierung von Data Warehouse Tools, die eine Kombination aus Bewertung per Kriterienkatalog und detaillierten praktischen Tests umfaßt. Die Vorgehensweise ist im Rahmen von Projekten mit Industriepartnern erprobt und wird am Beispiel einer Evaluierung führender ETL-Werkzeuge demonstriert.

Microarrays make it possible to monitor the expression of thousands of genes in parallel thus generating huge amounts of data. So far, several databases have been developed for managing and analyzing this kind of data but the current state of the art in this field is still early stage. In this paper, we comprehensively analyze the requirements for microarray data management. We consider the various kinds of data involved as well as data preparation, integration and analysis needs. The identified requirements are then used to comparatively evaluate eight existing microarray databases described in the literature. In addition to providing an overview of the current state of the art we identify problems that should be addressed in the future to obtain better solutions for managing and analyzing microarray data.

Malaria is the parasitic disease which affects the most humans, with Plasmodium falciparum malaria being responsible for the majority of severe malaria and malaria related deaths. The asexual form of the parasite causes the signs and symptoms associated with malaria infection. The sexual form of the parasite, also known as a gametocyte, is the stage responsible for infectivity of the human host (patient) to the mosquito vector, and thus ongoing transmission of malaria and the spread of antimalarial drug resistance. Historically malaria therapeutic efficacy studies have focused mainly on the clearance of asexual parasites. However, malaria in a community can only be truly combated if a treatment program is implemented which is able to clear both asexual and sexual parasites effectively. In this thesis focus will be on the modeling of the key features of gametocytemia. Particular emphasis will be on the modeling of the time to gametocyte emergence, the density of gametocytes and the duration of gametocytemia. It is also of interest to investigate the impact of the administered treatment on the aforementioned features. Gametocyte data has several interesting features. Firstly, the distribution of gametocyte data is zero-inflated with a long tail to the right. The observed longitudinal gametocyte profile also has a nonlinear relationship with time. In addition, since most malaria intervention studies are not designed to optimally measure the evolution of the longitudinal gametocyte profile, there are very few observation points in the time period where the gametocyte profile is expected to peak. Gametocyte data collected from malaria intervention studies are also affected by informative censoring, which leads to incomplete gametocyte profiles. An example of informative censoring is when a patient who experiences treatment failure is “rescued", and withdrawn, from the study in order to receive alternative treatment. This patient can be considered to be in worse health as compared to the patients who remain in this study. There are also competing risks of exit from the study, as a patient can either experience treatment failure or be lost to follow-up. The above mentioned features of gametocyte data make it a statistically appealing dataset to analyze. In literature there are several modeling techniques which can be used to analyze individual features of the data. These techniques include standard survival models for modeling the time to gametocyte emergence and the duration of gametocytemia. The longitudinal nonlinear gametocyte profile would typically be modeled using nonlinear mixed effect models. These nonlinear models could then subsequently be extended to accommodate the zero-inflation in the data, by changing the underlying assumption around the distribution of the response variable. However, it is important to note that these standard techniques do not account for informative censoring. Failure to account for informative censoring leads to bias in parameter estimates. Joint modeling techniques can be used to account for informative censoring. The joint models applied in this thesis combined the longitudinal nonlinear gametocyte densities and the time to censoring due to either lost to follow up or treatment failure. The data analyzed in this thesis were collected from a series of clinical trials conducted be- tween 2002 and 2004 in Mozambique and the Mpumulanga province of South Africa. These trials were a part of the South East African Combination Antimalarial Therapy (SEACAT) evaluation of the phased introduction of combination anti-malarial therapy, nested in the Lubombo Spatial Development Initiative. The aim of these studies was primarily to measure the efficacy of sulfadoxine-pyrimethamine (SP) and a combination of artesunate and sulfadoxine-pyrimethamine (ACT), in eliminating asexual parasites in patients. The patients enrolled in the study had uncomplicated malaria, at a time of increasing resistance to sulfadoxine-pyrimethamine (SP) treatment. Blood samples were taken from patients during the course of 6 weeks on days 0, 1, 2, 3, 7, 14, 21, 28 and 42. Analysis of these blood samples provided longitudinal measurements for asexual 1 parasite densities, gametocyte densities, sulfadoxine drug concentrations and pyrimethamine drug concentrations. The gametocyte data collected in this study was initially analyzed using standard survival modeling techniques. Non-parametric Cox regression models and parametric survival models were applied to the data as part of this initial investigation. These models were used to investigate the factors which affected the time to gametocyte emergence. Subsequently, using the subset of the population which experienced gametocytemia, accelerated failure time models were applied to investigate the factors which affected the duration of gametocytemia. It is evident that the findings from the aforementioned duration investigation would only be able to provide valid duration estimates for patients who were detected to have gametocytemia. This work was extended to allow for population level duration estimates by incorporating the prevalence of gametocytemia into the estimation of duration, for generic patients with specific covariate patterns. The prevalence of gametocytemia was modeled using an underlying binomial distribution. The delta method was subsequently used to derive confidence intervals for the population level duration estimates which were associated with specific covariate patterns. An investigation into the factors affecting the early withdrawal of patients from the study was also conducted. Early exit from the study arose either through loss to follow-up (LTFU) or through treatment failure. The longitudinal gametocyte profile was modeled using joint modeling techniques. The resulting joint model used shared random effects to combine a Weibull survival model, describing the cause- specific hazards of patient exit from the study, with a nonlinear zero-adjusted gamma mixed effect model for the longitudinal gametocyte profile. This model was used to impute the incomplete gametocyte profiles, after adjusting for informative censoring. These imputed profiles were then used to estimate the duration of gametocytemia. It was found, in this thesis, that treatment had a very strong effect on the hazard of gametocyte emergence, density of gametocytes and the duration of gametocytemia. Patients who received a combination of sulfadoxine-pyrimethamine and artesunate were found to have significantly lower hazards of gametocyte emergence, lower predicted durations of gametocytemia and lower predicted longitudinal gametocyte densities as compared to patients who received sulfadoxine-pyrimethamine treatment only.

