This section consists of papers on uncertainty representation and visualization with the help of user studies. The papers were published in SIG CHI conference.
Sample-Oriented Task-Driven Visualizations: Allowing Users to Make Better, More Confident Decisions
This paper presents guidelines for creating visual annotations for solving tasks with uncertainty, and an implementation that addresses five core tasks on a bar chart. A preliminary user study shows promising results: that users have a justified confidence in their answers with our system.
Uncertainty in data is paralleled by uncertainty in reasoning processes, and while uncertainty in data is starting to get some of the visualization research attention it deserves, the uncertainty in the reasoning process is thus far often overlooked. This article gathers and consolidates the issues involved in uncertainty relating to reasoning and analyzes how uncertainty visualizations can support cognitive and meta-cognitive processes.
Queries over large scale (petabyte) data bases often mean waiting overnight for a result to come back. Scale costs time. Such time also means that potential avenues of exploration are ignored because the costs are perceived to be too high to run or even propose them. With sampleAction this paper explores whether interaction techniques to present query results running over only incremental samples can be presented as sufficiently trustworthy for analysts both to make closer to real time decisions about their queries and to be more exploratory in their questions of the data.
Enterprise Data Analysis and Visualization: An Interview Study
Organizations rely on data analysts to model customer engagement, streamline operations, improve production, inform
business decisions, and combat fraud. Though numerous analysis and visualization tools have been built to improve the scale and
efficiency at which analysts can work, there has been little research on how analysis takes place within the social and organizational context of companies. This paper aims to better understand the enterprise analysts’ ecosystem.
Evaluating Sketchiness as a Visual Variable for the Depiction of Qualitative Uncertainty
This paper reports on results of a series of user studies on the perception of four visual variables that are commonly used in the
literature to depict uncertainty. The first formal evaluation of the use of these variables to facilitate an easier reading of uncertainty in visualizations that rely on line graphical primitives is defined along with the advantages and limitations of each technique.
A User Study to Compare Four Uncertainty Visualization Methods for 1D and 2D Datasets
Many techniques have been proposed to show uncertainty in data visualizations. However, very little is known about
their effectiveness in conveying meaningful information. This paper presents a user study that evaluates the perception of
uncertainty amongst four of the most commonly used techniques for visualizing uncertainty in one-dimensional and two dimensional
Evaluating the Effects of Displaying Uncertainty in Context-Aware Applications
Many context aware systems assume that the context information they use is highly accurate. In reality, however, perfect and
reliable context information is hard if not impossible to obtain. Several researchers have therefore argued that proper feedback such as monitor and control mechanisms have to be employed in order to make context aware systems applicable and usable in scenarios of realistic complexity. As of today, those feedback mechanisms are difficult to compare since they are too rarely evaluated. This paper proposes and evaluates a simple but effective feedback mechanism for context aware systems.
How Good is 85%? A Survey Tool to Connect Classifier Evaluation to Acceptability of Accuracy
Many HCI and ubiquitous computing systems are characterized by two important properties: their output is uncertain it has an associated accuracy that researchers attempt to optimize and this uncertainty is user facing it directly affects the quality of the user experience. Novel classifiers are typically evaluated using measures like the F1 score but given an F-score of (e.g.) 0.85, how do we know whether this performance is good enough? Is this level of uncertainty actually tolerable to users of the intended application and do people weight precision and recall equally? This paper develops a survey instrument that can systematically
answer such questions.
Large databases with uncertain information are becoming more common in many applications including data integration, location tracking, and Web search. In these applications, ranking records with uncertain attributes needs to handle new problems that are fundamentally different from conventional ranking. Specifically, uncertainty in records’ scores induces a partial order over records, as opposed to the total order that is assumed in the conventional ranking settings. This paper presents a new probabilistic model, based on partial orders, to encapsulate the space of possible rankings originating from score uncertainty.
Expressive Query Construction through Direct Manipulation of Nested Relational Results
Despite extensive research on visual query systems, the standard way to interact with relational databases remains to be through SQL queries and tailored form interfaces. This paper considers three requirements to be essential to a successful alternative: (1) query specification through direct manipulation of results, (2) the ability to view and modify any part of the current query without departing from the direct manipulation interface, and (3) SQL-like expressiveness. The first visual query system to meet all three requirements in a single design is presented.