Scott Barlowe

Click2Annotate: Automated Insight Externalization with rich semantics

Insight Externalization (IE) refers to the process of capturing and recording the semantics of insights in decision making and problem solving. To reduce human effort, Automated Insight Externalization (AIE) is desired. Most existing IE approaches achieve automation by capturing events (e.g., clicks and key presses) or actions (e.g., panning and zooming). In this paper, we propose a novel AIE approach named Click2Annotate. It allows semi-automatic insight annotation that captures low-level analytics task results (e.g., clusters and outliers), which have higher semantic richness and abstraction levels than actions and events. Click2Annotate has two significant benefits. First, it reduces human effort required in IE and generates annotations easy to understand. Second, the rich semantic information encoded in the annotations enables various insight management activities, such as insight browsing and insight retrieval. We present a formal user study that proved this first benefit. We also illustrate the second benefit by presenting the novel insight management activities we developed based on Click2Annotate, namely scented insight browsing and faceted insight search.

VAET: A Visual Analytics Approach for E-Transactions Time-Series

Previous studies on E-transaction time-series have mainly focused on finding temporal trends of transaction behavior. Interesting transactions that are time-stamped and situation-relevant may easily be obscured in a large amount of information. This paper proposes a visual analytics system, Visual Analysis of E-transaction Time-Series (VAET), that allows the analysts to interactively explore large transaction datasets for insights about time-varying transactions. With a set of analyst-determined training samples, VAET automatically estimates the saliency of each transaction in a large time-series using a probabilistic decision tree learner. It provides an effective time-of-saliency (TOS) map where the analysts can explore a large number of transactions at different time granularities. Interesting transactions are further encoded with KnotLines, a compact visual representation that captures both the temporal variations and the contextual connection of transactions. The analysts can thus explore, select, and investigate knotlines of interest. A case study and user study with a real E-transactions dataset (26 million records) demonstrate the effectiveness of VAET.

Multivariate visual explanation for high dimensional datasets

Understanding multivariate relationships is an important task in multivariate data analysis. Unfortunately, existing multivariate visualization systems lose effectiveness when analyzing relationships among variables that span more than a few dimensions. We present a novel multivariate visual explanation approach that helps users interactively discover multivariate relationships among a large number of dimensions by integrating automatic numerical differentiation techniques and multidimensional visualization techniques. The result is an efficient workflow for multivariate analysis model construction, interactive dimension reduction, and multivariate knowledge discovery leveraging both automatic multivariate analysis and interactive multivariate data visual exploration. Case studies and a formal user study with a real dataset illustrate the effectiveness of this approach.

PIWI: Visually Exploring Graphs Based on Their Community Structure

Community structure is an important characteristic of many real networks, which shows high concentrations of edges within special groups of vertices and low concentrations between these groups. Community related graph analysis, such as discovering relationships among communities, identifying attribute-structure relationships, and selecting a large number of vertices with desired structural features and attributes, are common tasks in knowledge discovery in such networks. The clutter and the lack of interactivity often hinder efforts to apply traditional graph visualization techniques in these tasks. In this paper, we propose PIWI, a novel graph visual analytics approach to these tasks. Instead of using Node-Link Diagrams (NLDs), PIWI provides coordinated, uncluttered visualizations, and novel interactions based on graph community structure. The novel features, applicability, and limitations of this new technique have been discussed in detail. A set of case studies and preliminary user studies have been conducted with real graphs containing thousands of vertices, which provide supportive evidence about the usefulness of PIWI in community related tasks.

Supporting effective common ground construction in Asynchronous Collaborative Visual Analytics

Asynchronous Collaborative Visual Analytics (ACVA) leverages group sensemaking by releasing the constraints on when, where, and who works collaboratively. A significant task to be addressed before ACVA can reach its full potential is effective common ground construction, namely the process in which users evaluate insights from individual work to develop a shared understanding of insights and collectively pool them. This is challenging due to the lack of instant communication and scale of collaboration in ACVA. We propose a novel visual analytics approach that automatically gathers, organizes, and summarizes insights to form common ground with reduced human effort. The rich set of visualization and interaction techniques provided in our approach allows users to effectively and flexibly control the common ground construction and review, explore, and compare insights in detail. A working proto-type of the approach has been implemented. We have conducted a case study and a user study to demonstrate its effectiveness.

