A parallel computational approach for similarity search using Bloom filters. (25th March 2018)

• Record Type: Journal Article
• Authors: Chauhan, Sachendra Singh; Batra, Shalini
• Is Part Of: Computational intelligence. Volume 34:Number 2(2018)
• Page Start: 713
• Page End: 733
• Publication Date: 2018-03-25
• Subjects: Bloom filter -- CUDA -- GPU -- parallel similarity search; Artificial intelligence -- Periodicals; Computational linguistics -- Periodicals; 006.3
• DOI: 10.1111/coin.12172
• Languages: English
• ISSNs: 0824-7935

Abstract

Abstract: Finding similar items in a large and unstructured dataset is a challenging task in many applications of data science, such as searching, indexing, and retrieval. With the increasing data volume and demand for real time responses, similarity search has gained much consideration. In this paper, a parallel computational approach for similarity search using Bloom filters (PCASSB) has been proposed, which uses Bloom filter for the representation of features of document and comparison with user's query. Query features are stored in integer query array ( I Q A ), an array of integer. The PCASSB, an approximate similarity search technique, has been implemented on graphics processing unit with compute unified device architecture as the programming platform. To compute the similarity score between query and reference dataset, Dice coefficient has been used as a baseline method. The accuracy of the results generated by PCASSB is compared with the baseline method and other state‐of‐the‐art methods. The experimental results show that the proposed technique is quite effective in processing large number of text documents as it takes less computational time.