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Efficient Adaptive Collect using Randomization
Efficient Adaptive Collect using Randomization

binary heap - K.f.u.p.m ocw
binary heap - K.f.u.p.m ocw

Improved Bounds for Orthogonal Point Enclosure Query and Point
Improved Bounds for Orthogonal Point Enclosure Query and Point

... Table 1: Summary of our results for orthogonal point enclosure in R3 . log∗ n is the iterated logarithm of n. log(1) n = log n and log(i) n = log(log(i−1) n) when i > 1 is a constant integer. Existing solutions in the literature for 6-sided rectangles require Ω(n log n) space. comparison of our resu ...
Priority Queues 1. Priority Queues 2. PQueue Specification
Priority Queues 1. Priority Queues 2. PQueue Specification

... • dequeue: In this case, we must also delete the root node from the tree and then restore the heap property. This can be achieved by moving the final entry in the tree to the newly-vacated root position and then calling heapify(1) to restore the heap property. This involves a few constant time opera ...
Summarizing Large Query Logs in Ettu
Summarizing Large Query Logs in Ettu

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chapter15

Offset Addressing Approach to Memory
Offset Addressing Approach to Memory

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Multidimensional Access Methods
Multidimensional Access Methods

... multidimensional access methods to denote the large class of access methods that support searches in spatial databases and are the subject of this survey. Within this class, we distinguish between point access methods (PAMs) and spatial access methods (SAMs). Point access methods have primarily been ...
ROW 1, COL 1 - WordPress.com
ROW 1, COL 1 - WordPress.com

Linked Data Structures Linked lists
Linked Data Structures Linked lists

... • the root node has no parent, all others have exactly one • nodes can have multiple children • in a binary tree, each node has at most two children • a leaf node has no children • the height of a tree is the maximum possible number of nodes from the root to a leaf (inclusive) ...
A Framework for Dynamizing Succinct Data Structures
A Framework for Dynamizing Succinct Data Structures

... answer queries efficiently, perform updates in a reasonable amount of time, and still maintain a compressed version of the dynamically-changing data. In that vein, there have been some results on dynamic succinct bitvectors (dictionaries) [14, 8, 12]. However, these data structures either perform qu ...
Data Structures and Algorithms. Lab Guide
Data Structures and Algorithms. Lab Guide

... data types. An abstract data type is a programming language facility for organizing programs into modules using criteria that are based on the data structures of the program. Also, an abstract data type can be defines as a set of values and a set of procedures that manipulate those values. The speci ...
ADS@Unit-3[Priority Queues]
ADS@Unit-3[Priority Queues]

... ADS@Unit-3[Priority Queues] Delete Operation in Binomial tree: Step 1:First find the binomial tree with small root in the priority queue. Step 2:Let Bk be the binomial tree of binomial queue in priority queue remove Bk from H and forming another binomial queue H`. Step 3:Now remove the root of Bk , ...


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pdf

Data Structures and Analysis - Department of Computer Science
Data Structures and Analysis - Department of Computer Science

... while the computation in the if branch takes Ω(n2 ) time. This means that the worst-case running time of this algorithm is Ω(n2 ). It is not too hard to prove the matching upper bound, and so the worst-case running time is Θ(n2 ). However, the loop only executes when every number in lst is even; whe ...
When to use splay trees
When to use splay trees

Self-Organizing Data Structures
Self-Organizing Data Structures

... structures can be phrased as follows. The elements of a set are stored in a collection of nodes. Each node also contains O(1) pointers to other nodes and additional state data which can be used for navigation and self-organization. The elements have associated key values, which may or may not be tot ...
FUNDAMENTALS OF DATABASE SYSTEMS Course No. 1.963
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ch13hashing
ch13hashing

... – Guarantees O(log n) time for insert, retrieve, remove, and search – Large collection of methods ...
Dynamic Succinct Tries - King`s College London
Dynamic Succinct Tries - King`s College London

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Fully-Functional Succinct Trees
Fully-Functional Succinct Trees

Evaluation Techniques for Generalized Path Pattern Queries on
Evaluation Techniques for Generalized Path Pattern Queries on

... every node in the XML tree is encoded [15,39,4,9,23]. Further, the nodes are partitioned, and an index of inverted lists called streams is built on this partition. In order to evaluate a query, the nodes of the relevant streams are read in the preorder of their appearance in the XML tree. Every node ...
< 1 ... 9 10 11 12 13 14 15 16 17 ... 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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