Visualization of complex data is a challenging topic. Data are often stored in spreadsheets making it difficult to get an overview of otherwise inaccessible information. Visualization of data is necessary for getting an understanding of complex structures. Organizations, among them financial institutions, nowadays consist of large owner structures and legal structures. Visualization of these structures is a challenging task due to the many levels of complexity within these structures.This report presents a visualization prototype of the legal business structures of financial institutions. The primary function of this prototype is to facilitate the understanding of complex legal business structures that would be hard to comprehend only from spreadsheets. The development of the prototype was performed using Python and NetworkX and the visualization was constructed as a graph representation. The evaluation of the prototype was conduced with semi-structured interviews together with a demonstration. The evaluation indicated that the utility of the visualization prototype concept can be further improved. The results suggests that a prototype is vital and is of good use for facilitating understanding of data. / Visualisering av data är ett svårt problem. Diverse data lagras ofta i textform vilket bidrar till en sämre översikt av datan. Med hjälp av visualisering kan man få en bättre förståelse för komplexa strukturer i datan. Organisationer så som finansinstitut involverar ofta stora ägarstrukturer och legala strukturer. Att kunna visualisera dessa strukturer blir då ett problem på grund av deras komplexitet.I denna rapport presenteras en visualiseringsprototyp av legala affärsstrukturer hos finansinstitut. Huvudsyftet med denna prototyp är att få en bättre förståelse av strukturer som annars är svåra att analysera utifrån enbart kalkylblad. Prototypen implementerades med hjälp av Python och NetworkX och visades visuellt som en graf representation. Evalueringen utfördes med hjälp av intervjuer samt en demonstration av prototypen. Evalueringen visar på att användarna ser en nytta med prototypen vilket tyder på att det finns utrymme för att vidare utveckla konceptet. Resultatet antyder att en visualisering är väsentlig när det kommer till att underlätta analys av data.

In recent years, the security of many corporate networks have been compromised by hackers who managed to obtain important information by leveraging the vulnerabilities of those networks. Such attacks can have a strong economic impact and affect the image of the entity whose network has been attacked. Various tools are used by network security analysts to study and improve the security of networks. Attack graphs are among these tools. Attack graphs are graphs that show all the possible chains of exploits an attacker could follow to access an important host on a network. While attack graphs are useful for network security, they may become hard to read because of their size when networks become larger. Previous work tried to deal with this issue by applying simplification algorithms on graphs. Experience shows that even if these algorithms can help improve the visualization of attack graphs, we believe that improvements can be made, especially by relying on Machin Learning (ML) algorithms. Thus, the goal of this thesis is to investigate how ML can help improve the visualization of attack graphs and the security analysis of networks based on their attack graph. To reach this goal, we focus on two main areas. First we used graph clustering which is the process of creating a partition of the nodes based on their position in the graph. This improves visualization by allowing network analysts to focus on a set of related nodes instead of visualizing the whole graph. We also design several metrics for security analysis based on attack graphs. We show that the ML algorithms in both areas. The ML clustering algorithms even produce better clusters than non-ML algorithms with respect to the coverage metric, at the cost of computation time. Moreover, the ML security evaluation algorithms show faster computation times on dense attack graphs than the non-ML baseline, while producing similar results. Finally, a user interface that permits the application of the methods presented   in the thesis is also developed, with the goal of making the use of such methods easier by network analysts. / Under de senaste åren har säkerheten för många företagsnätverk äventyrats av hackare som lyckats få fram viktig information genom att utnyttja sårbarheterna i dessa nätverk. Sådana attacker kan ha en stark ekonomisk inverkan och påverka bilden av den enhet vars nätverk har angripits. Olika verktyg användes av nätverkssäkerhetsanalytiker för att studera och förbättra säkerheten i nätverken. Attackgrafer ät bland dessa verktyg. Attackgrafer är diagram som visar alla möjliga kedjor av utnyttjande en angripare kan följa för att komma åt en viktig värd i ett nätverk. Även om attackgrafer är användbara för nätverkssäkerhet, kan de bli svåra att läsa på grund av deras storlek när nätverk blir större. Tidigare arbete försökte hantera detta problem genom att tillämpa förenklingsalgoritmer på grafer. Erfarenheten visar att även om dessa algoritmer kan hjälpa till att förbättra visualiseringen av attackgrafer tror vi att förbättringar kan göras, särskilt genom att förlita sig på Machine Learning  (ML) algoritmer. Således är målet med denna avhandling att undersöka hur ML kan hjälpa till att förbättra visualiseringen av attackgrafer och säkerhetsanalys av nätverk baserat på deras attackgraf. För att nå detta mål fokuserar vi på två huvudområden. Först använder vi grafklustering som är processen för att skapa en partition av noderna baserat på deras position i grafen. Detta förbättrar visualiseringen genom att låta nätverksanalytiker fokusera på en uppsättning relaterade noder istället för att visualisera hela grafen. Vi utformar också flera mätvärden för säkerhetsanalys baserat på attackgrafer. Vi visar att ML-algoritmerna är lika effektiva som icke-LM-algoritmer inom båda områdena. Klusteringsalgoritmerna ML producerar till och med bättre kluster än icke-ML-algoritmer med avseende på täckningsvärdet, till kostnaden för beräkningstid. Dessutom visar ML säkerhetsutvärderingsalgoritmerna snabbare beräkningstider på täta attackgrafer än icke-ML baslinjen, samtidigt som de ger liknande resultat. Slutligen utvecklas också ett användargränssnitt som tillåter tillämpning av metoderna som presenteras i avhandlingen, med målet att göra användningen av sådana metoder enklare för nätverksanalytiker.

