mxkernel: A Novel System Software Stack for Data Processing on Modern Hardware

A Cost Model for Data Stream Processing on Modern Hardware

For stream processing application domains, using queries to process or analyze data incoming from potentially endless streams, low latency and high throughput are key requirements. It is not easy to achieve this as many factors influence the actual runtime of query execution plans and one can not measure all of them individually. Therefore, query optimizers try to overcome this hurdle by using ...

Data Processing in Modern Hardware

Data processing is changing in radical ways from how it has developed in the last four to five decades. On the one hand, data science and big data have brought an unprecedented growth and variety in data sizes, demanding workloads, data types, and applications. From studying social networks on graph data to genomics over string matching algorithms; from low latency key value stores used to retr...

A Hardware/Software Stack for Heterogeneous Systems

Plenty of novel emerging technologies are being proposed and evaluated today, mostly at the device and circuit levels. It is unclear what the impact of different new technologies at the system level will be. What is clear, however, is that new technologies will make their way into systems and will increase the already high complexity of heterogeneous parallel computing platforms, making it ever...

Efficient Query Processing on Modern Hardware

Most database systems translate a given query into an expression in a (physical) algebra, and then start evaluating this algebraic expression to produce the query result. The traditional way to execute these algebraic plans is the iterator model: Every physical algebraic operator conceptually produces a tuple stream from its input, and allows for iterating over this tuple stream. This is a very...

Hardware/Software Processing Architecture for Statistical Data Compression Algorithms