M-tree

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M-trees are tree data structures that are similar to R-trees and B-trees. It is constructed using a metric and relies on the triangle inequality for efficient range and k-NN queries. While M-trees can perform well in many conditions, the tree can also have large overlap and there is no clear strategy on how to best avoid overlap. In addition, it can only be used for distance functions that satisfy the triangle inequality, while many advanced dissimilarity functions used in information retrieval do not satisfy this.[1]

Overview[edit]

2D M-Tree visualized using ELKI. Due to the axis scales, the spheres appear ellipsoidal. Every blue sphere (leaf) is contained in a red sphere (directory nodes). Leaves overlap, but not too much.

As in any Tree-based data structure, the M-Tree is composed of Nodes and Leaves. In each node there is a data object that identifies it uniquely and a pointer to a sub-tree where its children reside. Every leaf has several data objects. For each node there is a radius r that defines a Ball in the desired metric space. Thus, every node n and leaf l residing in a particular node N is at most distance r from N, and every node n and leaf l with node parent N keep the distance from it.

M-Tree construction[edit]

Components[edit]

An M-Tree has these components and sub-components:

  1. Non-leaf nodes
    1. A set of routing objects NRO.
    2. Pointer to Node's parent object Op.
  2. Leaf nodes
    1. A set of objects NO.
    2. Pointer to Node's parent object Op.
  3. Routing Object
    1. (Feature value of) routing object Or.
    2. Covering radius r(Or).
    3. Pointer to covering tree T(Or).
    4. Distance of Or from its parent object d(Or,P(Or))
  4. Object
    1. (Feature value of the) object Oj.
    2. Object identifier oid(Oj).
    3. Distance of Oj from its parent object d(Oj,P(Oj))

Insert[edit]

The main idea is first to find a leaf node N where the new object O belongs. If N is not full then just attach it to N. If N is full then invoke a method to split N. The algorithm is as follows:

Algorithm Insert
  Input: Node N  of M-Tree MT, Entry O_{n}
  Output: A new instance of MT containing all entries in original MT plus O_{n}
  N_{e}N's routing objects or objects
  if N is not a leaf then
  {
       /*Look for entries that the new object fits into*/
       let N_{in} be routing objects from N_{e}'s set of routing objects N_{RO} such that d(O_{r}, O_{n}) <= r(O_{r})
       if N_{in} is not empty then
       {
          /*If there are one or more entry, then look for an entry such that is closer to the new object*/
          O_{r}^{*} = \min_{O_{r}\in N_{in}} d(O_{r}, O_{n})
       }
       else
       {
          /*If there are no such entry, then look for an object with minimal distance from */ 
          /*its covering radius's edge to the new object*/
          O_{r}^{*} = \min_{O_{r}\in N_{in}} d(O_{r}, O_{n}) - r(O_{r})
          /*Upgrade the new radii of the entry*/
          r(O_{r}^{*}) = d(O_{r}^{*}, O_{n})
       }
       /*Continue inserting in the next level*/
       return insert(T(O_{r}^{*}), O_{n});
  else
  {
       /*If the node has capacity then just insert the new object*/
       if N is not full then
       {  store(N, O_{n})   }
       /*The node is at full capacity, then it is needed to do a new split in this level*/
       else
       {  split(N, O_{n}) }
  }
  • "←" is a shorthand for "changes to". For instance, "largestitem" means that the value of largest changes to the value of item.
  • "return" terminates the algorithm and outputs the value that follows.

Split[edit]

If the split method arrives to the root of the tree, then it choose two routing objects from N, and creates two new nodes containing all the objects in original N, and store them into the new root. If split methods arrives to a node N that is not the root of the tree, the method choose two new routing objects from N, re-arrange every routing object in N in two new nodes N_{1} and N_{2}, and store this new nodes in the parent node N_{p} of original N. The split must be repeated if N_{p} has not enough capacity to store N_{2}. The algorithm is as follow:

