|Abstract:||The collection of historical or real-time data on moving objects is quickly becoming a ubiquitous task. With the help of GPS devices, RFID sensors, RADAR, satellites, and other technologies, mobile objects of all sizes, whether it be a tiny cellphone or a giant ocean liner, can be easily tracked around the globe. Many fundamental problems in the database field have found their parallels in the moving object domain. They include indexing and query processing of moving objects over static or continuous queries and similarity search between moving objects. The same has happened with data mining problems as well. Clustering of moving objects is one popular topic; spatial association patterns is another.
However, even with the recent attention, there are still many unexplored areas in moving objects research. Specifically, higher semantic level problems remain mostly untouched. One example is anomaly detection. With the ever-increasing focus on video surveillance, many cities are tracking and analyzing vehicles as they move throughout the city. With the ultimate goal of automated reporting and alerting, sophisticated algorithms are needed to evaluate the moving object trajectories. Furthermore, associations with other multi-dimensional features will need to be considered as well. Another example is periodic traffic pattern detection. Everyone is familiar with rush hour traffic in big cities, but extracting and representing them in an efficient and concise manner has not been addressed.
To this end, we present our studies in this thesis. With regards to anomaly detection, we present three models to automatically detect moving object anomaly, traffic anomaly, and subspace anomaly. The last of which detects anomalies in a multidimensional space, which is often the case in real world datasets. Additionally, we also address problems that could occur due to sampling in a multidimensional space and how to summarize moving object trajectories for more efficient processing.