Visualization systems traditionally focus on graphical representation of information. They tend not to provide integrated analytical services that could aid users in tackling complex knowledge discovery tasks. Users¡¯ exploration in such environments is usually impeded due to several problems: 1) Valuable information is hard to discover, when too much data is visualized on the screen. 2) They have to manage and organize their discoveries off line, because no systematic discovery management mechanism exists. 3) Their discoveries based on visual exploration alone may lack accuracy. 4) They have no convenient access to the important knowledge learned by other users. To tackle these problems, it has been recognized that analytical tools must be introduced into visualization systems. In this paper, we present a novel analysis-guided exploration system, called the Nugget Management System (NMS). It leverages the collaborative effort of human comprehensibility and machine computations to facilitate users¡¯ visual exploration process. Specifically, NMS first extracts the valuable information (nuggets) hidden in datasets based on the interests of users. Given that similar nuggets may be re-discovered by different users, NMS consolidates the nugget candidate set by clustering based on their semantic similarity. To solve the problem of inaccurate discoveries, data mining techniques are applied to refine the nuggets to best represent the patterns existing in datasets. Lastly, the resulting well-organized nugget pool is used to guide users¡¯ exploration. To evaluate the effectiveness of NMS, we integrated NMS into XmdvTool, a freeware multivariate visualization system. User studies were performed to compare the users¡¯ efficiency and accuracy of finishing tasks on real datasets, with and without the help of NMS. Our user studies confirmed the effectiveness of NMS. Keywords: Visual Analytics, Visual Knowledge

As estrategias de passes têm sido sempre de interesse para a pesquisa de futebol. Desde os inícios do futebol, os técnicos tem usado olheiros, gravações de vídeo, exercícios de treinamento e feeds de dados para coletar informações sobre as táticas e desempenho dos jogadores. No entanto, a natureza dinâmica das estratégias de passes são bastante complexas para refletir o que está acontecendo dentro do campo e torna difícil o entendimento do jogo. Além disso, existe uma demanda crecente pela deteção de padrões e analise de estrategias de passes popularizado pelo tiki-taka utilizado pelo FC. Barcelona. Neste trabalho, propomos uma abordagem para abstrair as sequências de pases e agrupálas baseadas na geometria da trajetória da bola. Para analizar as estratégias de passes, apresentamos um esquema de visualização interátiva para explorar a frequência de uso, a localização espacial e ocorrência temporal das sequências. A visualização Frequency Stripes fornece uma visão geral da frequencia dos grupos achados em tres regiões do campo: defesa, meio e ataque. O heatmap de trajetórias coordenado com a timeline de passes permite a exploração das formas mais recorrentes no espaço e tempo. Os resultados demostram oito trajetórias comunes da bola para sequências de três pases as quais dependem da posição dos jogadores e os ângulos de passe. Demonstramos o potencial da nossa abordagem com utilizando dados de várias partidas do Campeonato Brasileiro sob diferentes casos de estudo, e reportamos os comentários de especialistas em futebol. / Passing strategies analysis has always been of interest for soccer research. Since the beginning of soccer, managers have used scouting, video footage, training drills and data feeds to collect information about tactics and player performance. However, the dynamic nature of passing strategies is complex enough to reflect what is happening in the game and makes it hard to understand its dynamics. Furthermore, there exists a growing demand for pattern detection and passing sequence analysis popularized by FC Barcelona’s tiki-taka. We propose an approach to abstract passing strategies and group them based on the geometry of the ball trajectory. To analyse passing sequences, we introduce a interactive visualization scheme to explore the frequency of usage, spatial location and time occurrence of the sequences. The frequency stripes visualization provide, an overview of passing groups frequency on three pitch regions: defense, middle, attack. A trajectory heatmap coordinated with a passing timeline allow, for the exploration of most recurrent passing shapes in temporal and spatial domains. Results show eight common ball trajectories for three-long passing sequences which depend on players positioning and on the angle of the pass. We demonstrate the potential of our approach with data from the Brazilian league under several case studies, and report feedback from a soccer expert.

