<p>За потребе поређења скупова података осмишљен је приступ поређењу<br />који се заснива на употреби графова. У овом приступу развијене су две<br />врсте графовских представа: представе вредности које описују скуп<br />података и представе разлика које описују разлике између две<br />представе вредности. У испитивањима приступа над синтетичким и<br />реалним скуповима података, показано је да је кроз визуално<br />истраживање представа разлика и примену помоћних поступака обраде<br />могуће уочити корисне обрасце који приказују разлике између представа<br />вредности, а посредно и између скупова података описаних путем ових<br />представа вредности.</p> / <p>Za potrebe poređenja skupova podataka osmišljen je pristup poređenju<br />koji se zasniva na upotrebi grafova. U ovom pristupu razvijene su dve<br />vrste grafovskih predstava: predstave vrednosti koje opisuju skup<br />podataka i predstave razlika koje opisuju razlike između dve<br />predstave vrednosti. U ispitivanjima pristupa nad sintetičkim i<br />realnim skupovima podataka, pokazano je da je kroz vizualno<br />istraživanje predstava razlika i primenu pomoćnih postupaka obrade<br />moguće uočiti korisne obrasce koji prikazuju razlike između predstava<br />vrednosti, a posredno i između skupova podataka opisanih putem ovih<br />predstava vrednosti.</p> / <p>In order to support data set comparison, a graph-based approach to<br />comparison was devised. In this approach, two types of graph-based<br />representations were introduced: value representations that represent a data<br />set and difference representations that represent differences between two<br />value representations. The results of approach evaluations on synthetic and<br />real data sets indicate that, by visually exploring difference representations<br />and applying auxiliary procedures, it is possible to discover useful patterns<br />which describe differences between two value representations and,<br />consequently, differences between the data sets corresponding to the value<br />representations.</p>

With the increasing amount of valuable data that companies have access to the need for tools visualizing this data have reached a wider group of users, many of which are not tech-savvy. Self-service Business Intelligence applications aim to meet this need and many guidelines regarding the general design of Business Intelligence have been produced in recent years. In this thesis some of these guidelines are interpreted and applied during the development of a Qlik Sense application for the Device Connection Platform department at Ericsson. The purpose of this thesis is to produce more specific guidelines that aim to complement existing general guidelines on Self-service Business Intelligence design; guidelines that should be taken into account when developing Qlik Sense applications. As a result, five guidelines that concerns conditional dimensions, screen resolutions, naming conventions for master items, the data layer and Qlik Sense conventions for visualizations are presented. Pros and cons regarding these guidelines are discussed along with alternative approaches. The conclusion states that the general guidelines interpreted in this project work were helpful for the workflow and readability of the application, but that more specific guidelines such as the ones presented in the result could be well needed when it comes to customizabil ity and flexibility for end users. / Allt eftersom mängden värdefull data som företag har tillgång till ökar har behovet av verktyg som visualiserar dessa data nått en bredare grupp användare, där många är mindre tekniskt kunniga. Self-service Business Intelligence applikationer syftar till att möta detta behov och många generella riktlinjer för hur sådana applikationer ska designas har tagits fram under senare år. I denna rapport blir dessa riktlinjer tolkade och därefter applicerade under tiden av skapandet av en Self-service Business Intelligence applikation i mjukvaran Qlik Sense och för Ericsson Device Connection Platform. Syftet med rapporten är att utforma och presentera specifika riktlinjer för Qlik Sense att användas som komplement till de existerande och mer generella riktlinjerna för design av Self-service Business Intelligence i allmänhet. Rapportens resultat består av fem riktlinjer som avser villkorliga dimensioner, skärmupplösning, namnkonventioner för original, datalagret och Qlik Sense egna konventioner för visualiseringar. Föroch nackdelar med de framtagna riktlinjerna diskuteras tillsammans med alternativa tillvägagångssätt. Vi drar slutsatsen att de generella riktlinjerna som tolkats genom projektet i denna rapport var speciellt hjälpfulla för att uppnå läsbarhet och ett bra arbetsflöde för slutanvändaren i applikationen. Vidare konstateras att fler mjukvaruspecifika riktlinjer kan vara välbehövliga när det kommer till anpassningsbarhet av applikationer och flexibilitet för slutanvändare.

