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De nos jours, nous générons une quantité immensément grande de données juste en accomplissant nos simples tâches quotidiennes. L'analyse de ces données soulève des challenges ardus. Dans cette thèse, nous nous intéressons à deux aspects des données relationnelles. En premier lieu, nous considérons les données relationnelles dans lesquelles les relations sont pondérées. Un exemple concret serait le nombre commun de suiveurs entre deux utilisateurs de Twitter. Dans un deuxième temps, nous abordons le cas dynamique de ces données qui est inhérent à leur nature. Par exemple, le nombre de suiveurs communs pourrait changer au fil du temps. Dans cette thèse nous utilisons les graphes pour modéliser ces données qui sont à la fois complexes et évolutives. Les travaux de cette thèse s'articulent aussi autour de la détection de communautés pour les graphes pondérés et dynamiques. Pour un utilisateur expert, l'identification de ces communautés pourrait l'aider à comprendre la sémantique sous-jacente à la structure du graphe. Notre hypothèse repose sur l'utilisation des triangles comme ossature pour la détection de communautés. Cela nous a amenés à proposer plusieurs algorithmes : Séparation et évaluation, recherche gloutonne, heuristiques et algorithme génétique sont proposés. En se basant sur cet ensemble de triangles, nous proposons un algorithme de détection de communautés, appelé Tribase. L'idée conductrice de cet algorithme est de comparer les poids des communautés, permettant aux communautés dominantes d'acquérir plus de membres. Les résultats de l'étude comparative sur le benchmark LFR montrent que l'algorithme que nous proposons parvient à détecter les communautés dans les graphes dans lesquels une structure de communautés existe. De plus, l'applicabilité de notre algorithme a été testée sur des données réelles du projet ANR Info-RSN. Dans l'optique d'accompagner l'utilisateur expert dans son processus d'acquisition de l'information, une application visuelle et interactive a été implémentée. NLCOMS (Nœud-Lien et COMmunautéS) propose une panoplie de vues synchronisées pour la représentation de l'information. Par ailleurs, nous proposons dans cette thèse un algorithme de détection de communautés pour les graphes pondérés et dynamiques, appelé Dyci. Dyci permet de gérer les différents scénarios de mise à jour possibles de la structure du graphe. L'idée principale de Dyci est de guetter au cours du temps l'affaiblissement d'une communauté (en termes de poids) dans le but de reconsidérer localement sa place dans la structure, évitant ainsi une réindentification globale des communautés. Une étude comparative a été menée montrant que l'algorithme que nous proposons offre un bon compromis entre la solution obtenue et le temps de calcul. Finalement, l'intégration dans NLCOMS des visualisations adéquates pour la variante dynamique a été effectuée / Relational data in our society are on a constant increasing, rising arduous challenges. In this thesis, we consider two aspects of relational data. First, we are interested in relational data with weighted relationship. As a concrete example, relationships among Twitter's users could be weighted with regard to their shared number of followers. The second aspect is related to the dynamism which is inherent to data nature. As an instance, in the previous example the number of common followers between two Twitter's users can change over time. In order to handle these complex and dynamic relational data, we use the modelling strength of graphs. Another facet considered in this thesis deals with community identification on weighted and dynamic graphs. For an analyst, the community detection might be helpful to grasp the semantic behind the graph structure. Our assumption relies on the idea to use a set of disjoint pairwise triangles as a basis to detect the community structure. To select these triangles, several algorithms are proposed (i.e., branch-and-bound, greedy search, heuristics and genetic algorithm). Thereafter, we propose a community detection algorithm, called Tribase. In the latter, the weights of communities are compared allowing dominant communities to gain in size. Tribase is compared with the well-known LFR benchmark. The results show that Tribase identifies efficiently the communities while a community structure exists. Additionally, to asset Tribase on real-world data, we consider social networks data, especially Twitter's data, of the ANR-Info-RSN project. In order to support the analyst in its knowledge acquisition, we elaborate a visual interactive approach. To this end, an interactive application, called NLCOMS is introduced. NLCOMS uses multiple synchronous views for visualizing community structure and the related information. Furthermore, we propose an algorithm for the identification of communities over time, called Dyci. The latter takes advantage from the previously detected communities. Several changes' scenarios are considered like, node/edge addition, node/edge removing and edge weight update. The main idea of the proposed algorithm is to track whether a part of the weighted graph becomes weak over time, in order to merge it with the "dominant" neighbour community. In order to assess the quality of the returned community structure, we conduct a comparison with a genetic algorithm on real-world data of the ARN-Info-RSN project. The conducted comparison shows that Dyci algorithm provides a good trade-off between efficiency and consumed time. Finally, the dynamic changes which occur to the underlying graph structure can be visualized with NLCOMS which combines physical an axial time to fulfil this need

Thesis (M.C.S.)--Miami University, Dept. of Computer Science and Systems Analysis, 2007. / Title from first page of PDF document. Includes bibliographical references (p. 86-88).