As estrategias de passes têm sido sempre de interesse para a pesquisa de futebol. Desde os inícios do futebol, os técnicos tem usado olheiros, gravações de vídeo, exercícios de treinamento e feeds de dados para coletar informações sobre as táticas e desempenho dos jogadores. No entanto, a natureza dinâmica das estratégias de passes são bastante complexas para refletir o que está acontecendo dentro do campo e torna difícil o entendimento do jogo. Além disso, existe uma demanda crecente pela deteção de padrões e analise de estrategias de passes popularizado pelo tiki-taka utilizado pelo FC. Barcelona. Neste trabalho, propomos uma abordagem para abstrair as sequências de pases e agrupálas baseadas na geometria da trajetória da bola. Para analizar as estratégias de passes, apresentamos um esquema de visualização interátiva para explorar a frequência de uso, a localização espacial e ocorrência temporal das sequências. A visualização Frequency Stripes fornece uma visão geral da frequencia dos grupos achados em tres regiões do campo: defesa, meio e ataque. O heatmap de trajetórias coordenado com a timeline de passes permite a exploração das formas mais recorrentes no espaço e tempo. Os resultados demostram oito trajetórias comunes da bola para sequências de três pases as quais dependem da posição dos jogadores e os ângulos de passe. Demonstramos o potencial da nossa abordagem com utilizando dados de várias partidas do Campeonato Brasileiro sob diferentes casos de estudo, e reportamos os comentários de especialistas em futebol. / Passing strategies analysis has always been of interest for soccer research. Since the beginning of soccer, managers have used scouting, video footage, training drills and data feeds to collect information about tactics and player performance. However, the dynamic nature of passing strategies is complex enough to reflect what is happening in the game and makes it hard to understand its dynamics. Furthermore, there exists a growing demand for pattern detection and passing sequence analysis popularized by FC Barcelona’s tiki-taka. We propose an approach to abstract passing strategies and group them based on the geometry of the ball trajectory. To analyse passing sequences, we introduce a interactive visualization scheme to explore the frequency of usage, spatial location and time occurrence of the sequences. The frequency stripes visualization provide, an overview of passing groups frequency on three pitch regions: defense, middle, attack. A trajectory heatmap coordinated with a passing timeline allow, for the exploration of most recurrent passing shapes in temporal and spatial domains. Results show eight common ball trajectories for three-long passing sequences which depend on players positioning and on the angle of the pass. We demonstrate the potential of our approach with data from the Brazilian league under several case studies, and report feedback from a soccer expert.

As estrategias de passes têm sido sempre de interesse para a pesquisa de futebol. Desde os inícios do futebol, os técnicos tem usado olheiros, gravações de vídeo, exercícios de treinamento e feeds de dados para coletar informações sobre as táticas e desempenho dos jogadores. No entanto, a natureza dinâmica das estratégias de passes são bastante complexas para refletir o que está acontecendo dentro do campo e torna difícil o entendimento do jogo. Além disso, existe uma demanda crecente pela deteção de padrões e analise de estrategias de passes popularizado pelo tiki-taka utilizado pelo FC. Barcelona. Neste trabalho, propomos uma abordagem para abstrair as sequências de pases e agrupálas baseadas na geometria da trajetória da bola. Para analizar as estratégias de passes, apresentamos um esquema de visualização interátiva para explorar a frequência de uso, a localização espacial e ocorrência temporal das sequências. A visualização Frequency Stripes fornece uma visão geral da frequencia dos grupos achados em tres regiões do campo: defesa, meio e ataque. O heatmap de trajetórias coordenado com a timeline de passes permite a exploração das formas mais recorrentes no espaço e tempo. Os resultados demostram oito trajetórias comunes da bola para sequências de três pases as quais dependem da posição dos jogadores e os ângulos de passe. Demonstramos o potencial da nossa abordagem com utilizando dados de várias partidas do Campeonato Brasileiro sob diferentes casos de estudo, e reportamos os comentários de especialistas em futebol. / Passing strategies analysis has always been of interest for soccer research. Since the beginning of soccer, managers have used scouting, video footage, training drills and data feeds to collect information about tactics and player performance. However, the dynamic nature of passing strategies is complex enough to reflect what is happening in the game and makes it hard to understand its dynamics. Furthermore, there exists a growing demand for pattern detection and passing sequence analysis popularized by FC Barcelona’s tiki-taka. We propose an approach to abstract passing strategies and group them based on the geometry of the ball trajectory. To analyse passing sequences, we introduce a interactive visualization scheme to explore the frequency of usage, spatial location and time occurrence of the sequences. The frequency stripes visualization provide, an overview of passing groups frequency on three pitch regions: defense, middle, attack. A trajectory heatmap coordinated with a passing timeline allow, for the exploration of most recurrent passing shapes in temporal and spatial domains. Results show eight common ball trajectories for three-long passing sequences which depend on players positioning and on the angle of the pass. We demonstrate the potential of our approach with data from the Brazilian league under several case studies, and report feedback from a soccer expert.

