Keywords

sensor data; knowledge acquisition; machine learning; complex event processing; wavellite

Location

Session F5: Advances in Environmental Software Systems

Start Date

18-6-2014 9:00 AM

End Date

18-6-2014 10:20 AM

Abstract

Environmental knowledge systems that build on sensor-based environmental monitoring rely on techniques in knowledge acquisition and representation to interpret the numbers obtained in measurement for what they tell about the monitored environment. Languages and systems in knowledge representation and reasoning, specifically Semantic Web technologies, support the formulation and execution of rules, a technique that enables deductive inference in a knowledge base. This technique has been used to demonstrate inference on sensor data. While the approach certainly has its merits, it is often demonstrated for numerical thresholds and, thus, for relatively trivial “semantic enrichment.” In reality, knowledge acquisition tasks of interest to environmental knowledge systems that build on sensor-based environmental monitoring are often more challenging. They rely on advanced computational techniques and models, e.g. in machine learning or complex event processing. In order to ease the formulation and execution of such tasks, systems need to integrate such techniques. Towards this end, we present the integration of machine learning with WEKA and complex event processing with Esper in Wavellite.

 
Jun 18th, 9:00 AM Jun 18th, 10:20 AM

Abstractions from Sensor Data with Complex Event Processing and Machine Learning

Session F5: Advances in Environmental Software Systems

Environmental knowledge systems that build on sensor-based environmental monitoring rely on techniques in knowledge acquisition and representation to interpret the numbers obtained in measurement for what they tell about the monitored environment. Languages and systems in knowledge representation and reasoning, specifically Semantic Web technologies, support the formulation and execution of rules, a technique that enables deductive inference in a knowledge base. This technique has been used to demonstrate inference on sensor data. While the approach certainly has its merits, it is often demonstrated for numerical thresholds and, thus, for relatively trivial “semantic enrichment.” In reality, knowledge acquisition tasks of interest to environmental knowledge systems that build on sensor-based environmental monitoring are often more challenging. They rely on advanced computational techniques and models, e.g. in machine learning or complex event processing. In order to ease the formulation and execution of such tasks, systems need to integrate such techniques. Towards this end, we present the integration of machine learning with WEKA and complex event processing with Esper in Wavellite.