Some of the key research areas of the UCR DBLab are:
Spatio-temporal queries: Indexing techniques for moving points and
complex spatio-temporal pattern queries.
Time series: Similarity searching in sequence databases, medical
time series, and approximating range queries.
Core data mining: Tools for visualizing and data mining, and
adaptive classification techniques.
Network data: Information retrieval in peer-to-peer systems, and
analysis of sensor data.
Search in databases: Information discovery in (semi)structured
data, and results navigation.
Data entry: Capturing and querying mixtures of structured and
unstructured data.
We put emphasis on interdisciplinary projects, which have involved
healthcare, entomology, disaster management, anthropology, and social
networks.
News:
Eamonn Keogh has created a time series repository for the data mining/machine learning community, to encourage reproducible research for time series classification and clustering. Webpage
Marios Hadjieleftheriou has created a Spatial Index Library which provides a general framework for developing spatial indices. Currently it defines generic interfaces, provides simple main memory and disk based storage managers and a robust implementation of an R*-tree, an MVR-tree and a TPR-tree. Webpage
