GPU-based similarity metrics computation and machine learning approaches for string similarity evaluation in large datasets

• Published in: Soft computing (Berlin, Germany) Vol. 28; no. 4; pp. 3465 - 3477
• Main Authors: Baloi, Aurel, Belean, Bogdan, Turcu, Flaviu, Peptenatu, Daniel
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2024; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Application of Soft Computing; Artificial Intelligence; Artists; Bioinformatics; Biomedical data; Central processing units; Complexity; Computational Intelligence; Control; CPUs; Data analysis; Data processing; Data search; Datasets; Dynamic programming; Efficiency; Engineering; Field programmable gate arrays; Graphics processing units; Machine learning; Malware; Mathematical Logic and Foundations; Mechatronics; Microprocessors; Neural networks; Random access memory; Robotics; Scientometrics; Search engines; Securities markets; Semantics; Similarity; Software; String matching; Parallel computation; String similarity score; GPU; String matching; CUDA kernel
• ISSN: 1432-7643; 1433-7479; 1433-7479
• DOI: 10.1007/s00500-023-08687-8

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, biomedical data analysis and even software tools for defending against viruses, spyware, or spam. String similarities are also used 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 mass-media broadcast materials. The present paper proposes a novel GPU-based approach for parallel implementation of the Jaro–Winkler string similarity metric computation, broadly used for matching strings over large datasets. The proposed implementation is applied in musical industry for matching playlist with over 100k records with a given repertory which includes a collection of over 1 million right owner records. The global GPU RAM memory is used to store multiple string lines representing repertory records, whereas single playlist string comparisons with the raw data are performed using the maximum number of available GPU threads and the stride operations. Further on, the accuracy of the Jaro–Winkler approach for the string matching procedure is increased using both an adaptive neural network approach guided by a novelty detection classifier (aNN) and a multiple-features neural network implementation (MF-NN). Thus, the aNN approach yielded an accuracy of 92% while the MF-NN approach achieved an accuracy of 99% at the cost of increased computational complexity. Timing considerations and the computational complexity are detailed for the proposed approaches compared with both the general-purpose processor (CPU) implementation and the state-of-the-art GPU approaches. A speed-up factor of 21.6 was obtained for the GPU-based Jaro–Winkler implementation compared with the CPU one, whereas a factor of 3.72 was obtained compared with the existing GPU implementation of string matching procedure based on Levenstein distance metrics.