<p>The Ethiopia Commodity Exchange (ECX), like many other data intensive organizations, is having difficulties making full use of the vast amounts of data that it collects. This MSc thesis identifies areas within the organization where concepts from the academic fields of information visualization and visual analytics can be applied to address this issue.Software solutions are designed and implemented in two areas with the purpose of evaluating the approach and to demonstrate to potential users, developers and managers what can be achieved using this method. A number of presentation methods are proposed for the ECX website, which previously contained no graphing functionality for market data, to make it easier for users to find trends, patterns and outliers in prices and trade volumes of commodieties traded at the exchange. A software application is also developed to support the ECX market surveillance team by drastically improving its capabilities of investigating complex trader relationships.Finally, as ECX lacked previous experiences with visualization, one software developer was trained in computer graphics and involved in the work, to enable continued maintenance and future development of new visualization solutions within the organization.</p>

A challenge for the clinical management of Parkinson's disease (PD) is the large within- and between-patient variability in symptom profiles as well as the emergence of motor complications which represent a significant source of disability in patients. This thesis deals with the development and evaluation of methods and systems for supporting the management of PD by using repeated measures, consisting of subjective assessments of symptoms and objective assessments of motor function through fine motor tests (spirography and tapping), collected by means of a telemetry touch screen device. One aim of the thesis was to develop methods for objective quantification and analysis of the severity of motor impairments being represented in spiral drawings and tapping results. This was accomplished by first quantifying the digitized movement data with time series analysis and then using them in data-driven modelling for automating the process of assessment of symptom severity. The objective measures were then analysed with respect to subjective assessments of motor conditions. Another aim was to develop a method for providing comparable information content as clinical rating scales by combining subjective and objective measures into composite scores, using time series analysis and data-driven methods. The scores represent six symptom dimensions and an overall test score for reflecting the global health condition of the patient. In addition, the thesis presents the development of a web-based system for providing a visual representation of symptoms over time allowing clinicians to remotely monitor the symptom profiles of their patients. The quality of the methods was assessed by reporting different metrics of validity, reliability and sensitivity to treatment interventions and natural PD progression over time. Results from two studies demonstrated that the methods developed for the fine motor tests had good metrics indicating that they are appropriate to quantitatively and objectively assess the severity of motor impairments of PD patients. The fine motor tests captured different symptoms; spiral drawing impairment and tapping accuracy related to dyskinesias (involuntary movements) whereas tapping speed related to bradykinesia (slowness of movements). A longitudinal data analysis indicated that the six symptom dimensions and the overall test score contained important elements of information of the clinical scales and can be used to measure effects of PD treatment interventions and disease progression. A usability evaluation of the web-based system showed that the information presented in the system was comparable to qualitative clinical observations and the system was recognized as a tool that will assist in the management of patients.

