201 research outputs found
Faceted Search of Heterogeneous Geographic Information for Dynamic Map Projection
This paper proposes a faceted information exploration model that supports
coarse-grained and fine-grained focusing of geographic maps by offering a
graphical representation of data attributes within interactive widgets. The
proposed approach enables (i) a multi-category projection of long-lasting
geographic maps, based on the proposal of efficient facets for data exploration
in sparse and noisy datasets, and (ii) an interactive representation of the
search context based on widgets that support data visualization, faceted
exploration, category-based information hiding and transparency of results at
the same time. The integration of our model with a semantic representation of
geographical knowledge supports the exploration of information retrieved from
heterogeneous data sources, such as Public Open Data and OpenStreetMap. We
evaluated our model with users in the OnToMap collaborative Web GIS. The
experimental results show that, when working on geographic maps populated with
multiple data categories, it outperforms simple category-based map projection
and traditional faceted search tools, such as checkboxes, in both user
performance and experience
TimberTrek: Exploring and Curating Sparse Decision Trees with Interactive Visualization
Given thousands of equally accurate machine learning (ML) models, how can
users choose among them? A recent ML technique enables domain experts and data
scientists to generate a complete Rashomon set for sparse decision trees--a
huge set of almost-optimal interpretable ML models. To help ML practitioners
identify models with desirable properties from this Rashomon set, we develop
TimberTrek, the first interactive visualization system that summarizes
thousands of sparse decision trees at scale. Two usage scenarios highlight how
TimberTrek can empower users to easily explore, compare, and curate models that
align with their domain knowledge and values. Our open-source tool runs
directly in users' computational notebooks and web browsers, lowering the
barrier to creating more responsible ML models. TimberTrek is available at the
following public demo link: https://poloclub.github.io/timbertrek.Comment: Accepted at IEEE VIS 2022. 5 pages, 6 figures. For a demo video, see
https://youtu.be/3eGqTmsStJM. For a live demo, visit
https://poloclub.github.io/timbertre
Hierarkkisessa asiakasdatassa ajan suhteen tapahtuvien muutosten visualisointi käyttäen visualisointikirjastoa Plotly Python Graphing Library
Time-dependent hierarchical data is a complex type of data that is difficult to visualize in a clear manner. It can be found in many real-life situations, for example in customer analysis, but the best practices for visualizing this type of data are not commonly known in business world.
This thesis focuses on visualizing changes over time in hierarchical customer data using the Plotly Python Graphing Library and is written as an assignment for a Finnish company. The thesis consists of a literature survey and experimental part. The literature survey introduces the most common hierarchical visualization methods, and the different possible encoding techniques for adding time dimension on top of these hierarchical visualization methods. Moreover, the pros and cons of different visualization techniques and encodings are discussed about.
In the experimental part of the thesis, visualization prototypes are designed using the Plotly Python Graphing Library. A company customer data set of the commissioning company is partitioned into hierarchical customer segments by a hierarchical industrial classification TOL 2008, and changes over time in a continuous variable are visualized by these segments. Two hierarchical visualization techniques: the sunburst chart and treemap, are used to create two prototype versions, and the combination of color, typography, and interaction is used to encode time dimension in these prototypes. The same prototypes are also exploited to visualize customer segments by an artificial hierarchy created by combining multiple categorical features into a hierarchical structure.
The prototypes are validated in the commissioning company by arranging an end user study and expert review. Concerning the prototypes by the industrial classification: According to the end users and experts, both prototype versions are very useful and well-implemented. Among the end users, there was no significant difference in which one of these prototype versions is faster to use, but the clear majority of the respondents regarded the sunburst chart version as their favorite prototype. The two experts who participated in the expert review had different opinions on which one of the prototype versions they would select to be utilized in practice. Concerning the prototypes by the artificial hierarchy: These prototypes also received positive feedback, but the possibility to change the order of features in the hierarchy was considered as an extremely important development idea.
