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projects:year5:16.10

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16.10 - Interactive Visual Exploration of Large Graphs with Enhanced Sampling and Summarization

Project - Summary

Many real-world systems are so large that capturing them entirely, analyzing them to extract information, and visualizing them for decision making are resource-consuming and challenging tasks. It is necessary to develop sampling and summarization approaches and integrate them with visualization interfaces to study large-scale graphs to understand the underlying real-world systems. We have designed and implemented a new similarity metric for use in graph summarization, allowing large graphs to be rendered more easily. The cross-platform, touch-based interface in Figure 1 illustrates the summarization hierarchy created by our metric with a real-world dataset and allows users to control the level of summarization.

Project - Team

Team Member Role Email Phone Number Academic Site/IAB
Christoph Borst PI Not available Not available UL Lafayette
Mehmet Engin Tozal PI Not available Not available UL Lafayette
Nicholas Lipari Graduate Student Not available Not available UL Lafayette
Md Enamul Haque Graduate Student Not available Not available UL Lafayette

Project - Impact and Uses/Benefits

Ranking is a very classical problem that is very relevant to each and every organization - as organizations rank departments, products, clients, or geographical regions for important decisions. Yet, ranking - for example in situations like KPI dashboards use a composite index with linear weighted models to compare objects; While existing visualization dashboards do provide drill down capabilities, they do not offer deeper understanding with respect to how well the objects compare with each other particularly conflicts or missing information. The proposed approaches offer clear benefits in terms of: (1) Reducing manual effort involved in developing weights for ranking by experts that would save time and money, (2) Offers deeper insights into how objects are ordered for people to make better decisions in government and industry domains that could potentially save costs and improve the performance of decision making in the context of ranking.

Project - Deep Dive

Project - Documents

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projects/year5/16.10.1566401641.txt.gz · Last modified: 2019/08/21 10:34 by sally.johnson