Identify traffic congestion in real time from data
City transport management
Determine driver behavior and potential hazards to improve passenger experience, based on sensor data
Receive alerts on potentially dangerous events based on camera feeds, with minimal human intervention
Credit card fraud recognition
Detect suspicious transactions from million transactions in realtime.
ILED is a software tool for machine learning complex event definitions from temporal data
1. Provide examples
Gather examples to learn from,
in the form of time-stamped event streams
2. Describe your target language
Specify generic patterns to compose
the rules form
3. Perform learning
ILED uses machine learning techniques
to automatically extract rules defining interesting events from data
4. See results
get the learnt event definitions as a set of logical rules
In which domains can I use ILED?
If you have streams of data and interesting events that can be detected from those streams, ILED
can help you.
Indicative streams of data and corresponding detectable events include:
- Transaction data (from online shops, credit cards) used to detect fraud, false orders, suspicious interaction.
- Events recognized from cameras (such as abrupt motion, people running, smoke rising) for detection of
possible collision, dangerous driving, crisis detection.
- Events recognized from other sensors (speed, proximity, acceleration, sound, temperature) to detect traffic congestion.
ILED has been used for learning
in the follow domains
- Public Space Surveillance: the
- City Transport Management: the
- See more