SPARKLINE HISTOGRAMS FOR COMPARING EVOLUTIONARY OPTIMIZATION METHODS

Abstract: Comparing evolutionary optimization methods is a difficult task. As more and more of articles are published in this field, the readers and reviewers are swamped with information that is hard to decipher. We propose the use of sparkline histograms that allow compact representation of test data in a way which is extremely fast to read and more informative than usually given metrics

Visualizing Set Relations and Cardinalities Using Venn and Euler Diagrams

In medicine, genetics, criminology and various other areas, Venn and Euler diagrams are used to visualize data set relations and their cardinalities. The data sets are represented by closed curves and the data set relationships are depicted by the overlaps between these curves. Both the sets and their intersections are easily visible as the closed curves are preattentively processed and form common regions that have a strong perceptual grouping effect. Besides set relations such as intersection, containment and disjointness, the cardinality of the sets and their intersections can also be depicted in the same diagram (referred to as area-proportional) through the size of the curves and their overlaps. Size is a preattentive feature and so similarities, differences and trends are easily identified. Thus, such diagrams facilitate data analysis and reasoning about the sets. However, drawing these diagrams manually is difficult, often impossible, and current automatic drawing methods do not always produce appropriate diagrams. This dissertation presents novel automatic drawing methods for different types of Euler diagrams and a user study of how such diagrams can help probabilistic judgement. The main drawing algorithms are: eulerForce, which uses a force-directed approach to lay out Euler diagrams; eulerAPE, which draws area-proportional Venn diagrams with ellipses. The user study evaluated the effectiveness of area- proportional Euler diagrams, glyph representations, Euler diagrams with glyphs and text+visualization formats for Bayesian reasoning, and a method eulerGlyphs was devised to automatically and accurately draw the assessed visualizations for any Bayesian problem. Additionally, analytic algorithms that instantaneously compute the overlapping areas of three general intersecting ellipses are provided, together with an evaluation of the effectiveness of ellipses in drawing accurate area-proportional Venn diagrams for 3-set data and the characteristics of the data that can be depicted accurately with ellipses

Visualizing multidimensional data similarities:Improvements and applications

Multidimensional data is increasingly more prominent and important in many application domains. Such data typically consist of a large set of elements, each of which described by several measurements (dimensions). During the design of techniques and tools to process this data, a key component is to gather insights into their structure and patterns, which can be described by the notion of similarity between elements. Among these techniques, multidimensional projections and similarity trees can effectively capture similarity patterns and handle a large number of data elements and dimensions. However, understanding and interpreting these patterns in terms of the original data dimensions is still hard. This thesis addresses the development of visual explanatory techniques for the easy interpretation of similarity patterns present in multidimensional projections and similarity trees, by several contributions. First, we propose methods that make the computation of similarity trees efficient for large datasets, and also enhance its visual representation to allow the exploration of more data in a limited screen. Secondly, we propose methods for the visual explanation of multidimensional projections in terms of groups of similar elements. These are automatically annotated to describe which dimensions are more important to define their notion of group similarity. We show next how these explanatory mechanisms can be adapted to handle both static and time-dependent data. Our proposed techniques are designed to be easy to use, work nearly automatically, and are demonstrated on a variety of real-world large data obtained from image collections, text archives, scientific measurements, and software engineering

A dynamic visual analytics framework for complex temporal environments

Introduction: Data streams are produced by sensors that sample an external system at a periodic interval. As the cost of developing sensors continues to fall, an increasing number of data stream acquisition systems have been deployed to take advantage of the volume and velocity of data streams. An overabundance of information in complex environments have been attributed to information overload, a state of exposure to overwhelming and excessive information. The use of visual analytics provides leverage over potential information overload challenges. Apart from automated online analysis, interactive visual tools provide significant leverage for human-driven trend analysis and pattern recognition. To facilitate analysis and knowledge discovery in the space of multidimensional big data, research is warranted for an online visual analytic framework that supports human-driven exploration and consumption of complex data streams. Method: A novel framework was developed called the temporal Tri-event parameter based Dynamic Visual Analytics (TDVA). The TDVA framework was instantiated in two case studies, namely, a case study involving a hypothesis generation scenario, and a second case study involving a cohort-based hypothesis testing scenario. Two evaluations were conducted for each case study involving expert participants. This framework is demonstrated in a neonatal intensive care unit case study. The hypothesis generation phase of the pipeline is conducted through a multidimensional and in-depth one subject study using PhysioEx, a novel visual analytic tool for physiologic data stream analysis. The cohort-based hypothesis testing component of the analytic pipeline is validated through CoRAD, a visual analytic tool for performing case-controlled studies. Results: The results of both evaluations show improved task performance, and subjective satisfaction with the use of PhysioEx and CoRAD. Results from the evaluation of PhysioEx reveals insight about current limitations for supporting single subject studies in complex environments, and areas for future research in that space. Results from CoRAD also support the need for additional research to explore complex multi-dimensional patterns across multiple observations. From an information systems approach, the efficacy and feasibility of the TDVA framework is demonstrated by the instantiation and evaluation of PhysioEx and CoRAD. Conclusion: This research, introduces the TDVA framework and provides results to validate the deployment of online dynamic visual analytics in complex environments. The TDVA framework was instantiated in two case studies derived from an environment where dynamic and complex data streams were available. The first instantiation enabled the end-user to rapidly extract information from complex data streams to conduct in-depth analysis. The second allowed the end-user to test emerging patterns across multiple observations. To both ends, this thesis provides knowledge that can be used to improve the visual analytic pipeline in dynamic and complex environments