Conceptual Framework using DL for Airport CEP

Complex event processing (CEP) has emerged as a technological foundation for many time-critical monitoring applications in a smart environment. The goal of CEP is to identify meaningful events (such as opportunities or threats) and respond to them quickly and with the most relevant response.

Airport complex event processing combines data from a wide variety of diverse data streams to infer events or patterns that suggest more complicated and/or dire situations. This conceptual framework leverages Deep Learning (DL) techniques to simplify the processing by encapsulating the events sequence and responses by recognizing an event and then, generating logical responses via supervised learning.

In this Knowledge Sharing article – awarded 3rd Place in the 2018 Knowledge Sharing Competition – Wei Lin and Bill Schmarzo present a simplified airport CEP scenario with an airliner on fire on the tarmac. The sensor inputs will focus on identifying, analyzing and classifying an image and then generating (predicting) the most relevant event responses.

The article is arranged in sections.

• Section 1 is an Introduction of the Analytics Framework for a complex event processing

• Section 2 describes the overall entities (airport, airport CEP architecture, CNN, LSTM) in this study

• Section 3 describes details of image (video frame) feature extraction, recognition, and response forecast analytics aspects

• Section 4 is the conclusion and future work

Read the full article here.