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DOI: 10.14569/IJACSA.2020.0110483
PDF

DMTree: A Novel Indexing Method for Finding Similarities in Large Vector Sets

Author 1: Phuc Do
Author 2: Trung Phan Hong
Author 3: Huong Duong To

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 11 Issue 4, 2020.

  • Abstract and Keywords
  • How to Cite this Article
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Abstract: In a vector set, to find similarities we will compute distances from the querying vector to all other vectors. On a large vector set, computing too many distances as above takes a lot of time. So we need to find a way to compute less distance and the MTree structure is the technique we need. The MTree structure is a technique of indexing vector sets based on a defined distance. We can solve effectively the problems of finding similarities by using the MTree structure. However, the MTree structure is built on one computer so the indexing power is limited. Today, large vector sets, not fit in one computer, are more and more. The MTree structure failed to index these large vector sets. Therefore, in this work, we present a novel indexing method, extended from the MTree structure, that can index large vector sets. Besides, we also perform experiments to prove the performance of this novel method.

Keywords: MTree; DMTree; spark; distributed k-NN query; distributed range query

Phuc Do, Trung Phan Hong and Huong Duong To, “DMTree: A Novel Indexing Method for Finding Similarities in Large Vector Sets” International Journal of Advanced Computer Science and Applications(IJACSA), 11(4), 2020. http://dx.doi.org/10.14569/IJACSA.2020.0110483

@article{Do2020,
title = {DMTree: A Novel Indexing Method for Finding Similarities in Large Vector Sets},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2020.0110483},
url = {http://dx.doi.org/10.14569/IJACSA.2020.0110483},
year = {2020},
publisher = {The Science and Information Organization},
volume = {11},
number = {4},
author = {Phuc Do and Trung Phan Hong and Huong Duong To}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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