Various snippet visualizations have been suggested to assist users in assessing the relevance of their online search results, including highlighting query terms and incorporating target page thumbnails. This study introduced two enhancements: shades and multiple thumbnails. In the shades visualization, query terms in the snippet are background highlighted in different shades based on the number of adjacent query terms. The multiple thumbnail visualization presents reduced-size thumbnails of the target landing page. A 3 × 3 experimental design examined the effects of thumbnails (no thumbnail, single thumbnail, multiple thumbnails) and highlighting (no highlighting, yellow background highlighting, shades), and their combined use on 37 participants’ effectiveness, efficiency, and satisfaction when performing complex search tasks. The results showed that although the shades and multiple thumbnail visualizations had no significant effect on users’ effectiveness compared to the baseline textual snippet visualization, they both significantly (p < 0.0001) improved users’ efficiency and satisfaction, and their combined effect was synergetic. The findings have operative implications for web search interface design.

Search engine results pages (SERPs) commonly organize online search results in a list-based format. Various viewing formats have been suggested to improve search effectiveness, efficiency, and user satisfaction. However, except for the Knowledge Graph view, which is based on semantic search and appears as information boxes on the SERP, those views are rarely implemented in popular Web search engines. This study investigates the effect of three viewing formats (list, Google Knowledge Graph, and clustered list) on user performance and satisfaction when performing five common types of search tasks mapped in previous studies (navigate, find-simple, find-complex, locate/acquire, and explore/learn). A Chrome extension we developed is used to enable clustering on Google search results. While overall effectiveness was similar across viewing formats, efficiency and satisfaction varied significantly depending on the search task. The operative implication is that search engine viewing formats should adapt to the specific search task.

Given the widespread use of word processing systems today, error correction and text editing tasks are frequently performed.A novel method for error correction and text editing using voi-ce&mouse was developed and evaluated.A within-participants counterbalanced study design was used to compare users' performance and satisfaction with the proposed method to their performance and satisfaction with two other methods that enable natural interaction with Microsoft Word, voice-only and voice&gaze, and with conventional interaction using the keyboard&mouse method, when performing error correction and text editing tasks.Our comprehensive evaluation showed that for all of the examined tasks the proposed voice&mouse method performed comparably to the voice-only and voice&gaze methods or significantly outperformed them on all evaluation measures.The keyboard&mouse method yielded significantly higher average SUS scores and lower NASA-TLX task loads than the other methods.However, participants significantly preferred using the voice&mouse method, in general, and especially for tasks that typically require typing in addition to using a mouse, such as fix, replace, and add, and the performance times for the two methods were comparable when performing these tasks.The findings support the use of the voi-ce&mouse method for error correction and text editing along with the keyboard&mouse method.This would allow users to choose their preferred interaction method for each scenario and task while improving their performance and satisfaction.

In this study, we introduce and evaluate a novel extractive text summarization methodology, “SummarEyes,” based on the visual interaction of the user with the text, using eye-tracking data, as opposed to the traditional approaches based on analysis of textual content only. We conducted a large-scale user study aiming to collect eye-tracking data while reading the text to be summarized. We utilized various user’s implicit attention metrics to generate novel eye-tracking-based text summarization models and compared them both to eye-tracking models typically using only a single feature of the gaze duration and to traditional, as well as state-of-the-art summarization methods, based solely on textual features. The models’ quality was evaluated in terms of ROUGE scores using intrinsic evaluation on the datasets we had generated, relating gaze behavior to personalized and DUC gold-standard summaries. The experimental results showed that “SummarEyes” significantly outperformed the other summarizers in predicting both the user’s personalized summarization and the generic gold standard summaries. With the increasing availability of eye-tracking technology, this research can lead to a new generation of effective user-centric text summarization tools.

Empirical evaluations of visual encodings for analytical tasks inform the design of automatic presentation systems. This study compares anomaly detection effectiveness, efficiency, and user satisfaction using tabular visualization and graphical representation of position, size, and color saturation visualizations and the visualizations’ relative ranking. In our user study analysts used the visualizations to detect anomalies in bivariate quantitative, ordinal, and nominal real data, before and after training. Consistent with Mackinlay ranking of the visual encodings’ effectiveness, the results showed that for all data types, position visualization outperformed size and color saturation visualizations on all measures, before and after training. The use of position visualization after training was as effective as using the table visualization for all data types but significantly easier to use and preferable. Furthermore, the average anomaly detection time was at least three times shorter when using position visualization compared to table visualization for ordinal and quantitative data.

