Risk-Factor-Identification-using-Truck-Fleet-Sensor-Data

• This usecase is to analyse various parameter of a truck fleet.
• Each truck has been equipped to log location and event data.
• These events are streamed back to a datacenter where we will be processing the data.
• The company wants to use this data to better understand risk.

Data

• Collected geo-location and truck data has been provided.
• Truck data is small and can be stored in RDBMS – to be imported from sqoop.
• Geo-location data will be stored on HDFS

Architecture

Steps

• Load the captured sensor data into Hadoop (HDFS)
• Load truck data from RDBMS to HDFS/Hive
• Run Hive, Pig scripts that compute truck mileage and driver risk factor.
• Access the refined sensor data with Microsoft Excel
• Visualize the sensor data using Excel Power View / Pivot Table /Graphs.

Loading Data into Hive

Analysing Data

The business objective is to better understand the risk the company is under from fatigue of drivers, over-used trucks, and the impact of various trucking events on risk.