Supporting the sensemaking process in visual analytics

Visual analytics is the science of analytical reasoning facilitated by interactive visual interfaces. It involves interactive exploration of data using visualizations and automated data analysis to gain insight, and to ultimately make better decisions. It aims to support the sensemaking process in which information is collected, organized and analyzed to form new knowledge and inform further action. Interactive visual exploration of the data can lead to many discoveries in terms of relations, patterns, outliers and so on. It is difficult for the human working memory to keep track of all findings during a visual analysis. Also, synthesis of many different findings and relations between those findings increase the information overload and thereby hinders the sensemaking process further. The central theme of this dissertation is How to support users in their sensemaking process during interactive exploration of data? To support the sensemaking process in visual analytics, we mainly focus on how to support users to capture, reuse, review, share, and present the key aspects of interest concerning the analysis process and the findings during interactive exploration of data. For this, we have developed generic models and tools that enable users to capture findings with provenance, and construct arguments; and to review, revise and share their visual analysis. First, we present a sensemaking framework for visual analytics that contains three linked views: a data view, a navigation view and a knowledge view for supporting the sense-making process. The data view offers interactive data visualization tools. The navigation view automatically captures the interaction history using a semantically rich action model and provides an overview of the analysis structure. The knowledge view is a basic graphics editor that helps users to record findings with provenance and to organize findings into claims using diagramming techniques. Users can exploit automatically captured interaction history and manually recorded findings to review and revise their visual analysis. Thus, the analysis process can be archived and shared with others for collaborative visual analysis. Secondly, we enable analysts to capture data selections as semantic zones during an analysis, and to reuse these zones on different subsets of data. We present a Select & Slice table that helps analysts to capture, manipulate, and reuse these zones more explicitly during exploratory data analysis. Users can reuse zones, combine zones, and compare and trace items of interest across different semantic zones and data slices. Finally, exploration overviews and searching techniques based on keywords, content similarity, and context helped analysts to develop awareness over the key aspects of the exploration concerning the analysis process and findings. On one hand, they can proactively search analysis processes and findings for reviewing purposes. On the other hand, they can use the system to discover implicit connections between findings and the current line of inquiry, and recommend these related findings during an interactive data exploration. We implemented the models and tools described in this dissertation in Aruvi and HARVEST. Using Aruvi and HARVEST, we studied the implications of these models on a user’s sensemaking process. We adopted the short-term and long-term case studies approach to study support offered by these tools for the sensemaking process. The observations of the case studies were used to evaluate the models

Supporting the sensemaking process in visual analytics

Visual analytics is the science of analytical reasoning facilitated by interactive visual interfaces. It involves interactive exploration of data using visualizations and automated data analysis to gain insight, and to ultimately make better decisions. It aims to support the sensemaking process in which information is collected, organized and analyzed to form new knowledge and inform further action. Interactive visual exploration of the data can lead to many discoveries in terms of relations, patterns, outliers and so on. It is difficult for the human working memory to keep track of all findings during a visual analysis. Also, synthesis of many different findings and relations between those findings increase the information overload and thereby hinders the sensemaking process further. The central theme of this dissertation is How to support users in their sensemaking process during interactive exploration of data? To support the sensemaking process in visual analytics, we mainly focus on how to support users to capture, reuse, review, share, and present the key aspects of interest concerning the analysis process and the findings during interactive exploration of data. For this, we have developed generic models and tools that enable users to capture findings with provenance, and construct arguments; and to review, revise and share their visual analysis. First, we present a sensemaking framework for visual analytics that contains three linked views: a data view, a navigation view and a knowledge view for supporting the sense-making process. The data view offers interactive data visualization tools. The navigation view automatically captures the interaction history using a semantically rich action model and provides an overview of the analysis structure. The knowledge view is a basic graphics editor that helps users to record findings with provenance and to organize findings into claims using diagramming techniques. Users can exploit automatically captured interaction history and manually recorded findings to review and revise their visual analysis. Thus, the analysis process can be archived and shared with others for collaborative visual analysis. Secondly, we enable analysts to capture data selections as semantic zones during an analysis, and to reuse these zones on different subsets of data. We present a Select & Slice table that helps analysts to capture, manipulate, and reuse these zones more explicitly during exploratory data analysis. Users can reuse zones, combine zones, and compare and trace items of interest across different semantic zones and data slices. Finally, exploration overviews and searching techniques based on keywords, content similarity, and context helped analysts to develop awareness over the key aspects of the exploration concerning the analysis process and findings. On one hand, they can proactively search analysis processes and findings for reviewing purposes. On the other hand, they can use the system to discover implicit connections between findings and the current line of inquiry, and recommend these related findings during an interactive data exploration. We implemented the models and tools described in this dissertation in Aruvi and HARVEST. Using Aruvi and HARVEST, we studied the implications of these models on a user’s sensemaking process. We adopted the short-term and long-term case studies approach to study support offered by these tools for the sensemaking process. The observations of the case studies were used to evaluate the models

