Contribute your skills

The MonetDB team invites people to contribute their skills to an even better uptake of this pioneering work.  A three-month internship with the core development team in Amsterdam is offered to qualified candidates.  MonetDB supports students embarking on their short term (3-6 month) master projects either remotely or stationed in Amsterdam.

Master project: MonetDB Data Vaults
Scientific discoveries increasingly rely on the ability to efficiently grind massive amounts of experimental data using database technologies.  To better support the data intensive scientific researches, one of the fundamental issues is to equip relational DBMS with flexible techniques to deal with the large amount of existing scientific data stored in various (standard) data formats.  The target of this project is to extend the MonetDB kernel with a new data vault for a scientific data format.  You will design the storage model for the data format and evaluate it with use cases from its corresponding scientific domain, using SQL and/or SciQL.  You can chose one of the following data sets to work with:
1) Seismic waveform data
2) Spectroscopy biological data
3) Spatial database of pathology images
For detailed information, please contact us

 

Topics of interest


Linked-Open data
SPARQL and RDF
Fusion with statistics (e.g. R)
Distributed query processsing
Performance using GPUs
Array support in SQL
One-minute database kernel
Processing result-sets
Query morphing
Scientific applications

 PhD project vacancy: Adaptive Indexing
This PhD position is in the broad area of adaptive indexing techniques for modern database systems. Adaptive indexing is a major challenge in database research; it tries to completely alleviate the need for indexing and tuning steps leading to database systems that require no physical design set-up and can  cope immediately with any workload. This leads to database systems that are instantly usable and thus  more applicable to numerous new applications that now consider databases too slow, too complex and too old. This position will focus specifically on adaptive indexing for extremely large databases where traditional indexing  represents a major bottleneck.

For detailed information, please contact us