The amount of textual data has reached a new scale and continues to grow at an unprecedented rate. IBMs SystemT software is a powerful text analytics system, which offers a query-based interface to reveal the valuable information that lies within these mounds of data. However, traditional server architectures are not capable of analyzing the so-called Big Data in an efficient way, despite the high memory bandwidth that is available. We show that by using a streaming hardware accelerator implemented in reconfigurable logic, the throughput rates of the SystemTs information extraction queries can be improved by an order of magnitude. We present how such a system can be deployed by extending SystemTs existing compilation flow and by using a multi-threaded communication interface that can efficiently use the bandwidth of the accelerator.
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