A two-stage framework for designing visual analytics system in organizational environments

A perennially interesting research topic in the field of visual analytics is how to effectively develop systems that support organizational users' decision-making and reasoning processes. The problem is, however, most domain analytical practices generally vary from organization to organization. This leads to diverse designs of visual analytics systems in incorporating domain analytical processes, making it difficult to generalize the success from one domain to another. Exacerbating this problem is the dearth of general models of analytical workflows available to enable such timely and effective designs. To alleviate these problems, we present a two-stage framework for informing the design of a visual analytics system. This design framework builds upon and extends current practices pertaining to analytical workflow and focuses, in particular, on incorporating both general domain analysis processes as well as individual's analytical activities. We illustrate both stages and their design components through examples, and hope this framework will be useful for designing future visual analytics systems. We validate the soundness of our framework with two visual analytics systems, namely Entity Workspace  and PatViz .

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Table of Contents 
 
Chapter 1  
Chapter 2 Gathering Customer Data 
Chapter 3 Principles of Contextual Inquiry 
Chapter 4 Contextual Inquiry in Practice 
Chapter 5 A Language of Work 
Chapter 6 Work Models 
Chapter 7 The Interpretation Session 
Chapter 8 Consolidation 
Chapter 9 Creating One View of the Customer 
Chapter 10 Communicating to the Organization 
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Chapter 12 Using Data to Drive Design 
Chapter 13 Design from Data 
Chapter 14 System Design 
Chapter 15 The User Environment Design 
Chapter 16 Project Planning and Strategy 
Chapter 17 Prototyping as a Design Tool 
Chapter 18 From Structure to User Interface 
Chapter 19 Iterating with a Prototype 
Chapter 20 Putting It into Practice

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abstract: The design and evaluation of most current information visualization systems descend from an emphasis on a user's ability to "unpack" the representations of data of interest and operate on them independently. Too often, successful decision-making and analysis are more a matter of serendipity and user experience than of intentional design and specific support for such tasks; although humans have considerable abilities in analyzing relationships from data, the utility of visualizations remains relatively variable across users, data sets, and domains. In this paper, we discuss the notion of analytic gaps, which represent obstacles faced by visualizations in facilitating higher-level analytic tasks, such as decision-making and learning. We discuss support for bridging these gaps, propose a framework for the design and evaluation of information visualization systems, and demonstrate its use.

title: Knowledge Encapsulation Framework for Collaborative Social Modeling
abstract: This paper describes the Knowledge Encapsulation Framework (KEF), a suite of tools to enable knowledge inputs (relevant, domain-specific facts) to modeling and simulation projects, as well as other domains that require effective collaborative workspaces for knowledge-based task. This framework can be used to capture evidence (e.g., trusted material such as journal articles and government reports), discover new evidence (covering both trusted and social media), enable discussions surrounding domain-specific topics and provide automatically generated semantic annotations for improved corpus investigation. The current KEF implementation is presented within a wiki environment, providing a simple but powerful collaborative space for team members to review, annotate, discuss and align evidence with their modeling frameworks. The novelty in this approach lies in the combination of automatically tagged and user-vetted resources, which increases user trust in the environment, leading to ease of adoption for the collaborative environment.

title: Connecting the dots with related notes
abstract: During visual analysis, users must often connect insights discovered at various points of time to understand implicit relations within their analysis. This process is often called "connecting the dots." In this paper, we describe an algorithm to recommend related notes from a user's past analysis based on his/her current line of inquiry during an interactive visual exploration process. We have implemented the related notes algorithm in HARVEST, a web based visual analytic system.

title: Iterative integration of visual insights during patent search and analysis
abstract: Patents are an important economic factor in todays globalized markets. Therefore, the analysis of patent information has become an inevitable task for a variety of interest groups. The retrieval of relevant patent information is an integral part of almost every patent analysis scenario. Unfortunately, the complexity of patent material inhibits a straightforward retrieval of all relevant patent documents and leads to iterative, time-consuming approaches in practice. With ‘PatViz’, a new system for interactive analysis of patent information has been developed to leverage iterative query refinement. PatViz supports users in building complex queries visually and in exploring patent result sets interactively. Thereby, the visual query module introduces an abstraction layer that provides uniform access to different retrieval systems and relieves users of the burden to learn different complex query languages. By establishing an integrated environment it allows for interactive reintegration of insights gained from visual result set exploration into the visual query representation. We expect that the approach we have taken is also suitable to improve iterative query refinement in other Visual Analytics systems.

title: Entity-based collaboration tools for intelligence analysis
abstract: Software tools that make it easier for analysts to collaborate as a natural part of their work will lead to better analysis that is informed by more perspectives. We are interested to know if software tools can be designed that support collaboration even as they allow analysts to find documents and organize information (including evidence, schemas, and hypotheses). We have modified the Entity Workspace system, described previously, to test such designs. We have evaluated the resulting design in both a laboratory study and a study where it is situated with an analysis team. In both cases, effects on collaboration appear to be positive. Key aspects of the design include an evidence notebook optimized for organizing entities (rather than text characters), information structures that can be collapsed and expanded, visualization of evidence that emphasizes events and documents (rather than emphasizing the entity graph), and a notification system that finds entities of mutual interest to multiple analysts.

title: Model-driven Visual Analytics
abstract: We describe a visual analytics (VA) infrastructure, rooted on techniques in machine learning and logic-based deductive reasoning that will assist analysts to make sense of large, complex data sets by facilitating the generation and validation of models representing relationships in the data. We use logic programming (LP) as the underlying computing machinery to encode the relations as rules and facts and compute with them. A unique aspect of our approach is that the LP rules are automatically learned, using Inductive Logic Programming, from examples of data that the analyst deems interesting when viewing the data in the high-dimensional visualization interface. Using this system, analysts will be able to construct models of arbitrary relationships in the data, explore the data for scenarios that fit the model, refine the model if necessary, and query the model to automatically analyze incoming (future) data exhibiting the encoded relationships. In other words it will support both model-driven data exploration, as well as data-driven model evolution. More importantly, by basing the construction of models on techniques from machine learning and logic-based deduction, the VA process will be both flexible in terms of modeling arbitrary, user-driven relationships in the data as well as readily scale across different data domains.

title: Defining and applying knowledge conversion processes to a visual analytics system
abstract: Knowledge-assisted visualization has been a fast growing field because it directly integrates and utilizes domain knowledge to produce effective data visualization. However, most existing knowledge-assisted visualization applications focus on integrating domain knowledge that is tailored only for specific analytical tasks. This reflects not only the different understandings of what ''knowledge'' is in visualization, but also the difficulties in generalizing and reapplying knowledge to new problems or domains. In this paper, we differentiate knowledge into two types, tacit and explicit, and suggest four conversion processes between them (internalization, externalization, collaboration, and combination) that could be included in knowledge-assisted visualizations. We demonstrate the applications of these four processes in a bridge visual analytical system for the US Department of Transportation and discuss their roles and utilities in real-life scenarios.