Moving Object Trajectories - Demo

Querying and Mining Moving Object Databases Using Places of Interest

Moving objects, carrying location-aware devices, produce trajectory data in the form of a sample of (Oid, t, x, y)-tuples, that contain object identifier and time-space information. The process of querying this huge volume of data may become cumbersome.

 

Recently, the notion of stops and moves were introduced. Intuitively, if a moving object spends a sufficient amount of time in a certain geographic place (which we denote  places of interest of an application), this place is considered a stop of the object’s trajectory. In between stops, a trajectory has moves.

We have proposed how moving object data analysis can benefit from replacing raw trajectory data by a sequence of stops and moves. We presented a formal model and a query language (denoted Lmo) that supports different forms of aggregation. The language could express complex queries involving spatial data stored in a GIS, nonspatial data (stored in a data warehouse) and moving object data. The stops and moves are Lmo language expressible.

 

We have developed an implementation using Java programming language, Postgres database with Postgis spatial extensions and Mondrian OLAP Server.  You can find a technical report on this work at http://arxiv.org/abs/0708.2717.  The demo in this site consists of an implementation of a language that based on regular expressions, denoted smRE, which allows to talk about temporally ordered sequences of stops and moves. We have defined different places of interest: coffees, restaurants, hotels, the Zoo and the Aquarium, represented as OLAP dimension hierarchies. We have converted 30.808.296 points corresponding to the trajectories of 6276 moving objects into compressed trajectories expressed by stops sequences. These reduced trajectories ocuppy only 0.343% of the original size. In this way we can efficiently verify trajectory patterns like "people go from Italian restaurants to  inexpensive coffe shops on monday mornings". 

 

 

Who was involved in this project?

This project is a collaboration between the University of Buenos Aires and the University of Hasselt, funded by a bilateral agreement between FWO in Flandes and SECyT in Argentina (Project #FW/PA05-EXI/005, "GISOLAP: An Approach for Spatial Data Aggregation  based in  OLAP"), and a PICT project in Argentina, #21350, "Using OLAP techniques in GEographic Information Systems".

 

Do you want to see a demo?

Follow this link

 

Future Work

The smRE language is a promising tool for mining trajectory data, specifically in the context of sequential patterns mining with constraints.

We are currently in the process of integrating this development to the Piet Framework Project.

 

Do you need more information about this project?

Send an email to avaisman@dc.uba.ar