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arXiv · 1806.01103

Giving Text Analytics a Boost

Raphael Polig·Kubilay Atasu·Laura Chiticariu·Christoph Hagleitner·H. Peter Hofstee·Frederick R. Reiss·Eva Sitaridi·Huaiyu Zhu
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Abstract

The amount of textual data has reached a new scale and continues to grow at an unprecedented rate. IBM's 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 SystemT's information extraction queries can be improved by an order of magnitude. We present how such a system can be deployed by extending SystemT's existing compilation flow and by using a multi-threaded communication interface that can efficiently use the bandwidth of the accelerator.

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Raphael Polig, Kubilay Atasu, Laura Chiticariu, Christoph Hagleitner, H. Peter Hofstee, Frederick R. Reiss, Eva Sitaridi, Huaiyu Zhu. 2018-04-25. Giving Text Analytics a Boost. https://doi.org/10.1109/mm.2014.69

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