Processing Life Science Data using Scalable Database Technology

This talk provides an overview of our (DBIS) research work in the area
of Genomics (Life Science), one of the most challenging areas for
computer science and database systems. Based on the current technology
we describe some of the challenges that we try to tackle using
scalable database technology.

First, we discuss some of fundamental problems that arise when
integrating, storing, and accessing genomic data in an ORDBMS. The
genomics view differs in several aspects compared to the database
view. Based on our observations and our experience with real users we
describe our approach to data integration by suggesting a processing
framework that encompasses flexibility and extensibility.

Second, we show that existing approaches to data cleansing have very
little success in the area of life science. I show how to identify
inconsistencies and errors in life science data before trying to
correct them, if possible.

Third, we focus on one of the major algorithms for similarity search
and discuss its alternatives for integration it into a database
oriented processing environment.

Finally, based on cooperation with life scientists I use a challenging
problem from the area of genetics (alternative splicing), to show how
to support complex data processing using data base technology both on
the data and metadata level.