Recent times have seen remarkable increase in number of mobile devices connected to the Internet and a variety of uses based on their location data have come into existence. This increase in number necessitates a scalable map-matching solution to meet the ever-increasing demand. Map Matching is the problem of how to match recorded geographic coordinates to a logical model of the real world. Thereby, measured GPS coordinates are matched onto a reference coordinate system (e.g. a map). The existing solutions for map matching do not scale and therefore are no suitable for Big Data. This project is mainly based on research conducted by Newson & Krumm and Mattheis et al. Based on the above, this project is aimed at developing a map matching application that runs on Apache Spark Streaming, where the map matching is done at scale and in a streaming fashion.
This project is based on BMWCarIT's Barefoot library. Check out the source here: https://github.com/bmwcarit/barefoot
The final report can be found here: https://github.com/achintya-kumar/SMM-with-Spark-Streaming/blob/master/Scalable_Map_Matching_Final_Report.pdf
This project is composed in Cloudera Quickstart VM and later tested on a fully-distributed Cloudera cluster. For specifications of the cluster, kindly refer to the above project report.
- Install Maven and Java 8. (Set
JAVA_HOMEif required) - As mentioned above, the project requires Barefoot library jar as a Maven dependency. It is therefore necessary that Barefoot is downloaded and installed as follows:
git clone https://github.com/bmwcarit/barefoot.git
cd barefoot
mvn clean install -DskipTests - Now the project can be built
mvn clean package
Add-qto build silently.