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Fundamental Data Structures
Fundamental Data Structures

... system level (our lowest abstraction level) a data item is located by a byte offset within the storage media. At the moment, we do not know how to find the operating system level addresses of the phone numbers within that record, but we will find a solution later in this chapter. We only know that t ...
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... ArrayList automatically allocates a larger backing (static) array when the capacity of its current backing array is exceeded. ...
Lecture 18 - UMass CS !EdLab
Lecture 18 - UMass CS !EdLab

Balanced Tree
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On (Dynamic) Range Minimum Queries in External Memory
On (Dynamic) Range Minimum Queries in External Memory

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Lecture 15 - Computer Science
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... where Process is a template), and the forms and number of parameters for f are determined by the actual call of f inside the template function X The real function argument for f when calling the the template function X cannot be a template function, it must be instantiated in advance or right in the ...
Data Structures and Other Objects Using C++
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... where Process is a template), and the forms and number of parameters for f are determined by the actual call of f inside the template function X The real function argument for f when calling the the template function X cannot be a template function, it must be instantiated in advance or right in the ...
Data Structures and Other Objects Using C++
Data Structures and Other Objects Using C++

ppt - Dr. Wissam Fawaz
ppt - Dr. Wissam Fawaz

... Introduction ...
< 1 ... 43 44 45 46 47 48 49 50 51 ... 76 >

Interval tree

In computer science, an interval tree is a tree data structure to hold intervals. Specifically, it allows one to efficiently find all intervals that overlap with any given interval or point. It is often used for windowing queries, for instance, to find all roads on a computerized map inside a rectangular viewport, or to find all visible elements inside a three-dimensional scene. A similar data structure is the segment tree.The trivial solution is to visit each interval and test whether it intersects the given point or interval, which requires O(n) time, where n is the number of intervals in the collection. Since a query may return all intervals, for example if the query is a large interval intersecting all intervals in the collection, this is asymptotically optimal; however, we can do better by considering output-sensitive algorithms, where the runtime is expressed in terms of m, the number of intervals produced by the query. Interval trees have a query time of O(log n + m) and an initial creation time of O(n log n), while limiting memory consumption to O(n). After creation, interval trees may be dynamic, allowing efficient insertion and deletion of an interval in O(log n). If the endpoints of intervals are within a small integer range (e.g., in the range [1,...,O(n)]), faster data structures exist with preprocessing time O(n) and query time O(1+m) for reporting m intervals containing a given query point.
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