La compréhension des pathologies humaines et du mode d'action des médicaments passe par la prise en compte des réseaux d'interactions entre biomolécules. Les recherches récentes sur les systèmes biologiques produisent de plus en plus de données sur ces réseaux qui gouvernent les processus cellulaires. L'hétérogénéité et la multiplicité de ces données rendent difficile leur intégration dans les raisonnements des utilisateurs. Je propose ici des approches intégratives mettant en oeuvre des techniques de gestion de données, de visualisation de graphes et de fouille de données, pour tenter de répondre au problème de l'exploitation insuffisante des données sur les réseaux dans la compréhension des phénotypes associés aux maladies génétiques ou des effets secondaires des médicaments. La gestion des données sur les protéines et leurs propriétés est assurée par un système d'entrepôt de données générique, NetworkDB, personnalisable et actualisable de façon semi-automatique. Des techniques de visualisation de graphes ont été couplées à NetworkDB pour utiliser les données sur les réseaux biologiques dans l'étude de l'étiologie des maladies génétiques entrainant une déficience intellectuelle. Des sous-réseaux de gènes impliqués ont ainsi pu être identifiés et caractérisés. Des profils combinant des effets secondaires partagés par les mêmes médicaments ont été extraits de NetworkDB puis caractérisés en appliquant une méthode de fouille de données relationnelles couplée à Network DB. Les résultats permettent de décrire quelles propriétés des médicaments et de leurs cibles (incluant l'appartenance à des réseaux biologiques) sont associées à tel ou tel profil d'effets secondaires / The understanding of human diseases and drug mechanisms requires today to take into account molecular interaction networks. Recent studies on biological systems are producing increasing amounts of data. However, complexity and heterogeneity of these datasets make it difficult to exploit them for understanding atypical phenotypes or drug side-effects. This thesis presents two knowledge-based integrative approaches that combine data management, graph visualization and data mining techniques in order to improve our understanding of phenotypes associated with genetic diseases or drug side-effects. Data management relies on a generic data warehouse, NetworkDB, that integrates data on proteins and their properties. Customization of the NetworkDB model and regular updates are semi-automatic. Graph visualization techniques have been coupled with NetworkDB. This approach has facilitated access to biological network data in order to study genetic disease etiology, including X-linked intellectual disability (XLID). Meaningful sub-networks of genes have thus been identified and characterized. Drug side-effect profiles have been extracted from NetworkDB and subsequently characterized by a relational learning procedure coupled with NetworkDB. The resulting rules indicate which properties of drugs and their targets (including networks) preferentially associate with a particular side-effect profile

Data analysis techniques can be useful in decision-making processes, when patterns of interest can indicate trends in specific domains. Such trends might support evaluation, definition of alternatives, or prediction of events. Currently, datasets have increased in size and complexity, posing challenges to modern hardware resources. In the case of large datasets that can be represented as graphs, issues of visualization and scalable processing are of current concern. Distributed frameworks are commonly used to deal with this data, but the deployment and the management of computational clusters can be complex, demanding technical and financial resources that can be prohibitive in several scenarios. Therefore, it is desirable to design efficient techniques for processing and visualization of large scale graphs that optimize hardware resources in a single computational node. In this course of action, we developed a visualization technique named StructMatrix to find interesting insights on real-life graphs. In addition, we proposed a graph processing framework M-Flash that used a novel, bimodal block processing strategy (BBP) to boost computation speed by minimizing I/O cost. Our results show that our visualization technique allows an efficient and interactive exploration of big graphs and our framework MFlash significantly outperformed all state-of-the-art approaches based on secondary memory. Our contributions have been validated in peer-review events demonstrating the potential of our finding in fostering the analytical possibilities related to large-graph data domains. / Técnicas de análise de dados podem ser úteis em processos de tomada de decisão, quando padrões de interesse indicam tendências em domínios específicos. Tais tendências podem auxiliar a avaliação, a definição de alternativas ou a predição de eventos. Atualmente, os conjuntos de dados têm aumentado em tamanho e complexidade, impondo desafios para recursos modernos de hardware. No caso de grandes conjuntos de dados