SharpTrees 1.0.1

SharpTrees

Implementation of tree data structures for C#

RTree

R-trees are tree data structures used for indexing multi-dimensional information. This project presents R-tree implementation with support of an arbitrary number of dimensions. The implementation is based on the article [1]. There is a R-tree modification, so called R-star tree, which allows to produce more optimal bounding rectangles [2]. It utilizes re-insertion approach when new item is added. However, this strategy is not implemented yet.

[1] A. Guttman. R-Trees: A dynamic index structure for spatial searching. ACM SIGMOD Record. Vol. 14, Issue 2, 1984. pp 47–57. https://doi.org/10.1145/971697.602266

[2] N. Beckmann, et al. The R*-tree: An efficient and Robust Access Method for Points and Rectangles. Proceedings of the 1990 ACM SIGMOD international conference on Management of dataMay. 1990. pp 322–331. https://doi.org/10.1145/93597.98741

How to use

R-tree is represented by generic class RTree<T>. T is a type of data that is stored in the R-tree. It must implement IBounded interface, which exposes two methods:

1. Bounds[] GetBounds() - returns bounds of the object.
2. bool IsEqual(IBounded other) - method to check equality of the objects.

Bounds is a structure that sets lower and upper bounds in a signle dimension (direction). The number of Bounds object returned by GetBounds() method sets the number of data dimensions.

Let's we need to store and retriewe 2D points. It has two coordinates (X and Y) and tolerance e.g. delta=0.01. If coordinates of two points differ less than delta, these points are considered as equal.

using SharpTrees;

class Point : IBounded {
    public double X { get; }
    public double Y { get; }
    private double const delta = 0.01;
    
    Point(double x, double y) {
        X = x;
        Y = y;
    }
    
    // IBounded interface implementation
    public Bounds[] GetBounds() {
        Bounds[] result = new Bounds[2];
        result[0] = new Bounds(X - delta, X + delta);
        result[1] = new Bounds(Y - delta, Y + delta);
        return result;
    }
    
    public bool IsEqual(IBounded other) {
        bool result = false;
        Point otherPoint = other as Point;
        if (otherPoint != null) {
            result = Math.Abs(X - otherPoint.X) < H && Math.Abs(Y - otherPoint.Y) < H;
        }
        return result;
    }
}

Now we can create RTree instance:

byte M = 4;
byte m = 2;

RTree<Point> rtree = new RTree<Point>(M, m, NodeSplitStrategy.Exhaustive);

The first parameter is the maximum number of entries that node can possess. The second parameter is the minimum number of entries. There are two constraints on these parameters:

1. m >= 2;
2. M >= 2 * m.

If these constraints are not respected, IncorrectLimitsOfEntriesException will be thrown. The third parameter is node splitting strategy. For now only Exhaustive strategy [1] is implemented. Therefore it is better to set small values of M, since complexity of exhaustive strategy growth exponentially.

Now we can add new items to the tree, search, delete.

bool result = rtree.Add(new Point(2, 3)); // True, if new item was added. False, if not - such
                                          // item already exists.
rtree.Add(new Point(4, 5));

bool del_result = rtree.Delete(new Point(2, 3), out Point deletedPoint); // True, if the point was 
               // deleted; false, if rtree does not contain such point. 

bool ser_result = rtree.Search(new Point(4, 5), out Point found);

Bounds[] bounds = new Bounds[] { new Bounds(3, 5), new Bounds(4, 6) };
ICollection<Point> points = rtree.SearchIntersections(Bounds[] bounds); // Finds all points that
              // have bounding rectangles intersecting specified bounds.