CAREER: Query Compilation Techniques for Complex Analytics on Enterprise Clusters (PI): Sharing of data sets can provide tremendous mutual benefits for industry, researchers and nonprofit organizations. For example, companies can profit from the fact that university researchers explore their data sets and make discoveries, which help the company to improve their business. At the same time, researchers are always on the search for real world data sets to show that their newly developed techniques work in practice. Unfortunately, many attempts to share relevant data sets between different stakeholders in industry and academia fail or require a large investment to make data sharing possible. A major obstacle is that data often comes with prohibitive restrictions on how it can be used (requiring e.g., the enforcement of legal terms or other policies, handling data privacy issues, etc.). In order to enforce these requirements today, lawyers are usually involved in negotiation the terms of each contract. It is not atypical that this process of creating an individual contract for data sharing ends up in protracted negotiations, which are both disconnected from what the actual stakeholders aim to do and fraught as both sides struggle with the implications and possibilities of modern security, privacy, and data sharing techniques. Worse, fear of missing a loophole in how the data might be (mis)used often prevents many data sharing efforts from even getting off the ground. To address these challenges, our new data sharing spoke will enable data providers to easily share data while enforcing constraints on the use of the data. This effort has two key components:(1) Creating a licensing model for data that facilitates sharing data that is not necessarily open or free between different organizations and (2) Developing a prototype data sharing software platform, ShareDB, which enforces the terms and restrictions of the developed licenses. We believe these efforts will have a transformative impact on how data sharing takes place. By moving data out of the silos of individuals and single organizations and into the hands of broader society, we can tackle many societally significant problems.
Google Faculty Research Award (PI) 
Together with SAP we are exploring new data models for Polystores. (Co-PI) 
Together with Oracle and Mellanox we are investigating the implications of RDMA on OLTP and OLAP data management systems. (PI)
Data Management for Small High-Performance Clusters (PI)
Two current hardware trends will fundamentally change the design of modern parallel analytical systems:
(1) high-performance RDMA capable networks such as Infiniband FDR/EDR and
(2) high-end many-core machines with considerable amounts of main memory.
Existing parallel analytical systems, such as Spark and Hadoop, are built in a fundamentally wrong way to effectively leverage the benefits of these trends since these systems target the wrong hardware:
huge cloud deployments with cheap but low-end machines connected via high-latency low-bandwidth networks.
This is not the infrastructure most businesses nor defense agencies operate.
Instead, for the increasing need for advance statistical machine learning techniques and agile analytics we see the future in Small High-Performance Computing (SHPC) clusters.
Already today, SHPC clusters that are equipped with fast networks and terabytes of main memory are reasonably affordable.
Together with Airforce we are exploring how data management systems, especially analytical systems, for SHPC clusters have to change.