The main task of this thesis was to create a software prototype of an analysing tool that would work as a compliment to IT-Maskinens event application, which is used as a digital tool for events. The requirement for the prototype was that it would be able to identify the user, gather relevant data from a possible storage location and visualize this in an aesthetically pleasing manner.   The thesis contained two different parts, investigation and implementation. The investigation included to determine which tool that would be used for the visualization of the data, which data likeminded companies thought was important and which information that was possible to extract from IT-Maskinens event application and indoor positioning system. How the software prototype was going to be implemented was also something that was investigated, in terms of which programming environment, library and language that could meet the requirements that were set for the prototype.   The implementation part of the thesis included the creation of a software prototype in ASP.NET MVC5 and Google Chart Tools. The creation of the software prototype was created using the programming languages C#, HTML, Razor, CSS, JavaScript and jQuery. / Huvuduppgiften i detta examensarbete har varit att skapa en programprototyp avett analyseringsverktyg tillhörande företaget IT-Maskinens eventapplikationen, som används som ett digitalt hjälpmedel vid event. Kravet på prototypen var att den skulle kunna identifiera användaren, samla in relevant data från en eventuell lagringsplats samt visualisera denna på ett estetiskt tillfredsställande sätt.   Examensarbetet innehöll två olika delar, utredning och implementation. Utredningen innefattade att fastslå vilket visualiseringsverktyg som skulle användas, vilken information liknande företag ansåg vara viktig samt vilken information som var möjlig att ta fram från IT-Maskinens eventapplikation och inomhuspositioneringssystem. Hur programprototypen skulle implementeras var också något som utreddes, i form av vilken programmeringsmiljö, vilket programmeringsbibliotek/programmeringsspråk som kunde uppfylla de krav som fanns på prototypen.   Implementationsdelen av examensarbetet innefattade skapande av programprototypen i ASP.NET MVC5 och Google Chart Tools. Skapandet av programprototypen gjordes i programmeringsspråken C#, HTML, Razor, CSS, JavaScript och jQuery.

Organizations can reduce carbon emissions by collecting data on what their emissions are and replace activities that are emission dense. To aid exploration, emission data can be visualized using a data visualization tool. A basic principle of designing a data visualization tool is the Visual Information Seeking Mantra (VISM): Overview first, zoom and filter, then details-on-demand. The company Measure & Change, which created a tool automating the retrieval and calculation of an organization’s emission data, has also designed a visualization tool for exploring such data. However, this tool was not created with the VISM in mind. This thesis discusses and evaluate (1) How can the data visualization tool be optimized using the design principle of Overview first, zoom and filter, details-on-demand? and (2) What is the impact of re-designing the data visualization tool in such a way? Through analysis of the dataset, user tasks, and the Visual Information Seeking Mantra, a new prototype was created. Both prototypes were then evaluated in A/B tests, with thinking aloud, a semi-structured interview, and the standardized ICE-T survey. The results suggest that the changes improved the tool, and that the user value increased. The ICE-T scores put the old implementation at 4.7, and the new prototype at 6. Visualizations scoring 5 or higher are generally accepted as valuable.

We present a Research through Design project that explores design opportunities and challenges of a personal visualization tool in artistic settings. The research process includes technical material exploration, an approach where state-of-the-art enabling techniques are examined to inspire artistic design decision-making. We propose three experiential qualities of such a tool: uniqueness, memorability, and aesthetics. They also serve as the heuristics throughout the design and evaluation process, aiming for meaningful user experience of personal visualization. Furthermore, we present how proceduralism democratizes user-generated content creation, how immersiveness improves the overall virtual experience, and how various computer graphics techniques are combined to improve the accessibility, learnability, and usability of the tool. / Vi presenterar en forskning genom ett designprojekt som utforskar möjligheter och utmaningar för ett personligt visualiseringsverktyg i konstnärliga scenarier. Forskningsprocessen inkluderar teknisk materialutforskning, ett tillvägagångssätt där state-of-the-art möjliggörande tekniker undersöks för att inspirera till beslutsfattande i konstnärlig design. Vi föreslår tre upplevelsemässiga egenskaper hos ett sådant verktyg: unikhet, minnesbarhet och estetik. Dessa egenskaper agerar även som heuristiken genom hela design- och utvärderingsprocessen, som syftar till en meningsfull användarupplevelse av personlig visualisering. Dessutom presenterar vi hur proceduralism demokratiserar användargenererat innehållsskapande, hur uppslukande förbättrar den övergripande virtuella upplevelsen och hur olika datorgrafiktekniker kan kombineras för att förbättra tillgängligheten, lärbarheten samt användbarheten av verktyget.

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As the public and government officials become aware of the impact of public health on communities, it is important that relevant public health statistics be available for decision making. Existing web resources have limited visualization options, cannot visually compare a county to all others in the US, and cannot compare the counties in an arbitrary region to all others in the US. The College of Public Health Indicator Visualization Tool (CPHIVT) is a web application providing visualization and ranking for a county in the US in comparison to all counties for a specific health indicator. An iterative development methodology was used to complete major features and refine the features over time. Features divided into small tasks that could be completed within two-week cycles. After the first version of the web application was completed and presented to the client, client feedback on the application was used to refine specifications and was incorporated into planning for future iterations. Iterative development was adopted with a focus on improving and expanding existing features and making the application publicly available online. A suite of automated user interface tests is being developed to verify the application’s functions. Making a complete version of the application publicly available involves significant research and software configuration to deploy the web application in a secure and performant manner. The web application has two major components corresponding to its two major user groups. The first component allows authenticated users from the Department of Public Health to upload and manage sets of data for various health indicators. Tools are included to automatically process uploaded data points. This allows the information presented on the web site to be expanded and kept up to date over time with minimal effort. The second component is accessible to anyone and allows a user to choose to a state or county with text search or hierarchical navigation. The application then provides graphical charts showing that location’s standing for various health indicators compared to all other counties nationally. This is accomplished by applying percentile rankings to the counties and plotting the percentiles against the values for a selected indicator. A user can save a generated chart to a variety of export formats including PNG image or PDF document. The application is expected to serve as a tool for many community members. Staff and students at the College of Public Health will use this tool for presentations and research. County health departments will be able to use the tool when planning community programs. County government leaders can use this tool to determine areas of need in the community. Decision makers will have the ability to visualize their county or region as compared to the nation, not just to neighboring counties or within a state.