Categorical data are common in various research areas, and clustering is a prevalent technique used for analyse them. However, two challenging problems are encountered in categorical data clustering analysis. The first is that most categorical data distance metrics were actually proposed for nominal data (i.e., a categorical data set that comprises only nominal attributes), ignoring the fact that ordinal attributes are also common in various categorical data sets. As a result, these nominal data distance metrics cannot account for the order information of ordinal attributes and may thus inappropriately measure the distances for ordinal data (i.e., a categorical data set that comprises only ordinal attributes) and mixed categorical data (i.e., a categorical data set that comprises both ordinal and nominal attributes). The second problem is that most hierarchical clustering approaches were actually designed for numerical data and have very high computation costs; that is, with time complexity O(N2) for a data set with N data objects. These issues have presented huge obstacles to the clustering analysis of categorical data. To address the ordinal data distance measurement problem, we studied the characteristics of ordered possible values (also called 'categories' interchangeably in this thesis) of ordinal attributes and propose a novel ordinal data distance metric, which we call the Entropy-Based Distance Metric (EBDM), to quantify the distances between ordinal categories. The EBDM adopts cumulative entropy as a measure to indicate the amount of information in the ordinal categories and simulates the thinking process of changing one's mind between two ordered choices to quantify the distances according to the amount of information in the ordinal categories. The order relationship and the statistical information of the ordinal categories are both considered by the EBDM for more appropriate distance measurement. Experimental results illustrate the superiority of the proposed EBDM in ordinal data clustering. In addition to designing an ordinal data distance metric, we further propose a unified categorical data distance metric that is suitable for distance measurement of all three types of categorical data (i.e., ordinal data, nominal data, and mixed categorical data). The extended version uniformly defines distances and attribute weights for both ordinal and nominal attributes, by which the distances measured for the two types of attributes of a mixed categorical data can be directly combined to obtain the overall distances between data objects with no information loss. Extensive experiments on all three types of categorical data sets demonstrate the effectiveness of the unified distance metric in clustering analysis of categorical data. To address the hierarchical clustering problem of large-scale categorical data, we propose a fast hierarchical clustering framework called the Growing Multi-layer Topology Training (GMTT). The most significant merit of this framework is its ability to reduce the time complexity of most existing hierarchical clustering frameworks (i.e., O(N2)) to O(N1.5) without sacrificing the quality (i.e., clustering accuracy and hierarchical details) of the constructed hierarchy. According to our design, the GMTT framework is applicable to categorical data clustering simply by adopting a categorical data distance metric. To make the GMTT framework suitable for the processing of streaming categorical data, we also provide an incremental version of GMTT that can dynamically adopt new inputs into the hierarchy via local updating. Theoretical analysis proves that the GMTT frameworks have time complexity O(N1.5). Extensive experiments show the efficacy of the GMTT frameworks and demonstrate that they achieve more competitive categorical data clustering performance by adopting the proposed unified distance metric.