que podem ser representados como grafos, aspectos de visualização e processamento escalável têm despertado interesse. Arcabouços distribuídos são comumente usados para lidar com esses dados, mas a implantação e o gerenciamento de clusters computacionais podem ser complexos, exigindo recursos técnicos e financeiros que podem ser proibitivos em vários cenários. Portanto é desejável conceber técnicas eficazes para o processamento e visualização de grafos em larga escala que otimizam recursos de hardware em um único nó computacional. Desse modo, este trabalho apresenta uma técnica de visualização chamada StructMatrix para identificar relacionamentos estruturais em grafos reais. Adicionalmente, foi proposta uma estratégia de processamento bimodal em blocos, denominada Bimodal Block Processing (BBP), que minimiza o custo de I/O para melhorar o desempenho do processamento. Essa estratégia foi incorporada a um arcabouço de processamento de grafos denominado M-Flash e desenvolvido durante a realização deste trabalho.Foram conduzidos experimentos a fim de avaliar as técnicas propostas. Os resultados mostraram que a técnica de visualização StructMatrix permitiu uma exploração eficiente e interativa de grandes grafos. Além disso, a avaliação do arcabouço M-Flash apresentou ganhos significativos sobre todas as abordagens baseadas em memória secundária do estado da arte. Ambas as contribuições foram validadas em eventos de revisão por pares, demonstrando o potencial analítico deste trabalho em domínios associados a grafos em larga escala.

Data analysis techniques can be useful in decision-making processes, when patterns of interest can indicate trends in specific domains. Such trends might support evaluation, definition of alternatives, or prediction of events. Currently, datasets have increased in size and complexity, posing challenges to modern hardware resources. In the case of large datasets that can be represented as graphs, issues of visualization and scalable processing are of current concern. Distributed frameworks are commonly used to deal with this data, but the deployment and the management of computational clusters can be complex, demanding technical and financial resources that can be prohibitive in several scenarios. Therefore, it is desirable to design efficient techniques for processing and visualization of large scale graphs that optimize hardware resources in a single computational node. In this course of action, we developed a visualization technique named StructMatrix to find interesting insights on real-life graphs. In addition, we proposed a graph processing framework M-Flash that used a novel, bimodal block processing strategy (BBP) to boost computation speed by minimizing I/O cost. Our results show that our visualization technique allows an efficient and interactive exploration of big graphs and our framework MFlash significantly outperformed all state-of-the-art approaches based on secondary memory. Our contributions have been validated in peer-review events demonstrating the potential of our finding in fostering the analytical possibilities related to large-graph data domains. / Técnicas de análise de dados podem ser úteis em processos de tomada de decisão, quando padrões de interesse indicam tendências em domínios específicos. Tais tendências podem auxiliar a avaliação, a definição de alternativas ou a predição de eventos. Atualmente, os conjuntos de dados têm aumentado em tamanho e complexidade, impondo desafios para recursos modernos de hardware. No caso de grandes conjuntos de dados que podem ser representados como grafos, aspectos de visualização e processamento escalável têm despertado interesse. Arcabouços distribuídos são comumente usados para lidar com esses dados, mas a implantação e o gerenciamento de clusters computacionais podem ser complexos, exigindo recursos técnicos e financeiros que podem ser proibitivos em vários cenários. Portanto é desejável conceber técnicas eficazes para o processamento e visualização de grafos em larga escala que otimizam recursos de hardware em um único nó computacional. Desse modo, este trabalho apresenta uma técnica de visualização chamada StructMatrix para identificar relacionamentos estruturais em grafos reais. Adicionalmente, foi proposta uma estratégia de processamento bimodal em blocos, denominada Bimodal Block Processing (BBP), que minimiza o custo de I/O para melhorar o desempenho do processamento. Essa estratégia foi incorporada a um arcabouço de processamento de grafos denominado M-Flash e desenvolvido durante a realização deste trabalho.Foram conduzidos experimentos a fim de avaliar as técnicas propostas. Os resultados mostraram que a técnica de visualização StructMatrix permitiu uma exploração eficiente e interativa de grandes grafos. Além disso, a avaliação do arcabouço M-Flash apresentou ganhos significativos sobre todas as abordagens baseadas em memória secundária do estado da arte. Ambas as contribuições foram validadas em eventos de revisão por pares, demonstrando o potencial analítico deste trabalho em domínios associados a grafos em larga escala.