Dynamic query expansion is a method of automatically identifying terms relevant to a target domain based on an incomplete query input. With the explosive growth of online media, such tools are essential for efficient search result refining to track emerging themes in noisy, unstructured text streams. It's crucial for large-scale predictive analytics and decision-making, systems which use open source indicators to find meaningful information rapidly and accurately. The problems of information overload and semantic mismatch are systemic during the Information Retrieval (IR) tasks undertaken by such systems. In this dissertation, we develop approaches to dynamic query expansion algorithms that can help improve the efficacy of such systems using only a small set of seed queries and requires no training or labeled samples. We primarily investigate four significant problems related to the retrieval and assessment of event-related information, viz. (1) How can we adapt the query expansion process to support rank-based analysis when tracking a fixed set of entities? A scalable framework is essential to allow relative assessment of emerging themes such as airport threats. (2) What visual knowledge discovery framework to adopt that can incorporate users' feedback back into the search result refinement process? A crucial step to efficiently integrate real-time `situational awareness' when monitoring specific themes using open source indicators. (3) How can we contextualize query expansions? We focus on capturing semantic relatedness between a query and reference text so that it can quickly adapt to different target domains. (4) How can we synchronously perform knowledge discovery and characterization (unstructured to structured) during the retrieval process? We mainly aim to model high-order, relational aspects of event-related information from microblog texts. / Ph. D. / Analysis of real-time, social media can provide critical insights into ongoing societal events. Where consequences and implications of specific events include monetary losses, threats to critical infrastructure and national security, disruptions to daily life, and a potential to cause loss of life and physical property. It is imperative for developing good ‘ground truth’ to develop adequate data-driven information systems, i.e., an authoritative record of events reported in the media cataloged alongside important dimensions. Availability of high-quality ground truth events can support various analytic efforts, e.g., identifying precursors of attacks, developing predictive indicators using surrogate data sources, and tracking the progression of events over space and time. A dynamic search result refinement is useful for expanding a general set of user queries into a more relevant collection. The challenges of information overload and misalignment of context between the user query and retrieved results can overwhelm both human and machine. In this dissertation, we focus our efforts on these specific challenges. With the ever-increasing volume of user-generated data large-scale analysis is a tedious task. Our first focus is to develop a scalable model that dynamically tracks and ranks evolving topics as they appear in social media. Then to simplify the cognitive tasks involving sense-making of evolving themes, we take a visual approach to retrieve situationally critical and emergent information effectively. This visual analytics approach learns from user’s interactions during the exploratory process and then generates a better representation of the data. Thus, improving the situational understanding and usability of underlying data models. Such features are crucial for big-data based decision & support systems. To make the event-focused retrieval process more robust, we developed a context-rich procedure that adds new relevant key terms to the user’s original query by utilizing the linguistic structures in text. This context-awareness allows the algorithm to retrieve those relevant characteristics that can help users to gain adequate information from social media about real-world events. Online social commentary about events is very informal and can be incomplete. However, to get the complete picture and adequately describe these events we develop an approach that models the underlying relatedness of information and iteratively extract meaning and denotations from event-related texts. We learn how to express the high-order relationships between events and entities and group them to identify those attributes that best explain the events the user is trying to uncover. In all the augmentations we develop, our strategy is to allow only very minimal human supervision using just a small set of seed event triggers and requires no training or labeled samples. We show a comprehensive evaluation of these augmentations on real-world domains - threats on airports, cyber attacks, and protests. We also demonstrate their applicability as for real-time analysis that provides vital event characteristics, and contextually consistent information can be a beneficial aid for emergency responders.

