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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.
© 2014 ETH Zurich