An Ingestion Based Analytics Framework for Complex Event Processing Engine in Internet of Things

Internet of Things (IoT) is the new paradigm that connects the physical world with the virtual world. The interconnection is generated by the optimal deployment of sensors which continuously generate data and streams it to a data store. The concept drift and data drift are integral characteristics of IoT data. Due to this nature, there is a need to process data from various sources and decipher patterns in them. This process of detecting complex patterns in data is called Complex Event Processing which provides near real-time analytics for various IoT applications. Current CEP deployments have a inherent capability to react to events instantaneously. This leaves room to develop CEPs which are proactive in nature which can take the help of various machine learning (ML) models to work together with CEP. In this paper, the usage of Complex Event Processing (CEP) engine is exhibited that allows the inference of new scenarios out of incoming traffic data. This conversion of historical data into actionable knowledge is undertaken by a Long Short Term Memory (LSTM) model so as to detect the occurrence of an event well before time. The experimental results suggest the rich abilities of Deep Learning to predict events proactively with minimal error. This allows to deal with uncertainties and steps for significant improvement can be made in advance.