Orthogonal Range Searching-1Computational Geometry Prof. Dr. Th. Ottmann 1 Orthogonal Range Searching 1.Linear Range Search : 1-dim Range Trees 2.2-dimensional.

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Presentation transcript:

Orthogonal Range Searching-1Computational Geometry Prof. Dr. Th. Ottmann 1 Orthogonal Range Searching 1.Linear Range Search : 1-dim Range Trees 2.2-dimensional Range Search : kd-trees 3.2-dimensional Range Search : 2-dim Range Trees 4.Range Search in Higher Dimensions

Orthogonal Range Searching-1Computational Geometry Prof. Dr. Th. Ottmann 2 Range search Salary Children Age Input: Set of data points in d-space, orthogonal (iso-oriented) query range R Output: All points contained in R

Orthogonal Range Searching-1Computational Geometry Prof. Dr. Th. Ottmann 3 Binary search tree (1-dimensional)

Orthogonal Range Searching-1Computational Geometry Prof. Dr. Th. Ottmann 4 Binary leaf search tree

Orthogonal Range Searching-1Computational Geometry Prof. Dr. Th. Ottmann 5 Range search

Orthogonal Range Searching-1Computational Geometry Prof. Dr. Th. Ottmann 6 1-Dimensional range query Finding the split node for query range [x, x´] FindSplitNode (T, x, x´) v = root (T) while not leaf(v) && (x´  v.x || x > v.x) if (x´  v.x) then v = left(v) else v = right(v) return v Running time : O(log n) Note : Only O(log n) subtrees fall into the query range.

Orthogonal Range Searching-1Computational Geometry Prof. Dr. Th. Ottmann 7 Example – Binary Search Tree Split node Query range: [22, 77]

Orthogonal Range Searching-1Computational Geometry Prof. Dr. Th. Ottmann 8 Algorithm 1-d-range-search 1DRangeQuery (T, [x, x´]) v split = FindSplitNode (T, x, x´) if ( leaf (v split ) && v split in R=[x, x´]) then write v split ; return; v = left-child(v split ); while (not leaf (v)) if (x  v.x ) then write Subtree (right-child(v)); v= left-child(v); else v = right-child(v) if (v in R) write v ; v = right-child(v split )... rchild(v) s v p xx´

Orthogonal Range Searching-1Computational Geometry Prof. Dr. Th. Ottmann 9 Theorem A 1-dim range query in a set of n points can be answered in time O(log n + k) using a 1-d-range tree, where k is the number of reported points which fall into the given range. Proof : FindSplitNode: O(log n) Leaf search: O(log n) The number of green nodes is O(k), since number of internal nodes is O(k)  O((log n)+k) total time.

Orthogonal Range Searching-1Computational Geometry Prof. Dr. Th. Ottmann 10 Summary Let P be a set of n points in 1-dimensional space. The set P can be stored in a balanced binary search tree, which uses O(n) storage and has O(n log n) construction time, such that the points in a query range can be reported in time O(k + log n), where k is the number of reported points.