data visualization – Page 2 – vialab

EduApps: Helping Non-Native English Speakers with Language Structure

Contributors:

Mariana Shimabukuro and Christopher Collins

First language (L1) influence errors are very frequent in English learners (L2), even more so when the learner’s proficiency level is higher (upper-intermediate/advanced). Our project aims to analyze errors made by learners from specific L1’s using learner corpora. Based on the analysis we want to focus on a specific type of error and research a way to identify it automatically in learners’ essays depending on their L1. This would allow us to implement an application that helps English as Second Language (ESL) students to identify and analyze their errors and to better understand the reasoning behind them, consequently improving the students’ English level.

About the EduApps initiative

EduApps is a suite of apps housed in an online environment that focuses on the health, well-being and development of one’s mind, body and community. Our research project titled, “There’s an App for That” is investigating the design process, development, implementation and evaluation of this suite of educational apps. Specifically, we are interested in helping students build confidence and competence in the cognitive, socio-emotional and physical domains. We are also interested in the impact a learning portal can have on students’ learning, teachers and the surrounding community. We hope that our research can build capacity for investigating and affecting innovation in formal and informal education settings in the use of digital technology. We have partnered with school boards and community organizations to develop and research the apps. More about each of the domains — their purpose, apps and related research can be found at http://eduapps.ca/.

Publications

Acknowledgements

Metatation: Annotation for Interaction to Bridge Close and Distant Reading

Contributors:

Hrim Mehta, Adam Bradley, Mark Hancock, and Christopher Collins

In the domain of literary criticism, many critics practice close reading, annotating by hand while performing a detailed analysis of a single text. Often this process employs the use of external resources to aid analysis. In this article, we present a study and subsequent tool design focused on leveraging a critic’s annotations as implicit interactions for initiating context-specific computational support that automatically searches external resources. We observed 14 poetry critics performing a close reading, revealing a set of cognitive practices supported through free-form annotation that have not previously been discussed in this context. We used guidelines derived from our study to design a tool, Metatation, which uses a pen-and-paper system with a peripheral display to utilize reader annotations as underspecified interactions to augment close reading. By turning paper-based annotations into implicit queries, Metatation provides relevant supplemental information in a just-in-time manner and acts as a bridge between close and distant reading.

Publications

DataTours: A Data Narratives Framework

Contributors:

Hrim Mehta, Amira Chalbi, Fanny Chevalier, and Christopher Collins

Visual storytelling is commonly employed to communicate data analyses results. Alternatively, (semi-)automated [1, 2, 6] data narratives or “tours” have been proposed as a means to prompt exploration of massive multidimensional datasets, substituting the more prevalent static overviews. While these works demonstrate specific instances of data tours, a concrete model to describe the building blocks of such tours is lacking. We present a descriptive hierarchical framework, DataTours, to formalize and guide the design of (semi-)automated tours for data exploration and discuss challenges evoked by the framework in the (semi-)automated authoring of such tours.

Publications

Abbreviating Text Labels on Demand

Contributors:

Mariana Shimabukuro and Christopher Collins

A known problem in information visualization labelling is when the text is too long to fit in the label space. There are some commonly known techniques used in order to solve this problem like setting a very small font size. On the other hand, sometimes the font size is so small that the text can be difficult to read. Wrapping sentences, dropping letters and text truncation can also be used. However, there is no research on how these techniques affect the legibility and readability of the visualization. In other words, we don’t know whether or not applying these techniques is the best way to tackle this issue. This thesis describes the design and implementation of a crowdsourced study that uses a recommendation system to narrow down abbreviations created by participants allowing us to efficiently collect and test the data in the same session. The study design also aims to investigate the effect of semantic context on the abbreviation that the participants create and the ability to decode them. Finally, based on the study data analysis we present a new technique to automatically make words as short as they need to be to maintain text legibility and readability.

Based on this project we implemented and made available online an API that allows other programmers to use our abbreviation algorithm in their web applications.

Check out our GitHub Repository for source code related to this project.

Download the crowd-sourced dataset.

For some demos applying our “Abbreviation on Demand” algorithm, and some visualizations of our study data access: http://vialab.science.uoit.ca/abbrVisualization/

Publications

ConToVi: Multi-Party Conversation Exploration using Topic-Space Views

Contributors:

Mennatallah El-Assady, Valentin Gold, Carmela Acevedo, Christopher Collins, and Daniel Keim

We introduce a novel visual analytics approach to analyze speaker behaviour patterns in multi-party conversations. We propose Topic-Space Views to track the movement of speakers across the thematic landscape of a conversation. Our tool is designed to assist political science scholars in exploring the dynamics of a conversation over time to generate and prove hypotheses about speaker interactions and behaviour patterns. Moreover, we introduce a glyph-based representation for each speaker turn based on linguistic and statistical cues to abstract relevant text features. We present animated views for exploring the general behaviour and interactions of speakers over time and interactive steady visualizations for the detailed analysis of a selection of speakers. Using a visual sedimentation metaphor we enable the analysts to track subtle changes in the flow of a conversation over time while keeping an overview of all past speaker turns. We evaluate our approach on real-world datasets and the results have been insightful to our domain experts.

