Even though interaction is an important part of information visualization (Infovis), it has garnered a relatively low level of attention from the Infovis community. A few frameworks and taxonomies of Infovis interaction techniques exist, but they typically focus on low-level operations and do not address the variety of benefits interaction provides. After conducting an extensive review of Infovis systems and their interactive capabilities, we propose seven general categories of interaction techniques widely used in Infovis: 1) Select, 2) Explore, 3) Reconfigure, 4) Encode, 5) Abstract/Elaborate, 6) Filter, and 7) Connect. These categories are organized around a user's intent while interacting with a system rather than the low-level interaction techniques provided by a system. The categories can act as a framework to help discuss and evaluate interaction techniques and hopefully lay an initial foundation toward a deeper understanding and a science of interaction.

https://www.cc.gatech.edu/~john.stasko/papers/infovis07-interaction.pdf

Toward a Deeper Understanding of the Role of Interaction in Information Visualization

Although information visualization (infovis) technologies have proven indispensable tools for making sense of complex data, wide-spread deployment has yet to take hold, as successful infovis applications are often difficult to author and require domain-specific customization. To address these issues, we have created prefuse, a software framework for creating dynamic visualizations of both structured and unstructured data. prefuse provides theoretically-motivated abstractions for the design of a wide range of visualization applications, enabling programmers to string together desired components quickly to create and customize working visualizations. To evaluate prefuse we have built both existing and novel visualizations testing the toolkit's flexibility and performance, and have run usability studies and usage surveys finding that programmers find the toolkit usable and effective.

/pdf/prefuse-a-toolkit-for-interactive-information-visualization-1k95wewczb.pdf

prefuse: a toolkit for interactive information visualization

Usability evaluation is an increasingly important part of the user interface design process. However, usability evaluation can be expensive in terms of time and human resources, and automation is therefore a promising way to augment existing approaches. This article presents an extensive survey of usability evaluation methods, organized according to a new taxonomy that emphasizes the role of automation. The survey analyzes existing techniques, identifies which aspects of usability evaluation automation are likely to be of use in future research, and suggests new ways to expand existing approaches to better support usability evaluation.

/pdf/the-state-of-the-art-in-automating-usability-evaluation-of-1c9cn51uch.pdf

The state of the art in automating usability evaluation of user interfaces

From the Publisher:
Well-designed graphical user interfaces (GUIs) for business systems can greatly increase user productivity, but designing them can be difficult and time consuming This book walks developers through the basics of good interface design, using real-world examples from systems that are proven successes
* Galitz is an internationally recognized consultant, author, and instructor with many years of experience with information systems and user interface design
* Written especially for developers who may be designing user interfaces for the first time, but also extremely useful for any developer involved in GUI or Web site design
* Revised to reflect the profound enhancements in interface design, specifically how Web page design has revolutionized interface design
* New information covers a variety of platforms, both traditional and Web-based

The Essential Guide to User Interface Design: An Introduction to GUI Design Principles and Techniques

We take a new, scenario-based look at evaluation in information visualization. Our seven scenarios, evaluating visual data analysis and reasoning, evaluating user performance, evaluating user experience, evaluating environments and work practices, evaluating communication through visualization, evaluating visualization algorithms, and evaluating collaborative data analysis were derived through an extensive literature review of over 800 visualization publications. These scenarios distinguish different study goals and types of research questions and are illustrated through example studies. Through this broad survey and the distillation of these scenarios, we make two contributions. One, we encapsulate the current practices in the information visualization research community and, two, we provide a different approach to reaching decisions about what might be the most effective evaluation of a given information visualization. Scenarios can be used to choose appropriate research questions and goals and the provided examples can be consulted for guidance on how to design one's own study.

/pdf/empirical-studies-in-information-visualization-seven-3li5yz6tt6.pdf

Empirical Studies in Information Visualization: Seven Scenarios

Effective management of documents on computers has been a central user interface problem for many years. One common approach involves using 2D spatial layouts of icons representing the documents, particularly for information workspace tasks. This approach takes advantage of human 2D spatial cognition. More recently, several 3D spatial layouts have engaged 3D spatial cognition capabilities. Some have attempted to use spatial memory in 3D virtual environments. However, there has been no proof to date that spatial memory works the same way in 3D virtual environments as it does in the real world. We describe a new technique for document management called the Data Mountain, which allows users to place documents at arbitrary positions on an inclined plane in a 3D desktop virtual environment using a simple 2D interaction technique. We discuss how the design evolved in response to user feedback. We also describe a user study that shows that the Data Mountain does take advantage of spatial memory. Our study shows that the Data Mountain has statistically reliable advantages over the Microsoft Internet Explorer Favorites mechanism for managing documents of interest in an information workspace.

