What May Visualization Processes Optimize?

Abstract

Abstract-In this paper, we present an abstract model of visualization and inference processes and describe an information-theoretic measure for optimizing such processes. In order to obtain such an abstraction, we first examined six classes of workflows in data analysis and visualization, and identified four levels of typical visualization components, namely disseminative, observational, analytical and model-developmental visualization. We noticed a common phenomenon at different levels of visualization, that is, the transformation of data spaces (referred to as alphabets) usually corresponds to the reduction of maximal entropy along a workflow. Based on this observation, we establish an information-theoretic measure of cost-benefit ratio that may be used as a cost function for optimizing a data visualization process. To demonstrate the validity of this measure, we examined a number of successful visualization processes in the literature, and showed that the information-theoretic measure can mathematically explain the advantages of such processes over possible alternatives.

An Ontological Framework for Supporting the Design and Evaluation of Visual Analytics Systems

“…As parts of the development life‐cycle of a VA system, design, evaluation, and improvement are all means for enabling the VA system to provide optimal support to the users concerned and their workflows. The information‐theoretic metric for analyzing the cost‐benefit ratio of VA workflows [CG16] is naturally a candidate for providing the basis of such optimization efforts. The cost‐benefit metric was recently used to analyze a wide range of visualization tasks in virtual environments (e.g., virtual reality, mixed reality, and non‐immersive environments) [CGJM19], demonstrating its applicability to practical scenarios.…”

“…Three Information‐Theoretic Measures. Chen and Golan introduced an information‐theoretic metric for measuring the cost‐benefit ratio of a VA workflow or any of its component processes [CG16]. The metric consists of three fundamental measures that are abstract representations of a variety of qualitative and quantitative criteria used in practice, including operational requirements (e.g., accuracy, speed, errors, uncertainty, provenance, automation), analytical capability (e.g., filtering, clustering, classification, summarization), cognitive capabilities (e.g., memorization, learning, context‐awareness, confidence), and so on.…”

“…The metric consists of three fundamental measures that are abstract representations of a variety of qualitative and quantitative criteria used in practice, including operational requirements (e.g., accuracy, speed, errors, uncertainty, provenance, automation), analytical capability (e.g., filtering, clustering, classification, summarization), cognitive capabilities (e.g., memorization, learning, context‐awareness, confidence), and so on. The abstraction results in a metric with the desirable mathematical simplicity [CG16]. In this work, we use the qualitative form of the metric:…”

“… Alphabet Compression (AC) measures the amount of entropy reduction (or information loss) achieved by a process. As it was noticed in [CG16], most VA processes (e.g., statistical aggregation, sorting, clustering, visual mapping, and interaction), feature many‐to‐one mappings from input to output, hence losing information. Although information loss is commonly regarded harmful, it cannot be all bad if it is a general trend of VA workflows.…”

“…The former relates a practical instance to different machine‐and human‐centric processes in VA workflows, such as statistics, algorithms, visualization, and interaction [CTBA∗11]. The latter relates a practical instance to different information‐theoretic measures in the cost‐benefit metric for optimizing VA processes [CG16]. For abstract reasoning in design optimization, we consider two states (“high” and “low”) of the three measures.…”