Wrong-patient errors from inadvertent menu selections while using computerized provider order entry (CPOE) systems could have fatal consequences. This study investigated whether the manipulation of CPOE interface design could improve healthcare providers’ ability to recognize patient selection errors and also decrease the time to error recognition. Using a 2 × 2 design, 120 participants were randomly assigned to one of four groups, interacting with different versions of a simulated CPOE: (1) control – standard version; (2) highlighted selection – the selected patient row was highlighted for 2 s, by blanking the rest of the screen; (3) photo – photographs of patients’ faces were displayed in all screens; (4) combined – with photo and highlighted selection. Each participant navigated through five order scenarios. On the last scenario, an error was simulated by directing the participant to a wrong patient. Recognition rates of the wrong-patient error and times to error recognition were significantly improved for the highlighted selection, photo, and combined groups, relative to the control group. These results suggest that the addition of patient photos and highlighted selection could substantially reduce errors in CPOE systems and other applications.

Visual overviews of tables of numerical and categorical data have been proposed for tables with a single value per cell. This article addresses the problem of exploring tables with columns that consist of cells that are distributions, for example, the distributions of movie ratings or trust ratings in recommender systems, age distributions in demographic data, usage distributions in logs of telephone calls, and so on. This article expands on heatmap approaches and proposes a novel way of displaying and interacting with distribution data. The usability study demonstrates the benefits of the heatmap interface in providing an overview of the data and facilitating the discovery of interesting clusters, patterns, outliers and relationships between columns.

Specifying event sequence queries is challenging even for skilled computer professionals familiar with SQL. Most graphical user interfaces for database search use an exact match approach, which is often effective, but near misses may also be of interest. We describe a new similarity search interface, in which users specify a query by simply placing events on a blank timeline and retrieve a similarity-ranked list of results. Behind this user interface is a new similarity measure for event sequences which the users can customize by four decision criteria, enabling them to adjust the impact of missing, extra, or swapped events or the impact of time shifts. We describe a use case with Electronic Health Records based on our ongoing collaboration with hospital physicians. A controlled experiment with 18 participants compared exact match and similarity search interfaces. We report on the advantages and disadvantages of each interface and suggest a hybrid interface combining the best of both.

Event sequence analysis is an important task in many domains: medical researchers may study the patterns of transfers within the hospital for quality control; transportation experts may study accident response logs to identify best practices. In many cases they deal with thousands of records. While previous research has focused on searching and browsing, overview tasks are often overlooked. We introduce a novel interactive visual overview of event sequences called \emph{LifeFlow}. LifeFlow is scalable, can summarize all possible sequences, and represents the temporal spacing of the events within sequences. Two case studies with healthcare and transportation domain experts are presented to illustrate the usefulness of LifeFlow. A user study with ten participants confirmed that after 15 minutes of training novice users were able to rapidly answer questions about the prevalence and temporal characteristics of sequences, find anomalies, and gain significant insight from the data.

A new ontological-content-based method for ranking the relevancy of items in the electronic newspapers domain is proposed. The method is being implemented in ePaper, a personalized electronic newspaper research project. The content-based part of the filtering method of ePaper utilizes a hierarchical ontology of news items. The method considers common and "close" ontology concepts appearing in the user's profile and in the item's profile, measuring the hierarchical distance between concepts in the two profiles. Based on the number of common and related concepts, and their distances from each other, the filtering algorithm computes the similarity between items and users, and rank-orders the news items according to their relevancy to each user, thus providing a personalized newspaper.

Many studies have compared the properties of agent-oriented methodologies, usually applying the qualitative approach. In contrast, the quantitative approach for comparing these methodologies has rarely been used. Thus, it is difficult to assess the extent of suitability of these methodologies to, and the understanding and usage of the methodologies by, their intended users. In this paper, we adopted a quantitative approach for the comparison of two agent-oriented modeling methods, the Multiagent System Engineering (MaSE) and the Object-Process Methodology for Multi-Agent Systems (OPM/MAS). In particular, we examined the modeling language of each method as reflected in the design results. The results of the examination highlighted the differences between the two methods. Specifically, MaSE was found to be more adequate for understanding interactions and in specifying a new behavior. On the other hand, OPM/MAS was found helpful in understanding the entire system and in specifying interactions with the environment. These results clearly indicate the improvements required for each of the methods compared.

The paper describes a user study examining methods for improving users queries, specifically interactive and automatic query expansion and advanced search options. The user study includes subjective and objective evaluation of the effect of the above methods and a comparison between the real and perceived effect.