This is my presentation for CSIS 638 on 08/03/2011

1. IBM InfoSphere Streams for
Scalable, Real-Time,
Intelligent Transportation
Services
Matt Garren

2. Problem
● Congestion in urban areas
● Too expensive/not enough room to build
more highways
● Large amount of traffic data from
multiple sensors
● Processing needed in real-time
● Large number of users wanting different
views

3. Infosphere Streams
● Structured and Unstructured Data
● Scalable
● Data Flow Graph
○ Streaming Data Objects (SDO)
○ Processing Elements (PE)
○ SPADE processing engine

4. Stream Processing Application
Declarative Engine (SPADE)
● Provides a programming language
○ Intermediate
○ Flexible
○ Type-generic operators
○ Extensible using C++/JAVA
○ Broad range of adapters

5. SPADE: Built in operators
● Source
● Sink
● Functor
● Aggregate
● Join
● Sort
● Barrier
● Punctor
● Split
● Delay

6. Intelligent Transportation Systems
● Stockholm, Sweden
● Functionality
○ Monitor current traffic
○ Compare to historical data
○ Estimate travel times
○ Compute shortest paths
● Pilot system used only historical data
● Real-time data in the future

7. Input
● 2008 GPS data traces
○ 1500 Taxis (1 reading/60 secs)
○ 400 Trucks (1 reading/30 secs)
○ 170 Million probe points over the year
○ 1000 GPS readings/sec peak rate

8. Application Logic
● Steps
○ Obtain, clean, de-noise, match to
underlying roads/networks
○ Aggregate to obtain statistics per link
and per time interval
○ Compute derived information (shortest
times, etc)

9. Data Flow

10. GPS Data Issues
● Matching to underlying roads
● Match to regions instead of links
○ Parking lots

11. End User Interactions (ITS)
● Visualize entire city on Google Earth
● Submit specific queries
● Web-based visualizations

12. Performance
● Cluster
○ 64-bit Intel Xeon quad core processors
○ 3GHz clock speed
○ 16 Gb Ram per machine

13. Performance

14. Performance

15. Questions?