Research Agenda

Physical aspects - indexing, query processing and optimization - of Moving Objects Databases (MOD)

The domain of spatiotemporal applications is a treasure trove of new types of data and queries. Examples include navigational systems, monitoring animals or natural phenomena, etc. Work in this area is dominated by related research from the spatial and temporal domains, so far, with little attention towards the true nature of spatiotemporal phenomena. In this work, the focus is on a spatiotemporal sub-domain, namely the trajectories of moving point objects. In this research, we deal with novel types of spatiotemporal queries, as well as algorithms to process those, appropriate indexing techniques and query optimization tools.

Algorithms and techniques for Location-Based Services (LBS)

Novel applications of spatiotemporal databases include the, so called, Location-Based Services (LBS), which involve stationery and moving clients (requesting services) and servers (providing services). In this field, we develop novel routing algorithms and processing techniques, and build data and query sets for benchmarking purposes.

The 'Pattern Base Management System (PBMS)' idea

A major challenge for the database discipline in the 21st century is the efficient manipulation of huge volumes of data along with the extraction and management of useful knowledge from these data sets. Nowadays, sophisticated data processing tools, based on data mining, pattern recognition and other knowledge extraction techniques, produce knowledge artifacts that represent large parts of underlying data sets in a concise and meaningful way (e.g., association rules, frequent parts of signals, etc.). In this research, we refer to these knowledge artifacts as patterns. We envision pattern repositories, called Pattern Base Management Systems (PBMS), for treating patterns as persistent objects that deserve effective and efficient storing, processing, and querying.

Data Mining tools for (stationery or moving) Spatial Objects

As the number of moving vehicles increases rapidly everyday, the need for knowledge extraction about traveled routes, as well as prediction of troublesome situations is vital. In this research, we combine models and aspects used to represent motion, such as trajectories and their properties, in a spatiotemporal environment with fundamental data mining methods. The objectives are, among others, the following: extracting knowledge about movement of vehicles, predicting troublesome situations, such as "traffic jams", and providing alternatives routes.