The acquisition of data and its analysis has become a common yet critical task in many areas of modern economy and research. Unfortunately, the ever-increasing scale of datasets has long outgrown the capacities and abilities humans can muster to extract information from them and gain new knowledge. For this reason, research areas like data mining and knowledge discovery steadily gain importance. The algorithms they provide for the extraction of knowledge are mandatory prerequisites that enable people to analyze large amounts of information. Among the approaches offered by these areas, clustering is one of the most fundamental. By finding groups of similar objects inside the data, it aims to identify meaningful structures that constitute new knowledge. Clustering results are also often used as input for other analysis techniques like classification or forecasting. As clustering extracts new and unknown knowledge, it obviously has no access to any form of ground truth. For this reason, clustering results have a hypothetical character and must be interpreted with respect to the application domain. This makes clustering very challenging and leads to an extensive and diverse landscape of available algorithms. Most of these are expert tools that are tailored to a single narrowly defined application scenario. Over the years, this specialization has become a major trend that arose to counter the inherent uncertainty of clustering by including as much domain specifics as possible into algorithms. While customized methods often improve result quality, they become more and more complicated to handle and lose versatility. This creates a dilemma especially for amateur users whose numbers are increasing as clustering is applied in more and more domains. While an abundance of tools is offered, guidance is severely lacking and users are left alone with critical tasks like algorithm selection, parameter configuration and the interpretation and adjustment of results. This thesis aims to solve this dilemma by structuring and integrating the necessary steps of clustering into a guided and feedback-driven process. In doing so, users are provided with a default modus operandi for the application of clustering. Two main components constitute the core of said process: the algorithm management and the visual-interactive interface. Algorithm management handles all aspects of actual clustering creation and the involved methods. It employs a modular approach for algorithm description that allows users to understand, design, and compare clustering techniques with the help of building blocks. In addition, algorithm management offers facilities for the integration of multiple clusterings of the same dataset into an improved solution. New approaches based on ensemble clustering not only allow the utilization of different clustering techniques, but also ease their application by acting as an abstraction layer that unifies individual parameters. Finally, this component provides a multi-level interface that structures all available control options and provides the docking points for user interaction. The visual-interactive interface supports users during result interpretation and adjustment. For this, the defining characteristics of a clustering are communicated via a hybrid visualization. In contrast to traditional data-driven visualizations that tend to become overloaded and unusable with increasing volume/dimensionality of data, this novel approach communicates the abstract aspects of cluster composition and relations between clusters. This aspect orientation allows the use of easy-to-understand visual components and makes the visualization immune to scale related effects of the underlying data. This visual communication is attuned to a compact and universally valid set of high-level feedback that allows the modification of clustering results. Instead of technical parameters that indirectly cause changes in the whole clustering by influencing its creation process, users can employ simple commands like merge or split to directly adjust clusters. The orchestrated cooperation of these two main components creates a modus operandi, in which clusterings are no longer created and disposed as a whole until a satisfying result is obtained. Instead, users apply the feedback-driven process to iteratively refine an initial solution. Performance and usability of the proposed approach were evaluated with a user study. Its results show that the feedback-driven process enabled amateur users to easily create satisfying clustering results even from different and not optimal starting situations.

Made available in DSpace on 2016-06-02T19:06:17Z (GMT). No. of bitstreams: 1 6293.pdf: 6594637 bytes, checksum: 313540db5bd44c0b02cd3d086cf8c310 (MD5) Previous issue date: 2014-05-29 / Financiadora de Estudos e Projetos / Since its creation television has undergone major paradigm shifts, from black and white images to color displays, from analog systems to digital transmissions, from low quality display to high quality displays, from passive programs for fully interactive programs. Currently the term Social TV is gaining popularity in television and innovating once again the way people watch TV, combining computing and communication mechanisms to provide new ways to entertain and inform the viewer. The object of this work lies in the context of Social TV, explores the concept of support agents that have the role to stimulate social interactions, having the aim of enrich the quality of experience in watching television. This paper proposes a model of interaction between the agent and the viewers in Social TV systems and tests with users using a tool developed for the interaction model. Finally the paper compares the results of interactions to investigate the influence generated by inserting the support agent in an environment of Social TV. / Desde sua criação a televisão sofreu algumas mudanças de paradigmas, de imagens em preto e branco para telas coloridas, de sistemas analógicos para transmissões digitais, de qualidade de exibição de baixa resolução para sistemas de alta qualidade de imagem, de programas passivos para programas totalmente interativos. Atualmente o termo TV Social vem ganhando popularidade no meio televisivo e inovando mais uma vez a forma como as pessoas assistem televisão, combinando mecanismos de comunicação e computação para fornecer novas formas de entreter e informar o telespectador. O objeto de estudo deste trabalho situa-se no contexto de TV Social, explora o conceito de Agentes Incentivadores que possuem o papel de estimular interações sociais, possuindo a finalidade de alcançar enriquecimento da qualidade de experiência em assistir televisão. O trabalho propõe um modelo de interação entre o Agente Incentivador e os telespectadores de um sistema de TV Social e realiza testes com usuários utilizando a ferramenta de interação desenvolvida para o modelo. Por fim o trabalho analisa os resultados das interações para averiguar a influência gerada pela inserção do agente incentivador em um ambiente de TV Social.

