Parallelized Sequential Indexing Tables for Fast High-Volume Data Processing

摘要

In the age of Big Data, processing high volumes of data as fast as possible is an important task. Parallel computing is a useful tool that has become more and more available and widespread in the past decade, making it possible to accelerate traditional data processing methods or create new ones that are built upon parallelization techniques. Sequential Fuzzy Indexing Tables are classifiers that expand the capabilities of Lookup Tables in order to achieve a fast classification. However, due to the size of their structure, they cannot be used for problems with larger complexity. In this paper, a new classifier architecture is proposed that uses parallelization techniques to be used in quick processing of large volumes of data.