For access to the tool, please take a look at the presentation slides or contact us via e-mail.

Presentation Slides (PDF)

Publications

Acknowledgements

PhysioEx: Visual Analysis of Physiological Event Streams

Contributors:

Rishikesan Kamaleswaran, Christopher Collins, Andrew James, and Carolyn McGregor

In this work, we introduce a novel visualization technique, the Temporal Intensity Map, which visually integrates data values over time to reveal the frequency, duration, and timing of significant features in streaming data. We combine the Temporal Intensity Map with several coordinated visualizations of detected events in data streams to create PhysioEx, a visual dashboard for multiple heterogeneous data streams. We have applied PhysioEx in a design study in the field of neonatal medicine, to support clinical researchers exploring physiologic data streams. We evaluated our method through consultations with domain experts. Results show that our tool provides deep insight capabilities, supports hypothesis generation, and can be well integrated into the workflow of clinical researchers.

EuroVis 2016 Presentation Slides

Publications

Acknowledgements

Using Visual Analytics of Heart Rate Variation to Aid in Diagnostics

Contributors:

Stephen McIntyre, J. Mikael Eklund, and Christopher Collins

We present an interactive visualization tool for exploring RR interval data (the time between consecutive heartbeats) to support diagnostics. An RR interval sequence diagram allows us to reduce hours of data into a general overview as opposed to using short-term ECG strips. A simple moving average is applied to the sequence diagram to smooth out short-term variance and highlight long-term trends. The moving average is surrounded by standard deviation bands which allow us to see the fluctuations invariance. After a brief training period using these tools coupled with RR interval and RR interval difference histograms, non-expert participants (undergraduate students) were able to differentiate between normal, atrial fibrillation, and congestive heart failure.

Publications

Acknowledgements

Off-Screen Desktop

Contributors:

Erik Paluka and Christopher Collins

We present Off-Screen Desktop, spatial navigation techniques that make use of the space around the display to extend direct manipulation beyond the desktop screen. To enable off-screen direct manipulation, these techniques visually transform the information space without affecting its interaction space. This allows a person to interact with the information space as if it physically extended beyond the boundaries of the display. Off-Screen Desktop is characterized by its implicit transience where the applied visual transformations are automatically reverted when the hand leaves the associated spatial interaction space. We illustrate Off-Screen Desktop with the design of three different techniques, which include Dynamic Distortion, Spatial Panning, and Dynamic Peephole Inset, as well as their evaluation in a comparative study with standard mouse panning. We also demonstrate their applicability with a number of use cases. Study results show that Spatial Panning was overall significantly faster than the other Off-Screen Desktop techniques when employed in two different navigation tasks.

Check out our GitHub Repository for source code related to this project.

Publications

Acknowledgements

PivotSlice

Contributors:

Jian Zhao, Christopher Collins, Fanny Chevalier, and Ravin Balakrishnan

Many datasets, such as scientific literature collections, contain multiple heterogeneous facets which derive implicit relations, as well as explicit relational references between data items. The exploration of this data is challenging not only because of large data scales but also the complexity of resource structures and semantics. In this paper, we present PivotSlice, an interactive visualization technique that provides efficient faceted browsing as well as flexible capabilities to discover data relationships. With the metaphor of direct manipulation, PivotSlice allows the user to visually and logically construct a series of dynamic queries over the data, based on a multi-focus and multi-scale tabular view that subdivides the entire dataset into several meaningful parts with customized semantics. PivotSlice further facilitates the visual exploration and sensemaking process through features including live search and integration of online data, graphical interaction histories and smoothly animated visual state transitions. We evaluated PivotSlice through a qualitative lab study with university researchers and report the findings from our observations and interviews. We also demonstrate the effectiveness of PivotSlice using a scenario of exploring a repository of information visualization literature.

Check out our Github Repository for source code related to this project.

Media

Presentation Slides

Publications

Acknowledgements

#FluxFlow

Contributors:

Jian Zhao, Nan Cao, Zhen Wen, Yale Song, Yu-Ru Lin, and Christopher Collins

We present FluxFlow, an interactive visual analysis system for revealing and analyzing anomalous information spreading in social media. Every day, millions of messages are created, commented on, and shared by people on social media websites, such as Twitter and Facebook. This provides valuable data for researchers and practitioners in many application domains, such as marketing, to inform decision-making. Distilling valuable social signals from the huge crowd’s messages, however, is challenging, due to the heterogeneous and dynamic crowd behaviours. The challenge is rooted in data analysts’ capability of discerning the anomalous information behaviours, such as the spreading of rumours or misinformation, from the rest that are more conventional patterns, such as popular topics and newsworthy events, in a timely fashion. FluxFlow incorporates advanced machine learning algorithms to detect anomalies and offers a set of novel visualization designs for presenting the detected threads for deeper analysis. We evaluated FluxFlow with real datasets containing the Twitter feeds captured during significant events such as Hurricane Sandy. Through quantitative measurements of the algorithmic performance and qualitative interviews with domain experts, the results show that the back-end anomaly detection model is effective in identifying anomalous retweeting threads, and its front-end interactive visualizations are intuitive and useful for analysts to discover insights in data and comprehend the underlying analytical model.