TITLE:
FPGA-Based Stream Processing for Frequent Itemset Mining with Incremental Multiple Hashes
AUTHORS:
Kasho Yamamoto, Masayuki Ikebe, Tetsuya Asai, Masato Motomura
KEYWORDS:
Data Mining, Frequent Itemset Mining, FPGA, Stream Processing
JOURNAL NAME:
Circuits and Systems,
Vol.7 No.10,
August
25,
2016
ABSTRACT: With the advent of the IoT
era, the amount of real-time data that is processed in data centers has
increased explosively. As a result, stream mining, extracting useful knowledge
from a huge amount of data in real time, is attracting more and more attention.
It is said, however, that real- time stream processing will become more
difficult in the near future, because the performance of processing applications
continues to increase at a rate of 10% - 15% each year, while the amount of
data to be processed is increasing exponentially. In this study, we focused on
identifying a promising stream mining algorithm, specifically a Frequent
Itemset Mining (FIsM) algorithm, then we improved its performance using an
FPGA. FIsM algorithms are important and are basic data- mining techniques used
to discover association rules from transactional databases. We improved on an
approximate FIsM algorithm proposed recently so that it would fit onto hardware
architecture efficiently. We then ran experiments on an FPGA. As a result, we
have been able to achieve a speed 400% faster than the original algorithm
implemented on a CPU. Moreover, our FPGA prototype showed a 20 times speed
improvement compared to the CPU version.