As part of its quest to become the first digital African city, in 2014 the City of Cape Town adopted an open data policy, which was later coupled with an open data portal to make government data available for public access. This was touted as a novelty initiative as the City of Cape Town was the first African city to implement a policy of this nature. This open data initiative aimed at enhancing transparency and accountability as well as promoting inclusive economic participation for its citizens. Open data project managers from the city and external industry experts working on open data initiatives were interviewed to understand the current the state of open data within the city and how it worked with other stakeholders. The study draws on these interviews to present the current challenges experienced by the city from the city’s official point of view as well as from open data experts working closely with the city. To understand the practical experiences of how the city publishes data in its platforms, the study also extensively explored the city’s open data portal, as well as examining and commenting on the documented open data policy guidelines contrasted and compared to current practical experiences. To guide the objectives and analysis of the study, four key themes were adopted from literature; context, use, data and impact. Context focused on the overall context or environment at which open data in the city is provided as a public service, while use focused challenges on the uses of open data as well as it is users, data focused on the types of datasets published on the portal as well as the technical challenges in publishing them. Lastly impact looked at the expected benefits and goals of the city’s open data policy. The study through the themes highlighted the ongoing challenges at various levels that the city experience as they implement and develop the open data policy. Overall it was noted that open data was not a goal but continuous challenges were arising daily while implementing and developing the policy- while it was noted that various stakeholders within and outside government had to collaborate to effectively meet the required open data standards.

Abstract As the business complexity, number of customers continues to grow and customers evolve into multinational organisations that operate across borders, many companies are faced with great challenges in the way they manage their customer data. In today’s business, a single customer may have a relationship with several entities of an organisation, which means that the customer data is collected through different channels. One customer may be described in different ways by each entity, which makes it difficult to obtain a unified view of the customer. In companies where there are several sources of data and the data is distributed to several systems, data environments become heterogenic. In this state, customer data is often incomplete, inaccurate and inconsistent throughout the company. This thesis aims to study how organisations with heterogeneous customer data sources implement the Master Data Management (MDM) concept to achieve and maintain high customer data quality. The purpose is to provide recommendations for how to achieve successful customer data management using MDM based on existing literature related to the topic and an interview-based empirical study. Successful customer data management is more of an organisational issue than a technological one and requires a top-down approach in order to develop a common strategy for an organisation’s customer data management. Proper central assessment and maintenance processes that can be adjusted according to the entities’ needs must be in place. Responsibilities for the maintenance of customer data should be delegated to several levels of an organisation in order to better manage customer data.

Nowadays, more personal data are being collected with the advent of computers and the development of new telecommunications technology. However, the emerging form of new technologies creates new challenges and risks in personal processing information, which can be inaccurate, incomplete or irrelevant. Furthermore, personal data can be accessed or disclosed without authorisation, used for a purpose other than that for which they were collected or destroyed. Therefore, the unregulated processing of personal information threatens a person’s right to privacy. In response to this, many countries are adopting or updating data protection legislation and policies. Still, the Democratic Republic of Congo (DRC) has been very slow and insignificant in adopting a data protection law. As a result, there is no specific comprehensive Data Protection legislation in the DRC, even though few provisions on data protection can be found in the Telecommunications and Information and Communications Technology (2020 ICT Act). This paper aims to do a critical analysis of principles governing the processing of personal data in the DRC to assess the extent to which they comply with international and African regional human rights standards applicable to data protection principles. It principally sets the gaps in the 2020 ICT Act while bringing to light its prospect to strengthen the protection of personal data in the DRC. The research also assesses the weaknesses that may require development going forward and proposes recommendations to ensure that the DRC complies with international standards. / Mini Dissertation (LLM (Human rights and Democratisation in Africa))--University of Pretoria, 2021. / European Union, Royal Norwegian Embassy in Pretoria / Centre for Human Rights / LLM (Human rights and Democratisation in Africa) / Unrestricted