Colossal Trajectory Mining

Colossal Trajectory Mining (CTM) detects co-movement patterns inside large scale trajectory datasets. A spatio-temporal references is partitioned in tiles, each tiles has a fixed spatial area and a temporal size. Then, for each tile is computed the set of trajectories whose at least one point is inside the tile boundaries.

Please refer/cite to the following research paper:

• Matteo Francia, Enrico Gallinucci. "Colossal Trajectory Mining: A Unifying Approach to Mine Behavioral Mobility Patterns." Expert Systems with Applications (to appear).

Input dataset

The input dataset must be a table accessible on a Hive installation, the table must have the at least the following columns:

• userid: String contains a custom ID for every point of a trajectory
• trajectoryid: String contains the trajectory ID for every point of a trajectory
• latitude: Double contains the latitude of a point
• longitude: Double contains the longitude of a point
• timestamp: Long contains seconds elapsed since 1/1/1970 (unix_time) as a Long

Output results

Output result are stored on a Hive table named: CTM__par1_val1__...__parN_valN.csv

How to run this project

Unit tests are available via the --debug option on launch.

 rm results/CTM_stats.csv; git pull; ./gradlew

Experimental tests are available by running:

 rm results/CTM_stats.csv; git pull; ./gradlew clean build shadowJar; sh run_CTM.sh