ACM Computing Classification System (CCS):
Human-Centered Computing → Visualization → Visualization Techniques
Human-Centered Computing → Visualization → Empirical Studies in Visualizatio
A Pattern Approach to Examine the Design Space of Spatiotemporal Visualization
Pattern language has been widely used in the development of visualization systems. This dissertation applies a pattern language approach to explore the design space of spatiotemporal visualization. The study provides a framework for both designers and novices to communicate, develop, evaluate, and share spatiotemporal visualization design on an abstract level. The touchstone of the work is a pattern language consisting of fifteen design patterns and four categories. In order to validate the design patterns, the researcher created two visualization systems with this framework in mind. The first system displayed the daily routine of human beings via a polygon-based visualization. The second system showed the spatiotemporal patterns of co-occurring hashtags with a spiral map, sunburst diagram, and small multiples. The evaluation results demonstrated the effectiveness of the proposed design patterns to guide design thinking and create novel visualization practices
MOOClm: Learner Modelling for MOOCs
Massively Open Online Learning systems, or MOOCs, generate enormous quantities of learning data. Analysis of this data has considerable potential benefits for learners, educators, teaching administrators and educational researchers. How to realise this potential is still an open question. This thesis explores use of such data to create a rich Open Learner Model (OLM). The OLM is designed to take account of the restrictions and goals of lifelong learner model usage. Towards this end, we structure the learner model around a standard curriculum-based ontology. Since such a learner model may be very large, we integrate a visualisation based on a highly scalable circular treemap representation. The visualisation allows the student to either drill down further into increasingly detailed views of the learner model, or filter the model down to a smaller, selected subset. We introduce the notion of a set of Reference learner models, such as an ideal student, a typical student, or a selected set of learning objectives within the curriculum. Introducing these provides a foundation for a learner to make a meaningful evaluation of their own model by comparing against a reference model. To validate the work, we created MOOClm to implement this framework, then used this in the context of a Small Private Online Course (SPOC) run at the University of Sydney. We also report a qualitative usability study to gain insights into the ways a learner can make use of the OLM. Our contribution is the design and validation of MOOClm, a framework that harnesses MOOC data to create a learner model with an OLM interface for student and educator usage
Um modelo para suporte automatizado ao reconhecimento, extração, personalização e reconstrução de gráficos estáticos
Data charts are widely used in our daily lives, being present in regular media,
such as newspapers, magazines, web pages, books, and many others. A well constructed
data chart leads to an intuitive understanding of its underlying data
and in the same way, when data charts have wrong design choices, a redesign
of these representations might be needed. However, in most cases, these
charts are shown as a static image, which means that the original data are not
usually available. Therefore, automatic methods could be applied to extract the
underlying data from the chart images to allow these changes. The task of
recognizing charts and extracting data from them is complex, largely due to the
variety of chart types and their visual characteristics.
Computer Vision techniques for image classification and object detection are
widely used for the problem of recognizing charts, but only in images without
any disturbance. Other features in real-world images that can make this task
difficult are not present in most literature works, like photo distortions, noise,
alignment, etc. Two computer vision techniques that can assist this task and
have been little explored in this context are perspective detection and
correction. These methods transform a distorted and noisy chart in a clear
chart, with its type ready for data extraction or other uses. The task of
reconstructing data is straightforward, as long the data is available the
visualization can be reconstructed, but the scenario of reconstructing it on the
same context is complex.
Using a Visualization Grammar for this scenario is a key component, as these
grammars usually have extensions for interaction, chart layers, and multiple
views without requiring extra development effort.
This work presents a model for automated support for custom recognition, and
reconstruction of charts in images. The model automatically performs the
process steps, such as reverse engineering, turning a static chart back into its
data table for later reconstruction, while allowing the user to make modifications
in case of uncertainties. This work also features a model-based architecture
along with prototypes for various use cases. Validation is performed step by
step, with methods inspired by the literature. This work features three use
cases providing proof of concept and validation of the model.
