7a.002.SBU - The Data Voyager - Interactive Visualization of Multivariate Data with Joint Data & Dimension Reduction and Continuance of the Intelligent Dashboard Project

Project - Summary

Dashboards play a critical role in representing the state of a system/organization & enhance its decision-making process. Building custom dashboards tailored for a specific task/role/organization can be a time-consuming and expensive process. In this project, we present an intelligent visual data exploration tool & automated end-to-end dashboard building framework. For any given tabular dataset, it generates a set of possible univariate & bivariate charts. Each chart is assigned a value based on how statistically interesting it is. Users are presented with the most interesting charts which accelerates the knowledge discovery process. The system also generates a set of fully automated dashboards. Users can choose from the set of pre-built dashboards or select charts to generate a custom dashboard.

Project - Team

Team Member Role Email Phone Number Academic Site/IAB
Klaus Mueller PI (631) 632-1524 Stony Brook University
Darius Coelho Graduate Student Not available Not available Stony Brook University
Bhavya Ghai Graduate Student Not available Not available Stony Brook University
Steven Greenspan Project Mentor Not available Not available CA Technology
Maria Velez Project Mentor Not available Not available CA Technology

Project - Novelty of Approach

Our triple-optimized approach described in the above paper is superior to what is available elsewhere. For this proposed project we will add support for big data via unbiased data and dimension reduction. An auto-configuring dashboard is also novel and not available thus far.

Project - Deliverables

1 System architecture schematic
2 Implementation of statistical analysis module
3 Implementation of task and visualization recommender module

Project - Benefits to IAB presents a novel approach for visual data exploration and building custom dashboards. It offers the perfect tradeoff between automation and flexibility. It employs statistical metrics to assign an ‘interestingness’ score to attributes of a dataset. It then uses NLP techniques to analyze domain specific documents to determine domain-specific ‘interestingness’ and analytical tasks related to these attributes. The system then uses this information to recommend visualizations in a dashboard which expedites the data exploration process. is highly useful in scenarios where users do not have the knowledge or time to create effective dashboards to explore interesting features in their data. Additionally, it allows users apply different “styles” of analyses to their dashboard. In the car dataset analysis, for example, only using car reviews from the Autocar magazine would apply the Autocar style of analysis, and only using car reviews from the Car and Driver magazine would apply the Car and Driver style of analysis.

We are looking forward to engaging in the next stage of the process:

  • further refine the extraction of task association knowledge module via user studies
  • finalize and test the dashboard generator
  • test the system on multiple datasets and tweak its parameters accordingly
  • perform a through usability study of the final system

Project - Presentation Video

Project - Documents

projects/year7/7a.002.sbu.txt · Last modified: 2021/06/02 17:11 by sally.johnson