GPU Based Similarity Metrics Computation and Machine Learning Approaches for String Similarity Evaluation in Large Datasets

Abstract

The digital era brings up on one hand massive amounts of available data, and on the other hand the need of parallel computing architectures for efficient data processing. String similarity evaluation is a processing task applied on large data volumes, commonly performed by various applications such as search engines, bio-medical data analysis and even software tools for defending against viruses, spyware, or spam. String similarities are also evaluated in musical industry for matching playlist records with repertory records composed of song titles, performer artists and producers names, aiming to assure copyright protection of massmedia broadcast materials. Thus, the present paper proposes a GPU based approach for parallel implementation of the Jaro-Winkler string similarity metric computation. Further on, a thresholding-based algorithm is also implemented using GPU for matching records over large datasets. The global GPU RAM memory is used to store multiple string lines as raw data. In the case of a single string, its comparisons with the raw data are performed using the maximum number of available GPU threads and the stride operations. Moreover, based on the computed similarity metrics, an adaptive neural network approach guided by a novelty detection classifier together with a naive neural network implementation are proposed to increase the accuracy of the records matching procedure. Timing considerations and the computational complexity are detailed for the proposed approaches compared with state-of-the-art CPU and GPU approaches. A speed-up factor of 21.6 was obtained for the GPU based JaroWinkler implementation compared with the general purpose processor one, whereas improved accuracy for the records matching procedure was delivered using machine learning approaches.