Deep Query Optimization
Query optimization is at the heart of every database system and enables fast and efficient query processing. State-of-the-art query optimisation includes the translation of a logical plan to a physical plan on the granularity of complete operators. We are currently working on deepening this process by breaking up the abstraction of a (physical) operator to consider more fine-granular subcomponents. With that, we aim to apply many more optimizations which would otherwise be missed.
mutable
Our goal with mutable is to develop a modular database system that allows for fast prototyping of research ideas. The design should enable abstraction such that high-level ideas and algorithms can easily be implemented into the system. At the same time, we leverage modern concepts of software design to achieve high performance. We do not want to trade off between abstraction and performance, instead we work towards the best of both worlds.
Blockchain
Applied Machine and Deep Learning
What is the impact on ML and DL to databases and big data technology? We are both interested in applying ML/DL to real-world problems as well as leveraging DB-technology to improve ML and DL methods. JD gave a keynote on this topic at VLDB 2017 and will give another keynote at the DEEM-workshop (Data Management for End-To-End Machine Learning) at SIGMOD 2018. We have several ongoing projects in that space such as weather prediction using deep learning and DeepTuneDB/NoDBA and also a start-up: d:AI:mond.ai.
Indexing
indexing is key to providing efficient query processing in almost any information system. We are interested in problems that may not be solved with existing techniques or require a fresh look at available techniques. We are especially interested in adaptive indexing techniques (adapting to query/workload patterns) and indexing support under high update rates (millions of updates per second). We won a VLDB 2014 best paper award for this work.