  Aggregation queries are a series of computationally-demanding analytics
operations on grouped and/or time series (streaming) data. They include tasks
such as summation or finding the mean among the items of a group (sharing a
group ID) or within the last N observed tuples. They have a wide range of
applications including in database analytics, operating systems, bank security
and medical sensors. Existing challenges include the increased hardware
utilisation and random memory access patterns that result from hash-based
approaches or multi-tasking as a way to introduce parallelism. There are also
challenges relating to the degree of which the function can be calculated
incrementally for sliding windows, such as with overlapping windows. This paper
presents a pipelined and reconfigurable approach for calculating a wide range
of aggregation queries with minimal hardware overhead.