/pdf/data-mountain-using-spatial-memory-for-document-management-34rsai1awg.pdf

Data mountain: using spatial memory for document management

Much is known about depth and breadth tradeoff issues in graphical user interface menu design. We describe an experiment to see if large breadth and decreased depth is preferable, both subjectively and via performance data, while attempting to design for optimal scent throughout different structures of a website. A study is reported which modified previous procedures for investigating depth/breadth tradeoffs in content design for the web. Results showed that, while increased depth did harm search performance on the web, a medium condition of depth and breadth outperformed the broadest shallow web structure overall.

https://www.microsoft.com/en-us/research/wp-content/uploads/2016/12/chi98_webdesign.pdf

Web page design: implications of memory, structure and scent for information retrieval

HIPs, or Human Interactive Proofs, are challenges meant to be easily solved by humans, while remaining too hard to be economically solved by computers. HIPs are increasingly used to protect services against automatic script attacks. To be effective, a HIP must be difficult enough to discourage script attacks by raising the computation and/or development cost of breaking the HIP to an unprofitable level. At the same time, the HIP must be easy enough to solve in order to not discourage humans from using the service. Early HIP designs have successfully met these criteria [1]. However, the growing sophistication of attackers and correspondingly increasing profit incentives have rendered most of the currently deployed HIPs vulnerable to attack [2,7,12]. Yet, most companies have been reluctant to increase the difficulty of their HIPs for fear of making them too complex or unappealing to humans. The purpose of this study is to find the visual distortions that are most effective at foiling computer attacks without hindering humans. The contribution of this research is that we discovered that 1) automatically generating HIPs by varying particular distortion parameters renders HIPs that are too easy for computer hackers to break, yet humans still have difficulty recognizing them, and 2) it is possible to build segmentation-based HIPs that are extremely difficult and expensive for computers to solve, while remaining relatively easy for humans.

Designing human friendly human interaction proofs (HIPs)

Human interaction proofs (HIPs) have become commonplace on the internet for protecting free online services from abuse by automated scripts/bots. They are challenges designed to be easily solved by humans, while remaining too hard for computers to solve. Reading based HIPs comprise a segmentation problem and one or more recognition problems. Recent studies have shown that computers are better at solving the recognition problem than the segmentation problem (Chellapilla and Simard, 2004; Chellapilla et al, 2005a). In this paper we compare human and computer single character recognition abilities through a sequence of human user studies and computer experiments using convolutional neural networks. In these experiments, we assume that segmentation has been solved and the approximate locations of individual HIP characters are known. Results show that computers are as good as or better than humans at single character recognition under all commonly used distortion and clutter scenarios used in todays HIPs.

/pdf/computers-beat-humans-at-single-character-recognition-in-387tx6ba05.pdf

Computers beat Humans at Single Character Recognition in Reading based Human Interaction Proofs (HIPs)

Human interaction proofs (HIPs) have become common place on the internet due to their effectiveness in deterring automated abuse of online services intended for humans. However, there is a co-evolutionary arms race in progress and these proofs are becoming more difficult for genuine users while attackers are getting better at breaking existing HIPs. We studied various popular HIPs on the internet to understand their strength and human friendliness. To determine HIP strength, we adopted a direct approach of building computer attacks using image processing and machine learning techniques. To understand human-friendliness, a sequence of users studies were conducted to investigate HIP character recognition by humans under a variety of visual distortions and clutter commonly employed in reading-based HIPs. We found that many of the online HIPs are pure recognition tasks that can be easily broken using machine learning. The stronger HIPs tend to pose a combination of segmentation and recognition challenges. Further, the HIP user studies show that given correct segmentation, computers are much better at HIP character recognition than humans. In light of these results, we propose that segmentation-based reading challenges are the future for building stronger human-friendly HIPs. An example of such a segmentation-based HIP is presented with a preliminary assessment of its strength and human-friendliness.

Kevin Larson

Papers

Data mountain: using spatial memory for document management

Web page design: implications of memory, structure and scent for information retrieval

Designing human friendly human interaction proofs (HIPs)

Computers beat Humans at Single Character Recognition in Reading based Human Interaction Proofs (HIPs)

Building segmentation based human-friendly human interaction proofs (HIPs)