Lors de l’exploration d’espace de conception, les données résultantes de la simulation d’un grand nombre d’alternatives de conception peuvent conduire à la surcharge d’information quand il s’agit de choisir une bonne solution de conception. Cette exploration d’espace de conception s’apparente à une méthode d’optimisation en conception multicritère mais en mode manuel pour lequel des outils appropriés à la visualisation de données multidimensionnelle sont employés. Pour le concepteur, un processus en trois phases – découverte, optimisation, sélection – est suivi selon un paradigme dit de Design by Shopping. Le fait de « parcourir » l’espace de conception permet de gagner en intuition sur les sous-espaces de solutions faisables et infaisables et sur les solutions offrant de bons compromis. Le concepteur apprend au cours de ces manipulations graphiques de données. La sélection d’une solution optimale se fait donc sur la base d’une décision dite informée. L’objectif de cette recherche est la performance des représentations graphiques pour l’exploration d’espace de conception, pour les trois phases du processus en Design by Shopping. Pour cela, cinq représentations graphiques, identifiées comme potentiellement performantes, sont testées à travers deux expérimentations. Dans la première, trente participants ont testé trois graphiques, pour la phase de sélection dans une situation multi-attribut, à travers trois scénarios de conception où une voiture doit être choisie parmi quarante selon des préférences énoncées. Pour cela, un indice de qualité est proposé pour calculer la qualité de la solution du concepteur pour un des trois scénarios définis, la solution optimale selon cet indice étant comparée à celles obtenues après manipulation des graphiques. Dans la deuxième expérimentation, quarante-deux concepteurs novices ont résolu deux problèmes de conception à l’aide de trois graphiques. Dans ce cas, la performance des graphiques est testée pour la prise de décision informée et pour les trois phases du processus dans une situation multi-objectif. Les résultats révèlent qu’un graphique est adapté à chacune des trois phases du Design by Shopping :: le graphique Scatter Plot Matrix pour la phase de découverte et pour la prise de décision informée, le graphique Simple Scatter pour la phase d’optimisation et le graphique Parallel Coordinate Plot pour la phase de sélection aussi bien dans une situation multi-attribut que multi-objectif. / In Design space exploration, the resulting data, from simulation of large amount of new design alternatives, can lead to information overload when one good design solution must be chosen. The design space exploration relates to a multi-criteria optimization method in design but in manual mode, for which appropriate tools to support multi-dimensional data visualization are employed. For the designer, a three-phase process - discovery, optimization, selection - is followed according to a paradigm called Design by Shopping. Exploring the design space helps to gain insight into both feasible and infeasible solutions subspaces, and into solutions presenting good trade-offs. Designers learn during these graphical data manipulations and the selection of an optimal solution is based on a so-called informed decision. The objective of this research is the performance of graphs for design space exploration according to the three phases of the Design by Shopping process. In consequence, five graphs, identified as potentially efficient, are tested through two experiments. In the first, thirty participants tested three graphs, in three design scenarios where one car must be chosen out of a total of forty, for the selection phase in a multi-attribute situation where preferences are enounced. A response quality index is proposed to compute the choice quality for each of the three given scenarios, the optimal solutions being compared to the ones resulting from the graphical manipulations. In the second experiment, forty-two novice designers solved two design problems with three graphs. In this case, the performance of graphs is tested for informed decision-making and for the three phases of the process in a multi-objective situation. The results reveal three efficient graphs for the design space exploration: the Scatter Plot Matrix for the discovery phase and for informed decision-making, the Simple Scatter Plot for the optimization phase and the Parallel Coordinate Plot for the selection phase in a multi-attribute as well as multi-objective situation.