Evaluating exploratory visualization systems: a user study on how clustering-based visualization systems support information seeking from large document collections

Iterative, opportunistic and evolving visual sense-making has been an important research topic as it assists users in overcoming ever-increasing information overload. Exploratory visualization systems (EVSs) maximize the amount of information users can gain through learning and have been widely used in scientific discovery and decision-making contexts. Although many EVSs have been developed recently, there is a lack of general guidance on how to evaluate such systems. Researchers face challenges such as understanding the cognitive learning process supported by these systems. In this paper, we present a formal user study on Newdle, a clustering-based EVS for large news collections, shedding light on a general methodology for EVS evaluation. Our approach is built upon cognitive load theory, which takes the user as well as the system as the focus of evaluation. The carefully designed procedures allow us to thoroughly examine the user’s cognitive process as well as control the variability among human subjects. Through this study, we analyse how and why clustering-based EVSs benefit (or hinder) users in a variety of information-seeking tasks. We also summarize leverage points for designing clustering-based EVSs.

WaveMap: interactively discovering features from protein flexibility matrices usingwavelet-based visual analytics

The knowledge gained from biology datasets can streamline and speed‐up pharmaceutical development. However, computational models generate so much information regarding protein behavior that large‐scale analysis by traditional methods is almost impossible. The volume of data produced makes the transition from data to knowledge difficult and hinders biomedical advances. In this work, we present a novel visual analytics approach named WaveMap for exploring data generated by a protein flexibility model. WaveMap integrates wavelet analysis, visualizations, and interactions to facilitate the browsing, feature identification, and comparison of protein attributes represented by two‐dimensional plots. We have implemented a fully working prototype of WaveMap and illustrate its usefulness through expert evaluation and an example scenario.

Touch2Annotate: generating better annotations with less human effort on multi-touch interfaces

Annotation is essential for effective visual sense making. For multidimensional data, most existing annotation approaches require users to manually type notes to record the semantic meaning of their findings. They require high effort from multi-touch interface users since these users often experience low typing speeds and high typing errors. To lower the typing effort and improve the quality of the generated annotations, we propose a new approach that semi-automatically generates annotations with rich semantic meanings on multidimensional visualizations. A working prototype of this approach, named Touch2Annotate, has been implemented and used on a tabletop. We present a scenario of using Touch2Annotate to demonstrate its effectiveness.

How software works: Computational thinking and ethics before CS1

It is widely acknowledged that many freshmen go to university without any prior grounding in computer science. Recent studies conducted in the US have shown that not only do high school students lack any exposure, but also they possess ill-conceived notions of what computer science is, a problem also affecting their parents, teachers and regional school superintendents. For incoming students, the extent to which computing technology impacts their daily lives is very likely at odds with the extent to which they have considered how software works and its ethical implications. The ability to critically analyze and consider related ethical consequences of computing is an important life skill for every twenty-first century adult and not just computer scientists. With this this in mind, this paper presents a unique CS0 introduction to programming and computer ethics for pre CS and non majors. It has been defined as a service course to promote digital literacy and to ensure that students appreciate what computer science is and its socio-ethical implications. The paper outlines the course and its content in detail, as well as providing quantitative and qualitative evidence of its benefit and appeal to female students.

O-Charts: Towards an effective toolkit for teaching time complexity

Scientists, business analysts, and others in a growing number of fields are trying to cope with the vast amount of data being generated. The lack of software that can efficiently process large data sets hinders insight into complex relationships. One of the most important concepts in learning how to construct efficient code is time complexity analysis with Big-O notation. Students often find time complexity difficult to learn and too abstract to apply in any meaningful way. Common instructional methods consist of a combination of mathematics and intuitive analysis which are often too cumbersome for practical application or cannot be extended to complex algorithms. Unfortunately, there are few tools available for teaching time complexity that students find concrete, straightforward, and applicable to real problems. In this paper, we present O-Charts, a first step in the development of a practical toolkit for teaching and applying time complexity analysis. O-Charts allow the systematic analysis of deeply nested loops where the use of control variables makes the number of executions difficult to define, calculate, and explain. We report initial results and our plans for future work.