Learning a Partitioning Advisor with Deep Reinforcement Learning

Commercial data analytics products such as Microsoft Azure SQL Data Warehouse or Amazon Redshift provide ready-to-use scale-out database solutions for OLAP-style workloads in the cloud. While the provisioning of a database cluster is usually fully automated by cloud providers, customers typically still have to make important design decisions which were traditionally made by the database administrator such as selecting the partitioning schemes. In this paper we introduce a learned partitioning advisor for analytical OLAP-style workloads based on Deep Reinforcement Learning (DRL). The main idea is that a DRL agent learns its decisions based on experience by monitoring the rewards for different workloads and partitioning schemes. We evaluate our learned partitioning advisor in an experimental evaluation with different databases schemata and workloads of varying complexity. In the evaluation, we show that our advisor is not only able to find partitionings that outperform existing approaches for automated partitioning design but that it also can easily adjust to different deployments. This is especially important in cloud setups where customers can easily migrate their cluster to a new set of (virtual) machines

Developing a distributed electronic health-record store for India

The DIGHT project is addressing the problem of building a scalable and highly available information store for the Electronic Health Records (EHRs) of the over one billion citizens of India

Taking real-life seriously : an approach to decomposing context beyond 'environment' in living labs

The maturity of Living Labs has grown and several researchers have tried to create a uniform definition of what Living Labs are by emphasizing the multi-method and real-life, contextual approach. Although researchers thus recognize the importance of context in Living Labs, they do not provide insights into how context can be taken into account. The real-life context predominantly focuses on the in-situ use of a product during field trials where users are observed in their everyday life. The contribution of this paper will be twofold. By means of a case study we will show how context can be evaluated in the front end of design, so Living Lab researchers are no longer dependent on the readiness level of a product, and we will show how field trials can be evaluated in a more structured way to cover all components of context. By using a framework to evaluate the impact of context on product use, Living Lab researchers can improve the overall effectiveness of data gathering and analysis methods in a Living Lab project