日本語版README

This is a multi-dimensional learned index that processes the following queries efficiently.

• Enumerate data points inside a hyperrectangle parallel to the axes
• Add/Delete data points

We used gnu++17 and python3 for the internal implementation.
We also used scikit-learn.

The sample code is main.cpp.
First, generate a sample dataset using the following command.

./data_generator.sh 3 100000 1000 2000000 ./data/data.csv ./data/sample_query.csv ./data/query.csv

After that, main.cpp can be executed by the following command.

./running.sh main.cpp ./data/data.csv ./data/sample_query.csv ./data/query.csv

The following is a detailed document.
Sections marked with 💻 are for those who understand the internal algorithm.
We designed it so that even if the sections marked with 💻 were ignored, it would not be a problem to use it as a black box.

(1) FlexFlood<typename Type, int dimension> index(std::vector<std::array<Type, dimension>>::iterator begin, std::vector<std::array<Type, dimension>>::iterator end, std::vector<std::array<Type, dimension>> sample_lowers, std::vector<std::array<Type, dimension>> sample_uppers, int num_sampling = 30 * dimension, double learning_time = 60.0 * dimension)
(2 💻) FlexFlood<typename Type, int dimension> index(std::vector<std::array<Type, dimension>>::iterator begin, std::vector<std::array<Type, dimension>>::iterator end, std::array<int, dimension> partitioning)

• Constructs a FlexFlood index from dimension-dimensional data of type Type between begin and end.
• (1) Optimizes the internal structure based on sample queries. Provide the coordinates of the lower corners of the hyperrectangles in sample_lowers and the upper corners in sample_uppers.
• (1) 💻 The number of samples for learning the distribution can be specified with num_sampling, and the learning time with learning_time.
• (2) Initializes based on the number of partitions for each axis specified in partitioning. Note that at least one value in partitioning must be $1$ for the internal algorithm to function properly.

Constraints

• (1) sample_lowers and sample_uppers must have the same length.
• (2) At least one value in partitioning must be $1$.
• (2) Each value in partitioning must be positive.

void index.clear()

Deletes all elements in index.

size_t index.size()

Returns the number of elements in index.

std::vector<std::array<Type, dimension>> enumerate(std::array<Type, dimension> lower, std::array<Type, dimension> upper)

Returns all data points within index that are inside the hyperrectangle defined by the lower and upper corners.

Constraints

• For each dimension, the value of lower must be less than or equal to the value of upper (behavior is undefined if this condition is not met).

void insert(std::array<Type, dimension> dat, std::pair<int, int> lower = std::make_pair(3, 1), std::pair<int, int> upper = std::make_pair(1, 2))

• Adds dat to index.
• If index already contains dat, no action is taken, and size() remains unchanged.
• 💻 lower and upper specify thresholds for internal processing of split and merge/equalize.
• 💻 lower = std::make_pair(a, b) means that the threshold coefficient for split is $\frac{b}{a}$.
• 💻 upper = std::make_pair(a, b) means that the threshold coefficient for merge/equalize is $\frac{b}{a}$.

void erase(std::array<Type, dimension> dat, std::pair<int, int> lower = std::make_pair(3, 1), std::pair<int, int> upper = std::make_pair(1, 2))

• Deletes dat from index.
• If index does not contain dat, no action is taken, and size() remains unchanged.
• 💻 lower and upper specify thresholds for internal processing of split and merge/equalize.
• 💻 lower = std::make_pair(a, b) means that the threshold coefficient for split is $\frac{b}{a}$.
• 💻 upper = std::make_pair(a, b) means that the threshold coefficient for merge/equalize is $\frac{b}{a}$.