A Review and Characterization of Progressive Visual Analytics

Progressive Visual Analytics (PVA) has gained increasing attention over the past years. It brings the user into the loop during otherwise long-running and non-transparent computations by producing intermediate partial results. These partial results can be shown to the user for early and continuous interaction with the emerging end result even while it is still being computed. Yet as clear-cut as this fundamental idea seems, the existing body of literature puts forth various interpretations and instantiations that have created a research domain of competing terms, various definitions, as well as long lists of practical requirements and design guidelines spread across different scientific communities. This makes it more and more difficult to get a succinct understanding of PVA’s principal concepts, let alone an overview of this increasingly diverging field. The review and discussion of PVA presented in this paper address these issues and provide (1) a literature collection on this topic, (2) a conceptual characterization of PVA, as well as (3) a consolidated set of practical recommendations for implementing and using PVA-based visual analytics solutions

Cognitive Activity Support Tools: Design of the Visual Interface

This dissertation is broadly concerned with interactive computational tools that support the performance of complex cognitive activities, examples of which are analytical reasoning, decision making, problem solving, sense making, forecasting, and learning. Examples of tools that support such activities are visualization-based tools in the areas of: education, information visualization, personal information management, statistics, and health informatics. Such tools enable access to information and data and, through interaction, enable a human-information discourse. In a more specific sense, this dissertation is concerned with the design of the visual interface of these tools. This dissertation presents a large and comprehensive theoretical framework to support research and design. Issues treated herein include interaction design and patterns of interaction for cognitive and epistemic support; analysis of the essential properties of interactive visual representations and their influences on cognitive and perceptual processes; an analysis of the structural components of interaction and how different operational forms of interaction components affect the performance of cognitive activities; an examination of how the information-processing load should be distributed between humans and tools during the performance of complex cognitive activities; and a categorization of common visualizations according to their structure and function, and a discussion of the cognitive utility of each category. This dissertation also includes a chapter that describes the design of a cognitive activity support tool, as guided by the theoretical contributions that comprise the rest of the dissertation. Those that may find this dissertation useful include researchers and practitioners in the areas of data and information visualization, visual analytics, medical and health informatics, data science, journalism, educational technology, and digital games

Interactive Visualizations: A Literature Review

The idea that pictures tell a more compelling story than words is a long-standing tradition. Research provides many examples, dating back to the maps used by Napoleon. Visualizations are now an integral part of information system design as they address limitations of human cognition. They are more than a picture and should be viewed as a tool that facilitates analytic activity through different modes of interaction. This paper presents a literature review of taxonomies of interactive visualizations defined by task type and interaction type

Interactive Data and Information Visualization: Unpacking its Characteristics and Influencing Aspects on Decision-making

Background: Interactive data and information visualization (IDIV) enhances information presentations by providing users with multiple visual representations, active controls, and analytics. Users have greater control over IDIV presentations than standard presentations and as such IDIV becomes a more popular and relevant means of supporting data analytics (DA), as well as augmenting human intellect. Thus, IDIV enables provision of information in a format better suited to users’ decision-making. Method: Synthesizing past literature, we unpack IDIV characteristics and their influence on decision-making. This study adopts a narrative review method. Our conceptualization of IDIV and the proposed decision-making model are derived from a substantial body of literature from within the information systems (IS) and psychology disciplines. Results: We propose an IS centered model of IDIV enhanced decision-making incorporating four bases of decision-making (i.e., predictors, moderators, mediators, and outcomes). IDIV is specifically characterized by rich features compared with standard information presentations, therefore, formulating the model is critical to understanding how IDIV affects decision processes, perceptual evaluations, and decision outcomes and quality. Conclusions: This decision-making model could provide a meaningful frame of reference for further IDIV research and greater specificity in IS theorizing. Overall, we contribute to the systematic description and explanation of IDIV and discuss a potential research agenda for future IDIV research into IS. Available at: https://aisel.aisnet.org/pajais/vol11/iss4/4

Guidance in Business Intelligence & Analytics Systems: A Review and Research Agenda

While the data amount grows exponentially, the number of people with analytical and technical skills is only slowly increasing. This skill gap is putting pressure on the labor market and increasing the need for personnel with these skills. At the same time, companies are forced to think of alternative ways to empower their less-skilled workforce to take on Business Intelligence and Analytics (BI&A) tasks. One promising attempt to address these challenges may turn to the concept of guidance. However, the current body of research on guidance in BI&A systems is scattered and lacks a structured investigation from which future research avenues can be derived. To address this gap, this article analyzes five categories, namely BI&A phases, guidance degree, guidance generation, user roles, and interactivity form. Reviewing 82 articles, our contribution is to synopsize articles on guidance in BI&A systems and to suggest five research avenues

Multi-View Ontology Explorer (MOE): Interactive Visual Exploration of Ontologies

An ontology is an explicit specification of a conceptualization. This specification consists of a common vocabulary and information structure of a domain. Ontologies have applications in many fields to semantically link information in a standardized manner. In these fields, it is often crucial for both expert and non-expert users to quickly grasp the contents of an ontology; and to achieve this, many ontology tools implement visualization components. There are many past works on ontology visualization, and most of these tools are adapted from tree and graph based visualization techniques (e.g. treemaps, node-link graphs, and 3D interfaces). However, due to the enormous size of ontologies, these existing tools have their own shortcomings when dealing information overload, usually resulting in clutter and occlusion on the screen. In this thesis, we propose a set of novel visualizations and interactions to visualize very large ontologies. We design 5 dynamically linked visualizations that focus on a different level of abstraction individually. These different levels of abstraction start from a high-level overview down to a low-level entity. In addition, these visualizations collectively visualize landmarks, routes, and survey knowledge to support the formation of mental models. Search and save features are implemented to support on-demand and guided exploration. Finally, we implement our design as a web application