Selecting, Quantifying, Optimizing, and Understanding Visualization Techniques: A Computational Intelligence-Based Approach

Abstract

Information visualization is a prominent technique to visually explore and analyze large volumes of data effectively. Visualization must be aesthetically appealing and perceptually pleasing to the human cognition. This needs necessitates a framework to predict visualization technique based on two aspects: the underlying dataset and the task to be performed on it. Additionally, the resultant visualization must be optimal in the context of aesthetics and human perception. This dissertation contributes in three perspectives that subsume information visualization aspects: automatic technique selection of a visualization, quantifying and optimizing visualization layout, and visualizing software trace. The study provides computational intelligence (CI) model to predict a visualization technique based on the metadata of original dataset and relevant tasks. Similarly, visualization metrics are formulated to objectively measure the visualization quality. Based on these metrics, an evolutionary algorithm optimizes the visualization layout. Finally, the hierarchical visualization technique is used to study the usage of application programming interface (API) objects in the program trace. The trace is collected using the bytecode instrumentation. This dissertation has three parts. First part aims to predict an appropriate visualization technique for a specific dataset. A customize dataset is built using the knowledge that exists in the contemporary literature on various visualization techniques. The dataset comprise of four metadata attributes, relevant task, and the visualization techniques. The study develops an artificial neural network (ANN) to predict a visualization technique using five input and eight output neurons. Optimal neural network architecture is obtained by evaluating various structures with different network configuration. Several well-known performance metrics, i.e., confusion matrix, accuracy, precision, and sensitivity of the classification are used to compare various neural network architectures. Additionally, the best ANN Abstract model is compared with five other well-known classifiers: k-nearest neighbor (k-NN), naïve Bayes (NB), decision tree (DT), random forest (RF), and support vector machine (SVM). Second part provides design of an optimal visualization using visualization quality metrics. Initially, the study focuses on the design parameters which contribute towards the quality of a visualization technique. Visualization metrics are proposed to measure the aesthetic and perceptual characteristics of visualization. They include: effectiveness, expressiveness, readability, and interactivity. An evolutionary algorithm (EA)-based framework to optimize the layout of a visualization technique is also proposed. Treemap visualization technique is used for layout optimization using the EA. These results are evaluated using control experiments and benchmark tasks. The last part uses treemap-based visualization to analyze API objects used in the software, particularly to understand API’s objects during runtime of Java programs. The work consists of two aspects: the extraction of APIs information using bytecode instrumentation, and development of a visualization tool to analyse the traces using treemaps. Initially, a bytecode instrumentation tool is developed to probe and collect runtime information. The extracted information is logged into an extensible markup language (XML) file. The log file is synthesized using treemap. The instrumentation part is evaluated using twenty benchmark and ten real world applications. The results show that the instrumentation tool causes minimal runtime overheads.