Big Data Visualization

1. Dhan V Sagar
CB.EN.P2CSE13007

2. Introduction
Data Visualization
Existing Tools
SciPy
Chimera
Taverna
Galaxy
Visualization Toolkit (VTK)
Drawbacks

3. DIVE - Introduction
Data Intensive Visualization Engine
Interactive data pipeline
Multiprocessor
Parallelized operations
Object oriented database
Interoperability

5. DIVE Architecture
DIVE API
Data Pipeline
Stream
Transform
Visualize
Data Exploration
Domain Independent
Data Agnostic

6. Data Representation
Inspired from Ontologies
Data Unit - Datanode
Typed
properties & methods
Inheritance
Can exist in an ontological network or graph
Dataedges - link datanodes
Datanodes are dynamic

7. Data nodes provide Virtual properties
Dataedges implement multiple inheritance models
is-a
contains
part-of
bounded-by

9. Data Import
Data node per column
Property per column
Relationship between datanodes
The pipeline
Plug-ins - create, consume, or transform
Compiled libraries
move data through pins
Maintaining datanode’s properties.

10. SE Considerations
Platform Independent
Support for dynamic languages
Threading, 64 bit architecture
Object Parsing
parser automatically generates datanodes and dataedges from any .NET object

12. Scripting
Interacting with the pipeline
Plug-in scripting
Micro scripting
Data Streaming
Interactive SQL
Pass Through SQL
Backing Store- collection of tables of instance data

13. Example

16. Thank You