5 research outputs found
The benefits of synchronous collaborative information visualization: evidence from an experimental evaluation
A great corpus of studies reports empirical evidence of how information visualization supports comprehension and analysis of data. The benefits of visualization for synchronous group knowledge work, however, have not been addressed extensively. Anecdotal evidence and use cases illustrate the benefits of synchronous collaborative information visualization, but very few empirical studies have rigorously examined the impact of visualization on group knowledge work. We have consequently designed and conducted an experiment in which we have analyzed the impact of visualization on knowledge sharing in situated work groups. Our experimental study consists of evaluating the performance of 131 subjects (all experienced managers) in groups of 5 (for a total of 26 groups), working together on a real-life knowledge sharing task. We compare (1) the control condition (no visualization provided), with two visualization supports: (2) optimal and (3) suboptimal visualization (based on a previous survey). The facilitator of each group was asked to populate the provided interactive visual template with insights from the group, and to organize the contributions according to the group consensus. We have evaluated the results through both objective and subjective measures. Our statistical analysis clearly shows that interactive visualization has a statistically significant, objective and positive impact on the outcomes of knowledge sharing, but that the subjects seem not to be aware of this. In particular, groups supported by visualization achieved higher productivity, higher quality of outcome and greater knowledge gains. No statistically significant results could be found between an optimal and a suboptimal visualization though (as classified by the pre-experiment survey). Subjects also did not seem to be aware of the benefits that the visualizations provided as no difference between the visualization and the control conditions was found for the self-reported measures of satisfaction and participation. An implication of our study for information visualization applications is to extend them by using real-time group annotation functionalities that aid in the group sense making process of the represented data
Supporting Web-based and Crowdsourced Evaluations of Data Visualizations
User studies play a vital role in data visualization research because they help measure the strengths and weaknesses of different visualization techniques quantitatively. In addition, they provide insight into what makes one technique more effective than another; and they are used to validate research contributions in the field of information visualization. For example, a new algorithm, visual encoding, or interaction technique is not considered a contribution unless it has been validated to be better than the state of the art and its competing alternatives or has been validated to be useful to intended users. However, conducting user studies is challenging, time consuming, and expensive.
User studies generally requires careful experimental designs, iterative refinement, recruitment of study participants, careful management of participants during the run of the studies, accurately collecting user responses, and expertise in statistical analysis of study results. There are several variables that are taken into consideration which can impact user study outcome if not carefully managed. Hence the process of conducting user studies successfully can take several weeks to months.
In this dissertation, we investigated how to design an online framework that can reduce the overhead involved in conducting controlled user studies involving web-based visualizations. Our main goal in this research was to lower the overhead of evaluating data visualizations quantitatively through user studies. To this end, we leveraged current research opportunities to provide a framework design that reduces the overhead involved in designing and running controlled user studies of data visualizations. Specifically, we explored the design and implementation of an open-source framework and an online service (VisUnit) that allows visualization designers to easily configure user studies for their web-based data visualizations, deploy user studies online, collect user responses, and analyze incoming results automatically. This allows evaluations to be done more easily, cheaply, and frequently to rapidly test hypotheses about visualization designs.
We evaluated the effectiveness of our framework (VisUnit) by showing that it can be used to replicate 84% of 101 controlled user studies published in IEEE Information Visualization conferences between 1995 and 2015. We evaluated the efficiency of VisUnit by showing that graduate students can use it to design sample user studies in less than an hour.
Our contributions are two-fold: first, we contribute a flexible design and implementation that facilitates the creation of a wide range of user studies with limited effort; second, we provide an evaluation of our design that shows that it can be used to replicate a wide range of user studies, can be used to reduce the time evaluators spend on user studies, and can be used to support new research
Visualizaci贸n de conocimiento en temas espec铆ficos mediante el uso del correo electr贸nico corporativo
Proyecto de Graduaci贸n (Maestr铆a en Computaci贸n con 茅nfasis en Sistemas de Informaci贸n) Instituto Tecnol贸gico de Costa Rica, Escuela de Ingenier铆a en Computaci贸n, 2014.Understanding who within a corporation has knowledge on a given topic, is key for effective decision making that enables the best use of the resources and strengthens collaboration. Based on studies of social network analysis, information visualization techniques are evaluated, the most adequate visualization is selected, and improvements developed over it
Recommended from our members
Recommender systems and market approaches for industrial data management
Industrial companies are dealing with an increasing data overload problem in all
aspects of their business: vast amounts of data are generated in and outside each
company. Determining which data is relevant and how to get it to the right users is
becoming increasingly difficult. There are a large number of datasets to be
considered, and an even higher number of combinations of datasets that each user
could be using.
Current techniques to address this data overload problem necessitate detailed
analysis. These techniques have limited scalability due to their manual effort and
their complexity, which makes them unpractical for a large number of datasets.
Search, the alternative used by many users, is limited by the user鈥檚 knowledge
about the available data and does not consider the relevance or costs of providing
these datasets.
Recommender systems and so-called market approaches have previously been
used to solve this type of resource allocation problem, as shown for example in
allocation of equipment for production processes in manufacturing or for spare part
supplier selection. They can therefore also be seen as a potential application for
the problem of data overload.
This thesis introduces the so-called RecorDa approach: an architecture using
market approaches and recommender systems on their own or by combining them
into one system. Its purpose is to identify which data is more relevant for a user鈥檚
decision and improve allocation of relevant data to users.
Using a combination of case studies and experiments, this thesis develops and
tests the approach. It further compares RecorDa to search and other mechanisms.
The results indicate that RecorDa can provide significant benefit to users with
easier and more flexible access to relevant datasets compared to other
techniques, such as search in these databases. It is able to provide a fast increase
in precision and recall of relevant datasets while still keeping high novelty and
coverage of a large variety of datasets
Cognitive Foundations for Visual Analytics
In this report, we provide an overview of scientific/technical literature on information visualization and VA. Topics discussed include an update and overview of the extensive literature search conducted for this study, the nature and purpose of the field, major research thrusts, and scientific foundations. We review methodologies for evaluating and measuring the impact of VA technologies as well as taxonomies that have been proposed for various purposes to support the VA community. A cognitive science perspective underlies each of these discussions