Solving the Traffic and Flitter Challenges with Tulip

International audienceWe present our visualization systems and findings for the Badge and Network Traffic as well as the Social Network and Geospatial challenges of the 2009 VAST contest. The summary starts by presenting an overview of our time series encoding of badge information and network traffic. Our findings suggest that employee 30 may be of interest. In the second part of the paper, we describe our system for finding subgraphs in the social network subject to degree constraints. Subsequently, we present our most likely candidate network which is similar to scenario B

Interactive Searching and Visualization of Patterns in Attributed Graphs.

International audienceSearching for patterns in graphs and visualizing the search results is an active area of research with numerous applications. With the continual growth of database size, querying these databases often results in multiple solutions. Text-based systems present search results as a list, and going over all solutions can be tedious. In this paper, we present an interactive visualization system that helps users find patterns in graphs and visualizes the search results. The user draws a source pattern and labels it with attributes. Based on these attributes and connectivity constraints, simplified subgraphs are generated, containing all the possible solutions. The system is quite generic and capable of searching patterns and approximate solutions in a variety of data sets

Solving the traffic and flitter challenges with tulip

We present our visualization systems and findings for the Badge and Network Traffic as well as the Social Network and Geospa-tial challenges of the 2009 VAST contest. The summary starts by presenting an overview of our time series encoding of badge infor-mation and network traffic. Our findings suggest that employee 30 may be of interest. In the second part of the paper, we describe our system for finding subgraphs in the social network subject to degree constraints. Subsequently, we present our most likely can-didate network which is similar to scenario B