Topics for Student Projects
Byte-addressable Persistent Memory
New storage devices provide much better performance characteristics than rotating drives or flash-based SSDs. However, in order to exploit the full potential of these devices, the database storage techniques must be reconsidered.
Holistic Application/Database Tuning
The interaction between applications and databases is often inefficient (too many too tiny queries). this issue cannot be addressed on the DB side only. The goal of this (series of) projects is to develop techniques for automated application and database tuning.
Query Processing and Optimization
Declarative query languages are now de-coupled from DBMSs. Well-known query processing and optimization techniques require re-consideration in a broad area of distributed data processing. Recently introduced approaches, such as top-down optimization and adaptive execution also require further exploration.
Application of Machine Learning to DB Administration Tasks
Several computationally hard problems, such as selection of indexes, selection of materialized views, data partitioning may benefit from the application of ML techniques.
Multi-Version Data Structures
The availability of huge volumes of storage enables the use of temporal data. To make the processing of temporal data efficient, new data storage techniques should be designed and analyzed.
Machine Learning over Structured Data
Recent publications demonstrate that combining ML with DB techniques may be beneficial in terms of both computational performance and quality of results.
Several application areas, including semantic Web, Linked data, social networks etc. use graph models, several prototypes of graph databases exist, but their performance is often below the expectations.
Students who are interested in any of those subjects are welcomed to contact the Head of Laboratory Boris Asenovich Novikov.
The list of current topics for student projects can be found here.