Algorithm Split
  Input: Node N  of M-Tree MT, Entry O_{n}
  Output: A new instance of MT containing a new partition.
  /*The new routing objects are now all those in the node plus the new routing object*/
  let be NN entries of N \cup O
  if N is not the root then
  {
     /*Get the parent node and the parent routing object*/
     let O_{p} be the parent routing object of N
     let N_{p} be the parent node of N
  }
  /*This node will contain part of the objects of the node to be split*/
  Create a new node N'
  /*Promote two routing objects from the node to be split, to be new routing objects*/
  Create new objects O_{p1} and O_{p2}.
  Promote(N, O_{p1}, O_{p2})
  /*Choose which objects from the node being split will act as new routing objects*/
  Partition(N, O_{p1}, O_{p2}, N_{1}, N_{2})
  /*Store entries in each new routing object*/
  Store N_{1}'s entries in N and N_{2}'s entries in N'
  if N is the current root then
  {
      /*Create a new node and set it as new root and store the new routing objects*/
      Create a new root node N_{p}
      Store O_{p1} and O_{p2} in N_{p}
  }
  else
  {
      /*Now use the parent rouing object to store one of the new objects*/
      Replace entry O_{p} with entry O_{p1} in N_{p}
      if N_{p} is no full then
      {
          /*The second routinb object is stored in the parent only if it has free capacity*/
          Store O_{p2} in N_{p}
      }
      else
      {
           /*If there is no free capacity then split the level up*/
           split(N_{p}, O_{p2})
      }
  }
  • "←" is a shorthand for "changes to". For instance, "largestitem" means that the value of largest changes to the value of item.
  • "return" terminates the algorithm and outputs the value that follows.

M-Tree Queries[edit]

Range Query[edit]

A range query is where a minimum similarity/maximum distance value is specified. For a given query object Q ∈ D and a maximum search distance r(Q), the range query range(Q, r(Q)) selects all the indexed objects Oj such that d(Oj, Q) ≤ r(Q).[2]

Algorithm RangeSearch starts from the root node and recursively traverses all the paths which cannot be excluded from leading to qualifying objects.

Algorithm Insert

Input: Node N of M-Tree MT, Q: query object, r(Q): search radius

Output: all the DB objects such that d(Oj, Q)r(Q)

{ let O_{p} be the parent object of node N;

if N is not a leaf then { for each entry(O_{r}) in N do:

          if | d(O_{p}, Q)d(O_{r}, O_{p}) | ≤ r(Q) +r(O_{r})
          then { Compute d(O_{r}, Q);
                      if d(O_{r}, Q)r(Q) +r(O_{r})
                      then RangeSearch(*ptr(T(O_{r})),Q,r(Q)); }}

else { for each entry(O_{j}) in N do:

          if | d(O_{p}, Q)d(O_{j}, O_{p}) | ≤ r(Q)
          then { Compute d(O_{j}, Q);
                     if d(O_{j}, Q)r(Q)
                     then add oid(O_{j}) to the result; }}}
  • "←" is a shorthand for "changes to". For instance, "largestitem" means that the value of largest changes to the value of item.
  • "return" terminates the algorithm and outputs the value that follows.

oid(O_{j}) is the identifier of the object which resides on a separate data file.

T(O_{r}) is a sub-tree – the covering tree of O_{r}

k-NN queries[edit]

K Nearest Neighbor (k-NN) query takes the cardinality of the input set as an input parameter. For a given query object Q ∈ D and an integer k ≥ 1, the k-NN query NN(Q, k) selects the k indexed objects which have the shortest distance from Q, according to the distance function d. [2]

See also[edit]

References[edit]

  1. ^ Ciaccia, Paolo; Patella, Marco; Zezula, Pavel (1997). "M-tree An Efficient Access Method for Similarity Search in Metric Spaces". "Proceedings of the 23rd VLDB Conference Athens, Greece, 1997". IBM Almaden Research Center: Very Large Databases Endowment Inc. pp. 426–435. p426. Retrieved 2010-09-07. 
  2. ^ a b P. Ciaccia, M. Patella, F. Rabitti, P. Zezula. "Indexing Metric Spaces with M-tree". Department of Computer Science and Engineering. University of Bologna. p. 3. Retrieved 19 November 2013.