Contextual Pattern Discovery in Ambient Intelligent Application

Abstract

Ambient Intelligence(Aarts, Harwig and Schuurmans, 2001) contributes by enriching the oil and gas environment with technology (mainly sensors and devices interconnected through a network) and built a system to help plant operators to make decisions based on real-time information gathered and historical data accumulated. Ambient Intelligence puts together all these resources to provide flexible and intelligent services to users acting in their environment. Besides, Information Fusion (Llinas, 2002) studies theories and methods to effectively combine data from multiple sensors and related information to achieve more specific inferences that could be achieved by using a single, independent sensor. Information fused from sensors and data mining analysis has recently attracted the attention of the research community for real-world applications. In this sense, the deployment of an ambient intelligent offshore petroleum environment will help to figure out a risky scenario based on the events occurred in the past related to anomalies and the profile of the current employee (role, location, etc.).

In this paper we propose an information fusion model for an ambient intelligent oil environment in which employees are alerted about possible risk situations while their are moving around their working place. The layered architecture, implements a reasoning engine capable of intelligently filtering the context profile of the employee (role, location) for the feature selection of an inter-transaction mining process. Depending on the employee contextual information he will receive intelligent alerts based on the prediction model that use his role and his current location. This model provides the big picture about risk analysis for that employee at that place in that moment.