Publication

The rapidly increasing amount of data available for real-time analysis (i.e., so-called operational business intelligence) is creating an interesting opportunity for creative approaches to speeding up data processing algorithms. One such approach that is starting to become more common is using hardware accelerators for stream processing. Typically these accelerators are implemented on top of reconfigurable hardware, known as fieldprogrammable gate arrays (FPGAs). Though the value of FPGAs for data warehouses is gradually recognized by the database community, their true potential for various business analytic tasks is yet unexplored. In this line of research, we investigate FPGA technology in the context of extreme data processing looking for opportunities where FPGAs can be exploited to improve over classical CPU-based architectures. We introduce a framework for FPGA-accelerated (real-time) analytics including a query-tohardware compiler for static complex event detection, an XPath engine for dynamic query workloads, and templates for highspeed data mining operators in hardware.

@inproceedings{abc,
	abstract = {The rapidly increasing amount of data available for
real-time analysis (i.e., so-called operational business intelligence)
is creating an interesting opportunity for creative approaches to
speeding up data processing algorithms. One such approach that
is starting to become more common is using hardware accelerators
for stream processing. Typically these accelerators are
implemented on top of reconfigurable hardware, known as fieldprogrammable
gate arrays (FPGAs). Though the value of FPGAs
for data warehouses is gradually recognized by the database
community, their true potential for various business analytic tasks
is yet unexplored. In this line of research, we investigate FPGA
technology in the context of extreme data processing looking for
opportunities where FPGAs can be exploited to improve over
classical CPU-based architectures. We introduce a framework
for FPGA-accelerated (real-time) analytics including a query-tohardware
compiler for static complex event detection, an XPath
engine for dynamic query workloads, and templates for highspeed
data mining operators in hardware.},
	author = {Louis Woods and Gustavo Alonso},
	booktitle = {Workshops Proceedings of the 27th International Conference on Data Engineering, ICDE 2011},
	title = {Fast Data Analytics with FPGAs},
	url = {http://dx.doi.org/10.1109/ICDEW.2011.5767669},
	venue = {Hannover, Germany},
	year = {2011}
}