Connecting the dots with related notes

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

Supporting exploration awareness in information visualization

When users want to continue an analysis performed in the past, done by themselves or by a collaborator, they need an overview of what has been done and found so far. Such an overview helps them to gain a shared knowledge about each otherspsila analysis strategy and continue the analysis. We aim to support users in this process, and thereby support their exploration awareness. We present an information visualization framework with three linked processes: overview, search and retrieve for this purpose. First, we present a userpsilas information interest model that captures key aspects of the exploration process. Exploration overview, and keyword and similarity based search mechanisms are designed based on these key aspects. A metadata view is used to visualize the search results and help users to retrieve specific visualizations from past analysis. Finally, we present three case studies and discuss the support offered by the framework for developing exploration awareness

Supporting exploration awareness for visual analytics

While exploring data using information visualization, analysts try to make sense of the data, build cases, and present them to others. However, if the exploration is long or done in multiple sessions, it can be hard for analysts to remember all interesting visualizations and the relationships among them they have seen. Often, they will see the same or similar visualizations, and are unable to recall when, why and how they have seen something similar. Recalling and retrieving interesting visualizations are important tasks for the analysis processes such as problem solving, reasoning, and conceptualization. In this paper, we argue that offering support for thinking based on past analysis processes is important, and present a solution for this

Supporting exploratory analysis with the Select & Slice table

Abstract In interactive visualization, selection techniques such as dynamic queries and brushing are used to specify and extract items of interest. In other words, users define areas of interest in data space that often have a clear semantic meaning. We call such areas Semantic Zones, and argue that support for their manipulation and reasoning with them is highly useful during exploratory analysis. An important use case is the use of these zones across different subsets of the data, for instance to study the population of semantic zones over time. To support this, we present the Select & Slice Table. Semantic zones are arranged along one axis of the table, and data subsets are arranged along the other axis of the table. Each cell contains a set of items of interest from a data subset that matches the selection specifications of a zone. Items in cells can be visualized in various ways, as a count, as an aggregation of a measure, or as a separate visualization, such that the table gives an overview of the relationship between zones and data subsets. Furthermore, users can reuse zones, combine zones, and compare and trace items of interest across different semantic zones and data subsets. We present two case studies to illustrate the support offered by the Select & Slice table during exploratory analysis of multivariate data. Keywords: H.5.2 [Information Interfaces and Presentation]: User Interfaces—Graphical User Interfaces (GUI)

Supporting exploratory analysis with the Select & Slice table

Abstract In interactive visualization, selection techniques such as dynamic queries and brushing are used to specify and extract items of interest. In other words, users define areas of interest in data space that often have a clear semantic meaning. We call such areas Semantic Zones, and argue that support for their manipulation and reasoning with them is highly useful during exploratory analysis. An important use case is the use of these zones across different subsets of the data, for instance to study the population of semantic zones over time. To support this, we present the Select & Slice Table. Semantic zones are arranged along one axis of the table, and data subsets are arranged along the other axis of the table. Each cell contains a set of items of interest from a data subset that matches the selection specifications of a zone. Items in cells can be visualized in various ways, as a count, as an aggregation of a measure, or as a separate visualization, such that the table gives an overview of the relationship between zones and data subsets. Furthermore, users can reuse zones, combine zones, and compare and trace items of interest across different semantic zones and data subsets. We present two case studies to illustrate the support offered by the Select & Slice table during exploratory analysis of multivariate data. Keywords: H.5.2 [Information Interfaces and Presentation]: User Interfaces—Graphical User Interfaces (GUI)

Supporting exploration awareness in information visualization

When users want to continue an analysis performed in the past, done by themselves or by a collaborator, they need an overview of what has been done and found so far. Such an overview helps them to gain a shared knowledge about each otherspsila analysis strategy and continue the analysis. We aim to support users in this process, and thereby support their exploration awareness. We present an information visualization framework with three linked processes: overview, search and retrieve for this purpose. First, we present a userpsilas information interest model that captures key aspects of the exploration process. Exploration overview, and keyword and similarity based search mechanisms are designed based on these key aspects. A metadata view is used to visualize the search results and help users to retrieve specific visualizations from past analysis. Finally, we present three case studies and discuss the support offered by the framework for developing exploration awareness

VisPad: Integrating visualization, navigation and synthesis

We present a new framework - VisPad - to support the user to revisit the visual exploration process, and to synthesize and disseminate information. It offers three integrated views. The data view allows the user to interactively explore the data. The navigation view captures the exploration process. It enables the user to revisit any particular state and reuse it. The knowledge view enables the user to record his/her findings and the relations between these findings