The research area of the thesis is the process of knowledge discovery from multidimensional data and the ways of improving the perception of the data investigated. Data perception is rather a complex problem, especially when the data refer to complicated object described by many parameters. In order to obtain exhaustive information on the analysed data, their all-round analysis is indispensable the stages of which are defined by the process of knowledge discovery. The object of dissertation research is the process of visual knowledge discovery. The following subjects are directly associated with this subject: formation of a primary set of multidimensional data; algorithms for clusterization, visualization, and classification; evaluation of the results obtained by data mining methods; mapping of a new multidimensional data; decision making and generalization of the knowledge obtained referring to the analysis results. The key target of the thesis is to develop and explore the methodology of knowledge discovery by visual methods that would allow us to increase the efficiency of data analysis. The research results of the work revealed new opportunities of medical (physiological) data analysis. The dissertation is written in Lithuanian. It consists of 5 chapters, and the list of references. There are 116 pages of the text, 44 figures, 12 tables and 156 bibliographical sources. The main results of this dissertation were published in 9 scientific papers: 1 article in a journal... [to full text] / Disertacijos tyrimų sritis yra žinių gavybos iš daugiamačių duomenų procesas ir tiriamų duomenų suvokimo gerinimo būdai. Duomenų suvokimas yra sudėtingas uždavinys, ypač kai duomenys nurodo sudėtingą objektą, kuris aprašytas daugeliu parametrų. Norint gauti išsamią informaciją apie analizuojamus duomenis būtina kompleksinė jų analizė, kurios etapus apibrėžia žinių gavybos procesas. Disertacijos tyrimų objektas – vizualios žinių gavybos procesas. Su šiuo objektu betarpiškai susiję dalykai: daugiamačių duomenų pirminės aibės suformavimas; klasterizavimo, vizualizavimo ir klasifikavimo algoritmai; duomenų gavybos metodais gautų rezultatų įvertinimas; naujų daugiamačių duomenų atvaizdavimas; sprendimų priėmimas ir gautų žinių apibendrinimas, atsižvelgiant į analizės rezultatus. Pagrindinis disertacijos tikslas yra sukurti ir ištirti žinių gavybos vizualiais metodais metodologiją, kuri leistų padidinti duomenų analizės efektyvumą. Darbe atliktų tyrimų rezultatai atskleidė naujas medicininių (fiziologinių) duomenų analizės galimybes. Disertaciją sudaro penki skyriai ir literatūros sąrašas. Bendra disertacijos apimtis 116 puslapių, 44 paveikslai ir 12 lentelių. Tyrimų rezultatai publikuoti 9 moksliniuose leidiniuose: 1 straipsnis leidinyje, įtrauktame į Mokslinės informacijos instituto pagrindinį (Thomson ISI Web of Science) sąrašą; 2 straipsniai leidiniuose, įtrauktuose į Mokslinės informacijos instituto konferencijos darbų (Thomson ISI Proceedings) duomenų bazę; 2 straipsniai... [toliau žr. visą tekstą]

Disertacijos tyrimų sritis yra žinių gavybos iš daugiamačių duomenų procesas ir tiriamų duomenų suvokimo gerinimo būdai. Duomenų suvokimas yra sudėtingas uždavinys, ypač kai duomenys nurodo sudėtingą objektą, kuris aprašytas daugeliu parametrų. Norint gauti išsamią informaciją apie analizuojamus duomenis būtina kompleksinė jų analizė, kurios etapus apibrėžia žinių gavybos procesas. Disertacijos tyrimų objektas – vizualios žinių gavybos procesas. Su šiuo objektu betarpiškai susiję dalykai: daugiamačių duomenų pirminės aibės suformavimas; klasterizavimo, vizualizavimo ir klasifikavimo algoritmai; duomenų gavybos metodais gautų rezultatų įvertinimas; naujų daugiamačių duomenų atvaizdavimas; sprendimų priėmimas ir gautų žinių apibendrinimas, atsižvelgiant į analizės rezultatus. Pagrindinis disertacijos tikslas yra sukurti ir ištirti žinių gavybos vizualiais metodais metodologiją, kuri leistų padidinti duomenų analizės efektyvumą. Darbe atliktų tyrimų rezultatai atskleidė naujas medicininių (fiziologinių) duomenų analizės galimybes. Disertaciją sudaro penki skyriai ir literatūros sąrašas. Bendra disertacijos apimtis 116 puslapių, 44 paveikslai ir 12 lentelių. Tyrimų rezultatai publikuoti 9 moksliniuose leidiniuose: 1 straipsnis leidinyje, įtrauktame į Mokslinės informacijos instituto pagrindinį (Thomson ISI Web of Science) sąrašą; 2 straipsniai leidiniuose, įtrauktuose į Mokslinės informacijos instituto konferencijos darbų (Thomson ISI Proceedings) duomenų bazę; 2 straipsniai... [toliau žr. visą tekstą] / The research area of the thesis is the process of knowledge discovery from multidimensional data and the ways of improving the perception of the data investigated. Data perception is rather a complex problem, especially when the data refer to complicated object described by many parameters. In order to obtain exhaustive information on the analysed data, their all-round analysis is indispensable the stages of which are defined by the process of knowledge discovery. The object of dissertation research is the process of visual knowledge discovery. The following subjects are directly associated with this subject: formation of a primary set of multidimensional data; algorithms for clusterization, visualization, and classification; evaluation of the results obtained by data mining methods; mapping of a new multidimensional data; decision making and generalization of the knowledge obtained referring to the analysis results. The key target of the thesis is to develop and explore the methodology of knowledge discovery by visual methods that would allow us to increase the efficiency of data analysis. The research results of the work revealed new opportunities of medical (physiological) data analysis. The dissertation is written in Lithuanian. It consists of 5 chapters, and the list of references. There are 116 pages of the text, 44 figures, 12 tables and 156 bibliographical sources. The main results of this dissertation were published in 9 scientific papers: 1 article in a journal... [to full text]