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Efficient top-k similarity document search utilizing distributed file systems and cosine similarity

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Abstract

Document similarity has important real life applications such as finding duplicate web sites and identifying plagiarism. While the basic techniques such as k-similarity algorithms have been long known, overwhelming amount of data, being collected such as in big data setting, calls for novel algorithms to find highly similar documents in reasonably short amount of time. In particular, pairwise comparison of documents’ features, a key operation in calculating document similarity, necessitates prohibitively high storage and computation power. In this paper, we propose a new filtering technique that decreases the number of comparisons between the query set and the search set to find highly similar documents. The proposed filtering technique utilizes Z-order prefix, based on the cosine similarity measure, in which only the most important features are used first to find highly similar documents. We propose a three-phase approach, where the phases are near duplicate detection, common important terms and join phase. We utilize the Hadoop distributed file system and the MapReduce parallel programming model to scale our techniques to big data setting. Our experimental results on real data show that the proposed method performs better than the previous work in the literature in terms of the number of joins, and therefore, speed.

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Acknowledgments

This project is supported by TUBITAK under Grant Number 113E537.

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Authors and Affiliations

  1. Faculty of Science and Engineering, Sabanci University, Istanbul, Turkey

    Mahmoud Alewiwi, Cengiz Orencik & Erkay Savaş

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  1. Mahmoud Alewiwi
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  2. Cengiz Orencik
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  3. Erkay Savaş
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Correspondence to Cengiz Orencik.

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Alewiwi, M., Orencik, C. & Savaş, E. Efficient top-k similarity document search utilizing distributed file systems and cosine similarity. Cluster Comput 19, 109–126 (2016). https://doi.org/10.1007/s10586-015-0506-0

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