Crate k2_tree

A collection designed to efficiently compress sparsely-populated bit-matrices.

See the original proposal here.

Note: This library heavily relies upon bitvec to optimally store its data. If you have k2_tree as a dependancy, always try to compile with optimisations! bit_vec is very slow without them!

What’s new in version 0.5:

• K2Tree now implements serde’s Serialize and Deserialize traits.

When K2Trees are Useful:

K2Trees are useful when you need to store two-dimensional data efficiently, especially when the data is sparsely populated.

A real world example would be representing Web-Graphs. In this scenario, each column and row of a bit-matrix would represent a specific webpage, and all bits represent the whether two pages are joined by a hyperlink; 1 if yes and 0 if no. As it turns out, these types of Web-Graphs tend to produce sparsely populated bit-matrices.

Another example would be representing Triple-Stores, which this repo demonstrates is effective.

How it Works:

Original Bit-Matrix:

00|00||10|10
00|00||00|11
------------
00|00||00|00
00|00||00|10
============
10|10||00|11
10|00||00|00
------------
00|00||00|00
00|00||00|00

As shown above, the 8x8 bit-matrix is sub-divided into sub-matrices where:

• The smallest is width k
• All others are k * children_width

Modified Matrix

Then, all sub-matrices containing only zeroes are substituted by a single zero, like so:

0    ||10|10
     ||00|11
     ||-----
     ||0 |00
     ||  |10
============
10|10||0 |11
10|00||  |00
------------
0 |0 ||0 |0 
  |  ||  |  

K2Tree Representation of Modified Matrix

And then the K2Tree is built from this modified matrix:

               0111
          ______|||________
          |     |         |
          1101  1100      0100
|----|----|     |----|    |
1000 1011 0010  1010 1000 1100

From left-to-right in the first layer of the tree, each bit refers to one of the 4 largest quadrants in the modified matrix:

• 0111 => Upper-left empty, upper-right not empty, lower-left not empty, lower-right not empty.

Each block in the second layer refers to the sub-matrices of each parent:

• The upper-right quadrant (1101) contains the following sub-quadrants:
  • Lower-left is empty.
  • Upper-left, upper-right and lower-right are not empty.
• And so on.

The final, or leaf, layer of the tree contains the actual data in the matrix. For example, the upper-left sub-quadrant of the upper-right quadrant contains the bits: 1000.

Bit Representation of K2Tree

Finally, the above K2Tree is stored as a series of bits:

[0111; 1101, 1100, 0100; 1000, 1011, 0010, 1010, 1000, 1100]

(Where ; separates layers and , separates blocks)

Final K2Tree:

K2Tree {
  stem_k: 2, // usize
  leaf_k: 2, // usize
  max_slayers: 2, // usize
  stems: [0111110111000100], // BitVec
  leaves: [100010110010101010001100], // BitVec
}

– groels

Modules

error

Library error types. These are all the custom errors that this library could return.

matrix

BitMatrix struct.

tree

K2Tree structure and assosciated types.

Structs

K2Tree

A collection designed to efficiently compress sparsely-populated bit-matrices.