RSMI

How to use

1. Required libraries

LibTorch

homepage: https://pytorch.org/get-started/locally/

CPU version: https://download.pytorch.org/libtorch/cpu/libtorch-macos-1.4.0.zip

For GPU version, choose according to your setup.

boost

homepage: https://www.boost.org/

2. Change Makefile

Choose CPU or GPU

# TYPE = CPU
TYPE = GPU

Change *home/liuguanli/Documents/libtorch_gpu* to your own path.

ifeq ($(TYPE), GPU)
	INCLUDE = -I/home/liuguanli/Documents/libtorch_gpu/include -I/home/liuguanli/Documents/libtorch_gpu/include/torch/csrc/api/include
	LIB +=-L/home/liuguanli/Documents/libtorch_gpu/lib -ltorch -lc10 -lpthread
	FLAG = -Wl,-rpath=/home/liuguanli/Documents/libtorch_gpu/lib
else
	INCLUDE = -I/home/liuguanli/Documents/libtorch/include -I/home/liuguanli/Documents/libtorch/include/torch/csrc/api/include
	LIB +=-L/home/liuguanli/Documents/libtorch/lib -ltorch -lc10 -lpthread
	FLAG = -Wl,-rpath=/home/liuguanli/Documents/libtorch/lib
endif

3. Change Exp.cpp

comment #define use_gpu to use CPU version

#ifndef use_gpu
#define use_gpu
.
.
.
#endif  // use_gpu

4. Change path

Change the path if you do not want to store the datasets under the project's root path.

Constants.h

const string Constants::RECORDS = "./files/records/";
const string Constants::QUERYPROFILES = "./files/queryprofile/";
const string Constants::DATASETS = "./datasets/";

data_generator.py

if __name__ == '__main__':
    distribution, size, skewness, filename, dim = parser(sys.argv[1:])
    if distribution == 'uniform':
        filename = "datasets/uniform_%d_1_%d_.csv"
        getUniformPoints(size, filename, dim)
    elif distribution == 'normal':
        filename = "datasets/normal_%d_1_%d_.csv"
        getNormalPoints(size, filename, dim)
    elif distribution == 'skewed':
        filename = "datasets/skewed_%d_%d_%d_.csv"
        getSkewedPoints(size, skewness, filename, dim)

5. Prepare datasets

python data_generator.py -d uniform -s 1000000 -n 1 -f datasets/uniform_1000000_1_2_.csv -m 2

python data_generator.py -d normal -s 1000000 -n 1 -f datasets/normal_1000000_1_2_.csv -m 2

python data_generator.py -d skewed -s 1000000 -n 4 -f datasets/skewed_1000000_4_2_.csv -m 2

6. Run

make clean
make -f Makefile
./Exp -c 1000000 -d uniform -s 1
./Exp -c 1000000 -d normal -s 1
./Exp -c 1000000 -d skewed -s 4

Notions

model save. If you do not record the training time, you can use trained models and load them.

//RSMI.h
    std::ifstream fin(this->model_path);
    if (!fin)
    {
	net->train_model(locations, labels);
	torch::save(net, this->model_path);
    }
    else
    {
	torch::load(net, this->model_path);
    }

Paper

Jianzhong Qi, Guanli Liu, Christian S. Jensen, Lars Kulik: Effectively Learning Spatial Indices. Proc. VLDB Endow. 13(11): 2341-2354 (2020)

@article{DBLP:journals/pvldb/QiLJK20,
  author    = {Jianzhong Qi and
               Guanli Liu and
               Christian S. Jensen and
               Lars Kulik},
  title     = {Effectively Learning Spatial Indices},
  journal = {{PVLDB}}
  volume    = {13},
  number    = {11},
  pages     = {2341--2354},
  year      = {2020},
  url       = {http://www.vldb.org/pvldb/vol13/p2341-qi.pdf},
}