The first use case features usage of chart recognition methods focused on
documents in the real-world, the second use case focus on vocalization of
charts, using a visualization grammar to reconstruct a chart in audio format,
and the third use case presents an Augmented Reality application that
recognizes and reconstructs charts in the same context (a piece of paper)
overlaying the new chart and interaction widgets. The results showed that with
slight changes, chart recognition and reconstruction methods are now ready for
real-world charts, when taking time, accuracy and precision into consideration.Os gráficos de dados são amplamente utilizados na nossa vida diária, estando
presentes nos meios de comunicação regulares, tais como jornais, revistas,
páginas web, livros, e muitos outros. Um gráfico bem construído leva a uma
compreensão intuitiva dos seus dados inerentes e da mesma forma, quando
os gráficos de dados têm escolhas de conceção erradas, poderá ser
necessário um redesenho destas representações. Contudo, na maioria dos
casos, estes gráficos são mostrados como uma imagem estática, o que
significa que os dados originais não estão normalmente disponíveis. Portanto,
poderiam ser aplicados métodos automáticos para extrair os dados inerentes
das imagens dos gráficos, a fim de permitir estas alterações. A tarefa de
reconhecer os gráficos e extrair dados dos mesmos é complexa, em grande
parte devido à variedade de tipos de gráficos e às suas características visuais.
As técnicas de Visão Computacional para classificação de imagens e deteção
de objetos são amplamente utilizadas para o problema de reconhecimento de
gráficos, mas apenas em imagens sem qualquer ruído. Outras características
das imagens do mundo real que podem dificultar esta tarefa não estão
presentes na maioria das obras literárias, como distorções fotográficas, ruído,
alinhamento, etc. Duas técnicas de visão computacional que podem ajudar
nesta tarefa e que têm sido pouco exploradas neste contexto são a deteção e
correção da perspetiva. Estes métodos transformam um gráfico distorcido e
ruidoso em um gráfico limpo, com o seu tipo pronto para extração de dados
ou outras utilizações. A tarefa de reconstrução de dados é simples, desde que
os dados estejam disponíveis a visualização pode ser reconstruída, mas o
cenário de reconstrução no mesmo contexto é complexo.
A utilização de uma Gramática de Visualização para este cenário é um
componente chave, uma vez que estas gramáticas têm normalmente
extensões para interação, camadas de gráficos, e visões múltiplas sem exigir
um esforço extra de desenvolvimento.
Este trabalho apresenta um modelo de suporte automatizado para o
reconhecimento personalizado, e reconstrução de gráficos em imagens
estáticas. O modelo executa automaticamente as etapas do processo, tais
como engenharia inversa, transformando um gráfico estático novamente na
sua tabela de dados para posterior reconstrução, ao mesmo tempo que
permite ao utilizador fazer modificações em caso de incertezas. Este trabalho
também apresenta uma arquitetura baseada em modelos, juntamente com
protótipos para vários casos de utilização. A validação é efetuada passo a
passo, com métodos inspirados na literatura. Este trabalho apresenta três
casos de uso, fornecendo prova de conceito e validação do modelo.
O primeiro caso de uso apresenta a utilização de métodos de reconhecimento
de gráficos focando em documentos no mundo real, o segundo caso de uso
centra-se na vocalização de gráficos, utilizando uma gramática de visualização
para reconstruir um gráfico em formato áudio, e o terceiro caso de uso
apresenta uma aplicação de Realidade Aumentada que reconhece e reconstrói
gráficos no mesmo contexto (um pedaço de papel) sobrepondo os novos
gráficos e widgets de interação. Os resultados mostraram que com pequenas
alterações, os métodos de reconhecimento e reconstrução dos gráficos estão
agora prontos para os gráficos do mundo real, tendo em consideração o
tempo, a acurácia e a precisão.Programa Doutoral em Engenharia Informátic
- …