This work concerns the research and application of data visualization techniques to depict ongoing activities in mankind’s investigation of space as part of a larger open-source visualization- and science-outreach software known as OpenSpace. It involves the construction of a physically accurate virtual environment of our local star group and solar system so as to facilitate development of a robust and generalized solution capable of articulating mission-science to its viewers. The research part is focused on deploying data visualization methods suitable for contextualizing scientific findings towards the general public in a pedagogical manner, with the end goal to provide a fully operational New Horizons visualization on the day of encounter with Pluto for the first public broadcast of OpenSpace across the globe.

隨著互動式資料視覺化工具越來越多，設計者需要一個方法來衡量其作品是否好用、能否被理解、使用效率高低。互動式資料視覺化需要透過使用者的互動才能觀察到資料的不同面向，再進一步產生洞見，然而現有的評估方式多僅聚焦於靜態資料圖表，設計者無法從中得知使用者的操作困難之處，並據此進行加強與改善，因此本研究提出一個整合量化分析與質化記錄的系統性評估方式，應用於互動式資料視覺化的優使性(usability)分析。 本研究的方法為追蹤使用者的眼動和滑鼠操作過程，先將其記錄成量化數據，透過興趣區域的標定與將轉換使用者行為成序列後，進行序列運算和統計分析；同時，從使用者經驗研究方法得到實驗過程的質化記錄，用來輔助解釋量化分析的結果。 本論文藉由兩個互動式資料視覺化工具來驗證以眼動與滑鼠追蹤評估互動式資料視覺化是可行的，我們提出了具體的實驗流程、量化紀錄與分析方式，並建議以下評估指標：吸引力、易發現性、困難度、易識別性、易理解性、精準表達程度、細部困難度、使用效率。 / As more and more interactive data visualization tools emerge, designers need an organized evaluation method to provide timely feedback and understand user behavior. In contrast to traditional graphical presentations, interactive data visualization tools call for user manipulation to gain specific insights. It is therefore imperative to study the intermediate operation process, rather than the final outcome, to provide a critical understanding of the developed tool. Toward this objective, we propose a systematic approach combining quantitative analysis and qualitative assessment to gauge the usability of interactive data visualization tools in this research. Firstly, quantitative data including gaze and mouse movements are collected. By combining the definition of area of interest, these trajectories can be converted into user sequences, which are conveniently accessible for further statistical analysis as well as path comparison. Secondly, qualitative information obtained by observing user operation is gathered to offer additional insight and complement/support conclusions obtained from quantitative analysis. Two interactive data visualization tools are employed to examine the feasibility and universality of our experimental and analytical procedure. To conclude, we come up with several key indicators to evaluate interactive data visualization, including attraction, discoverability, difficulty, identifiability, comprehensibility, precision of expression, difficulty(detailed) and efficiency.

Detta arbete inkluderar en undersökning av hur unga förhåller sig till brand content på Sociala medier. Resultaten av denna undersökning har bidragit till två slutprodukter som i sin tur levererats till två olika uppdragsgivare. Målgruppen är unga personer i åldrarna 15-24 bosatta i Sverige. Undersökningen har även inkluderat om målgruppen följer företag/varumärken på Sociala medier, anledningarna till varför de gör det, samt vad för slags innehåll som intresserar dem. Utifrån resultaten kan vi konstatera att ålder och/eller kön inte är avgörande för hur unga personer förhåller sig till, och konsumerar, brand content på sociala meder. Det beror snarare på personlighet och individuellt beteende. Av de data som har samlats in under arbetet har vi skapat två olika slutprodukter, en vardera till de olika uppdragsgivarna. / This project contains an analysis about how young people relate to brand content on Social media. The findings of the study have contributed to two end products that have been delivered to two different customers. The target group is between 15-24 years old and are residents in Sweden. The analysis has also included if the target group follow brands on Social media, reasons why they do, and what kind of content interests them. The report shows that age and/or gender does not correlate to how young people relate to, or consume, brand content on social media. The consumption of and relation to social media content is more dependent on aspects like the consumer’s personality and individual behaviour. From the data collected during the project we have created two different end products, one to each of the different clients.