Code Monkey home page Code Monkey logo

psams's Introduction

Introduction

This repo holds the source code and scripts for our paper: Pivot Selection Algorithms in Metric Spaces: An Experimental Evaluation,To appear in The VLDB Journal (VLDBJ), 2021.

Details can be refferred to https://github.com/ZJU-DBL/PSAMS

Usage

  1. Datasets and Pivots

The used datasets has the following files: *.txt *_qid.txt *_query.txt

The first file contains all the data, and the first line for datasets except 'words' denotes the size, the dimention and the distance metric (taking 1 as an example, it means L-1 Norm Distance).

The second and the third file contains the ids and the values for the quries.

  1. Index

After asking the authors for the codes of all the three indexes, we only get the execute file.

The file 'indexes' contains the execute files for LAESA (LAESA_Float.exe, LAESA_String.exe), MVP tree (MVP_Tree_Float.exe, MVP_Tree_String.exe) and SPB (SPB_Tree_Float.exe, SPB_Tree_String.exe) tree. In the sub folder, 'input.txt' is the input file for all the exe files. The sample input is as follow:

sample.txt
sample_qid.txt
sample_query.txt
pivot/pivotsample.txt 5  
cost_sample.txt
1 5 10 20 50 100
2 3 4 5 6 8 10
5

The first three lines denotes the path of datasets and queries as explained before. The 4th line contains path of the tested pivot and the corresponding pivot number. The 5th line indicates the output file. The 6th and 7th line are 6 and 7 numbers seperately, which denotes the k for k-nearest neighbour query and r for range query. The last line denotes th size of the query.

After filling 'fil.in', simply runs the exe file and the output file will be generated to the given path.

Compared algorithms

Algorithm Paper Year
SSS Spatial Selection of Sparse Pivots for Similarity Search in Metric Spaces 2007
FFT A Best Possible Heuristic for the k-Center Problem 1985
BPP A comparison of pivot selection techniques for permutation-based indexing 2015
BPS A new version of the nearestneighbour approximating and eliminating search algorithm (AESA) with linear preprocessing time and memory requirements 1994
HF The Omni-family of all-purpose access methods: a simple and effective way to make similarity search more efficient 2007
MV Reference-based indexing for metric spaces with costly distance measures 2008
SC Selecting vantage objects for similarity indexing 2006
PCA Pivot selection: Dimension reduction for distance-based indexing 2012
IS Pivot selection techniques for proximity searching in metric spaces 2003
DSSS A Dynamic Pivot Selection Technique for Similarity Search 2008
HFI Efficient metric indexing for similarity search 2015

Datasets

Each dataset can be obtained from the following links.

Dataset link Dimension
LA http://www.dbs.informatik.uni-muenchen.de/ 2
Words http://icon.shef.ac.uk/Moby/ 1~34
Color http://cophir.isti.cnr.it/ 282
INT 20

The dataset INT and examples for each dataset are included in the folder dataset.

The specific k for k-nearest neighbour query and r for range query are as belows:

Dataset Range for 1%, 2%, 4%, 8%, 16%, 32%, 64%
LA 473, 692, 989, 1409, 1875, 2314, 3096
Integer 2321,2733, 3229,3843, 4614, 5613, 7090
Color 3838, 4092, 4399, 4773, 5241, 5904, 7104
Words 2, 3, 4, 5, 6, 8, 10

Citation

If you use our code for research work, please cite our paper as below:

@article{,
  title={Pivot Selection Algorithms in Metric Spaces: An Experimental Evaluation},
  author={Yifan Zhu, Lu Chen, Yunjun Gao, Christian S. Jensen},
  journal={VLDBJ}
  year={2021},
}

psams's People

Stargazers

 avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar  avatar

Watchers

 avatar  avatar

Forkers

zhuyifan531

psams's Issues

Cannot compile HFI

I cannot compile the HFI code because the objvector.h file is missing. Can you please upload it?

Recommend Projects

  • React photo React

    A declarative, efficient, and flexible JavaScript library for building user interfaces.

  • Vue.js photo Vue.js

    ๐Ÿ–– Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.

  • Typescript photo Typescript

    TypeScript is a superset of JavaScript that compiles to clean JavaScript output.

  • TensorFlow photo TensorFlow

    An Open Source Machine Learning Framework for Everyone

  • Django photo Django

    The Web framework for perfectionists with deadlines.

  • D3 photo D3

    Bring data to life with SVG, Canvas and HTML. ๐Ÿ“Š๐Ÿ“ˆ๐ŸŽ‰

Recommend Topics

  • javascript

    JavaScript (JS) is a lightweight interpreted programming language with first-class functions.

  • web

    Some thing interesting about web. New door for the world.

  • server

    A server is a program made to process requests and deliver data to clients.

  • Machine learning

    Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.

  • Game

    Some thing interesting about game, make everyone happy.

Recommend Org

  • Facebook photo Facebook

    We are working to build community through open source technology. NB: members must have two-factor auth.

  • Microsoft photo Microsoft

    Open source projects and samples from Microsoft.

  • Google photo Google

    Google โค๏ธ Open Source for everyone.

  • D3 photo D3

    Data-Driven Documents codes.