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Strict Fibonacci Heaps
Strict Fibonacci Heaps

On the Fast Construction of Spatial Hierarchies for Ray Tracing
On the Fast Construction of Spatial Hierarchies for Ray Tracing

... the DS very efficiently from scratch for every frame of the animation. Such a solution, if it exists, avoids storing auxiliary data in the nodes of the tree to be updated and guarantees good performance for every frame in the animation. Clearly, it can also be used for static scenes to decrease the ...
Introduction into XML
Introduction into XML

Web Page Cleaning for Web Mining through Feature Weighting
Web Page Cleaning for Web Mining through Feature Weighting

... blocks; and (2) the system knows a priori which blocks are the same blocks in different Web pages. As we will see, partitioning a Web page and identifying corresponding blocks in different pages are actually two critical problems in Web page cleaning. Our system is able to perform these tasks automa ...
Optimal dynamic vertical ray shooting in rectilinear planar subdivisions
Optimal dynamic vertical ray shooting in rectilinear planar subdivisions

linked lists
linked lists

... integer value, the link forward to the next node, and the link backward to the previous node A technique known as XOR-linking allows a doubly linked list to be implemented using a single link field in each node. Multiply linked list In a multiply linked list, each node contains two or more link fiel ...
Enhancing the B+-tree by Dynamic Node Popularity Caching
Enhancing the B+-tree by Dynamic Node Popularity Caching

notes
notes

Automatic verification of parameterized data structures *
Automatic verification of parameterized data structures *

Text Processing in Linux A Tutorial for CSE 562/662 (NLP)
Text Processing in Linux A Tutorial for CSE 562/662 (NLP)

Lock-Free Red-Black Trees Using CAS
Lock-Free Red-Black Trees Using CAS

... If there is contention, all but one concurrent process accessing the shared data must “roll-back” and redo its computation using the updated data. Optimistic techniques can be divided into “lock-free” and, the stronger, “wait-free” techniques. Our focus is on lock-free techniques. Concurrent program ...
Heaps - CENG METU
Heaps - CENG METU

... uses an extra array for the items exiting the heap. • We can avoid this problem as follows: – After each deleteMin, the heap shrinks by 1. – Thus the cell that was last in the heap can be used to store the element that was just deleted. – Using this strategy, after the last deleteMin, the array will ...
The X-tree: An Index Structure for High
The X-tree: An Index Structure for High

Lecture Notes Data Structure Using *C* Sem-2nd Branch-ALL
Lecture Notes Data Structure Using *C* Sem-2nd Branch-ALL

... This is called a single-dimensional array. The array Size must be an integer constant greater than zero and type can be any valid C data type. For example, to declare a 10-element array called balance of type int, use this statement: int balance[10]; Now balance is avariable array which is sufficien ...
Non-blocking binary search trees
Non-blocking binary search trees

... not interfere with one another if they are on different parts of the tree. In contrast, the lock-based algorithms described in Section 2 require locking all nodes along a path from the root to a leaf (although not all simultaneously). To achieve a non-blocking implementation, we use a strategy simila ...
Chapters 8
Chapters 8

Heaps - COW :: Ceng
Heaps - COW :: Ceng

Indexing Mixed Types for Approximate Retrieval
Indexing Mixed Types for Approximate Retrieval

SigMatch*Fast and Scalable Multi
SigMatch*Fast and Scalable Multi

... obtain k values.  The bit positions corresponding to these values are then set to 1 in the array.  To ascertain whether an element belongs to a set, the same set of k hash functions are applied to the element to obtain k values. ...
ppt
ppt

... How do we look up a query term qi in this array at query time? That is: which data structure do we use to locate the entry (row) in the array where qi is stored? ...
The Skip Quadtree: A Simple Dynamic Data Structure for
The Skip Quadtree: A Simple Dynamic Data Structure for

... • balanced box decomposition (BBD) trees [4–6]: regions are defined by hypercubes with smaller hypercubes subtracted away, so that the height of the decomposition tree is O(log n). These regions have good aspect ratios, that is, they are “fat” [17, 18], but they are not convex, which limits some of ...
Data Structure - knowledgebounce
Data Structure - knowledgebounce

... Discuss the advantages & disadvantages of circular linked list. Circular lists have certain advantages over singly linked list. The first advantage is that in a circular list every node is accessible from a given node. A second advantage is concerned with the deletion operation. In order to delete a ...
Priority Queues
Priority Queues

... In order to implement Put(x, p), we must find the correct place to insert x so that the order of the priorities is maintained. Let us therefore reduce the Put operation to the problem of finding the correct location to insert a given priority p. This location is the index i, 0 ≤ i ≤ size, such that ...
course handout - Cambridge Computer Laboratory
course handout - Cambridge Computer Laboratory

... This course, a continuation of what we did in Algorithms I last year, is about some of the coolest stuff a programmer can do. Most real-world programming is conceptually pretty simple. The undeniable difficulties come primarily from size: enormous systems with millions of lines of code and complex A ...
First
First

< 1 ... 13 14 15 16 17 18 19 20 21 ... 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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