1 / 16

Visualizing Massive Multi-Digraphs

Visualizing Massive Multi-Digraphs. James Abello Jeffrey Korn Information Visualization Research Shannon Laboratories, AT&T Labs-Research All the graphs copied from “Visualizing massive Multi-Digraphs”. Massive Graph Visualizer (MGV).

francesca-roberts
Download Presentation

Visualizing Massive Multi-Digraphs

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Visualizing Massive Multi-Digraphs James Abello Jeffrey Korn Information Visualization Research Shannon Laboratories, AT&T Labs-Research All the graphs copied from “Visualizing massive Multi-Digraphs”

  2. Massive Graph Visualizer (MGV) • Visualization and exploration system for massive multi-digraph navigation. • Assumes a vertex set of the underlying digraph corresponds to leave sets. • Out-of-core graph hierarchy and visual representation of each hierarchy slice. • Implemented in C and Java 3D. • Applied in geographic information systems, telecommunications traffic and internet data …

  3. Problems with data visualization • Massive data size • Bottlenecks • I/O bandwidth • Screen • Solution Hierarchical graph slices

  4. Traditional graph representation Traditional nodes and edges representation of a fully connected graph with 20 nodes

  5. Hierarchical graph slice rationale(1) • Build hierarchical multi-digraph layers on top of input multi-digraph. • Each layer is obtained from coalescing disjoint sets of vertices at previous level • In short, convert multi-digraph data into hierarchical data structure. V sets, E sets Root, Leaves, Height

  6. Hierarchical graph slice rationale(2) • Layer of each level is a subgraph with vertex and edges , so called Hierarchical Graph Slices. • On each slice, less nodes, much less edges.

  7. Handling two bottlenecks • The original graph is in the external memory,tree is computed and stored in RAM. Engine needs to computes one slice for interface at a time upon request. • Panoramic 3D display provides hierarchical and horizontal navigation thru all nodes and edges.no information lost

  8. Slice View Interfaces • MGV provides flexible interface. • Works on adjacency representation matrix.similar to representation of Needle Grid. • Handle massive data :AT&T call detail multi-digraph has 275million daily increment on 260 million vertices.

  9. Needle grid • Edge maps into a little tick • Lines weighted By color, length, width, orientation

  10. Star Maps • Rearrange matrix into circular histogram • Well focused • Detail data triggered By mouse

  11. Multi-comb • stack of star maps,single object represent aggregated view of millions of edges. • 3D coordinates facilitates data evaluation. • Useful for animation of data evolution

  12. Multi-wedge • Each wedge is the distribution spectrum of a state. • 2D

  13. Aggregated views • Simply splice the segment to single bar • User move the cursor into the bar for part information

  14. Usability metrics • Ease of Use & Navigation • Good First Impression • High User Retention over Time • High Learnability • Lesser number of user errors

  15. Conclusion on MGV • Computational engine + Java based user interface • Engine runs at a web server, communication thru XML. • Java provides fast rendering • Hierarchical algorithm facilitates navigation on slice, actually integrates visualization and computation. • Large class of massive data sets.

  16. Questions ?and thank you!

More Related