Bayesian Approach for Automotive Vehicle Data Analysis

Abstract

Streaming network data can be analyzed by advance machine data methods. Machine data methods are ideal for large scale and sensor concentrated applications. Prediction analytics can be used to support proactive complex event processing while probabilistic graphical model can be extensively used to ascertain data transmitted by sensors. The structure of probabilistic graphical models encompasses variety of different types of models and range of methods relating to them. In this paper, real time sensor (OBD ha-II) device data has been used from telematics competition organized by kaggle.com for driver signature. This device is highly equipped to extract sensors related information such as Accelerometer, Gyroscope, GPS, and Magnetometer. Data cleaning, pre-processing, and integration techniques are performed on data obtained from OBD-II device. We have performed various classification algorithms on sensor data using data mining and machine learning open source tool “WEKA 3.7.10” and have identified that Bayes Net classification technique generates best results.