Yedendra B. Shrinivasan

Supporting the Sensemaking Process in Visual Analytics

During my PhD research, I am focusing on how to enable users to capture interesting aspects during interactive information exploration and support their reasoning process. Publications related to this research are listed below:
Supporting the Analytical Reasoning Process in Information Visualization

Abstract

We present a new information visualization framework that supports the analytical reasoning process. It consists of three views - a data view, a knowledge view and a navigation view. The data view offers interactive information visualization tools. The knowledge view enables the analyst to record analysis artifacts such as findings, hypotheses and so on. The navigation view provides an overview of the exploration process by capturing the visualization states automatically. An analysis artifact recorded in the knowledge view can be linked to a visualization state in the navigation view. The analyst can revisit a visualization state from both the navigation and knowledge views to review the analysis and reuse it to look for alternate views. The whole analysis process can be saved along with the synthesized information. We present a user study and discuss the perceived usefulness of a prototype based on this framework that we have developed.

Aruvi
Citation: Shrinivasan Y.B and van Wijk .J.J, "Support the Analytical Reasoning Process in Information Visualization", ACM Human Factors in Computing Systems (CHI), Florence, Italy 2008. (pdf) (Bibtex)

A poster was presented at IEEE symposium on Visual Analytics and Science Technology - 2007, Sacramento, California, USA and received a best poster award. Click here to view the poster. A video demo of the Aruvi Prototype can be found here.

Supporting Exploration Awareness in Information Visualization

Abstract

When users want to continue or review an analysis performed in the past, either their own or a collaborator’s, they need an overview of what has been done and found so far. Such an overview helps them gain shared knowledge about each other’s analysis strategies and continue the analysis. The authors aim to support users in this process and, thereby, support their exploration awareness. They present an information-visualization framework with three linked processes—overview, search, and retrieve—for this purpose. First, they present a users’ information-interest model that captures key aspects of the exploration process. These key aspects form the basis for exploration overview and keyword- and similarity-based search mechanisms. Analysts can use a metadata view to visualize the search results and help users retrieve specific visualizations from past analyses. Finally, the authors present three case studies and discuss the framework’s support for developing exploration awareness.

Aruvi
Citation: Shrinivasan Y.B and van Wijk .J.J, "Support Exploration Awareness in Information Visualization", IEEE Computer Graphics and Applications, vol. 29, no. 5, pp. 34-43, Sep./Oct. 2009, doi:10.1109/MCG.2009.87. (pdf) (Bibtex)
Connecting the Dots in Visual Analysis

Abstract

During visual analysis, users must often connect insights discovered at various points of time. This process is often called “connecting the dots.” When analysts interactively explore complex datasets over multiple sessions, they may uncover a large number of findings. As a result, it is often difficult for them to recall the past insights, views and concepts that are most relevant to their current line of inquiry. This challenge is even more difficult during collaborative analysis tasks where they need to find connections between their own discoveries and insights found by others.

In this paper, we describe a context-based retrieval algorithm to identify notes, views and concepts from users’ past analyses that are most relevant to a view or a note based on their line of inquiry. We then describe a related notes recommendation feature that surfaces the most relevant items to the user as they work based on this algorithm. We have implemented this recommendation feature in HARVEST, a web based visual analytic system. We evaluate the related notes recommendation feature of HARVEST through a case study and discuss the implications of our approach.

Aruvi
Citation: Shrinivasan Y.B, Gotz .D and Lu .J "Connecting the Dots in Visual Analytics", IEEE Symposium on Visual Analytics Science and Technology, pp. 123 - 130, October 2009. (pdf) (DOI)

This research work is done in collaboration with IBM Reseach New York.

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.

 

Aruvi
Citation: Shrinivasan Y.B and van Wijk .J.J "Support Exploratory Analysis with the Select & Slice Table", To appear in Eurographics/IEEE Symposium on Visualization, June 2010. (pdf)
Copyright 2010. Y.B. Shrinivasan. All rights reserved