Current Projects

Data Center Model

Large enterprise datacenters which have evolved over time contain a heterogeneous mix of machines, switches, and virtual networks (in contrast to more idealized, “green field” datacenters built as a single unit), and run a mix of inter-dependent services, frequently with complex constraints on placement and communication. More...


Peer-to-Peer Database Replication

When dealing with data sets that are heavily read-dominated and seldom updated it is often beneficial to cache parts of a database locally on all machines. But with an increasing number of machines scalability problems arise as the data and all its cached copies have to be updated. More...


Rule Engine

In business rule engines that are accessed by thousands of clients simultaneously, retrieving rules in a timely manner is of the essence. In this project we look at problems that arise in such engines and which indexing strategies are applicable in this environment. More...


Privacy in the Cloud

Cloud computing is the next big thing. But many potential users hesitate to outsource their computing needs to a cloud service provider because they do not want to outsource control. This project addresses the need to encrypt databases in the cloud and at the same time execute complex SQL queries efficiently. The goal is to use the computing power of a cloud service and at the same time preserve privacy. A dictionary-based encoding is used to achieve this goal. More...


SharedDB

SharedDB is a relational database that is designed to handle large and complex transactional and analytic workloads. On modern multicore machines, traditional databases handle such workloads by executing queries one-at-a-time on separate cores which leads to unpredictable resource contention and query interaction. More...


Storing and Processing Temporal Data in a Main Memory Column Store

Column stores have been shown to outperform row stores significantly in a number of recent studies. In this project we investigate alternative approaches to extend column stores with versioning; i.e., the maintenance of historic data and time-travel queries. On the one hand, adding versioning can simplify the design of a column store because it provides a solution for the implementation of updates, traditionally a weak point in the design of column stores. On the negative side, implementing a versioned column store is challenging because it imposes a two dimensional clustering problem. More...


Temporary Crescando

Over the last few years, temporal data has become increasingly important, and the possibility to answer temporal queries is a common use case in modern applications. Especially for more detailed data anlysis, the added time domain is of great significance. In most temporal database systems, the querying on the additional temporal domain is done via a data index. More...