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.
Announcements:
A Post-Doctoral Researcher position is available, starting as soon as possible, at the Database Lab at the University of California, Riverside.
This NSF-funded position is for 1 year, with possibility for extension if funding permits.
The ideal candidate should have a strong publication record in one or more of the following areas: Health Informatics, Web/Social Data Management, Information Retrieval, Databases (involving text and/or graph data).
PhD in Computer Science or equivalent is required.
Further, strong paper writing skills are required.
The position will also involve some implementation tasks.
If you are interested, please send your CV to Dr. Hristidis.
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
