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Database System Concepts, 5th Ed
Database System Concepts, 5th Ed

ch12
ch12

Wavelet Trees Meet Suffix Trees
Wavelet Trees Meet Suffix Trees

... the bitmasks Bv , we do not remove the most significant bit of each character, and instead of partitioning the values based on this bit, we make a decision based on whether the leaf corresponding to the character lies in the left or in the right subtree of v. Our construction algorithm generalizes t ...
Automatic Memory Reclamation for Lock-Free Data
Automatic Memory Reclamation for Lock-Free Data

Document
Document

Dynamic 3-sided planar range queries with expected - delab-auth
Dynamic 3-sided planar range queries with expected - delab-auth

... hB-trees and various R-trees have been proposed. A recent survey can be found in [16]. Often these data structures are used in applications, because they are relatively simple, require linear space and perform well in practice most of the time. However, they all have highly sub-optimal worst case pe ...
Improving the efficiency of priority-queue structures
Improving the efficiency of priority-queue structures

Chapter 11: Indexing and Hashing
Chapter 11: Indexing and Hashing

... In both leaf and internal nodes,  we cannot guarantee that K1 < K2 < K3 < . . . < Kn–1  but can guarantee K1 ≤ K2 ≤ K3 ≤ . . . ≤ Kn–1 Search-keys in the subtree to which Pi points  are ≤ Ki,, but not necessarily < Ki,  To see why, suppose same search key value V is present in two leaf node Li an ...
Chapter 11: Indexing and Hashing
Chapter 11: Indexing and Hashing

Problem Description Earned Max 1 Inheritance/Polymorphism 2
Problem Description Earned Max 1 Inheritance/Polymorphism 2

Concise Notes on Data Structures and Algorithms
Concise Notes on Data Structures and Algorithms

Lock-free internal binary search trees with memory management
Lock-free internal binary search trees with memory management

... versions protecting the data structure via mutual exclusion. Unfortunately, non-blocking algorithms have their own set of associated problems. The complexity of their design means that even minor improvements to existing algorithms warrant publication as an academic paper accompanied with a detailed ...
Manual - Channabasaveshwara InStitute Of Technology
Manual - Channabasaveshwara InStitute Of Technology

lec6
lec6

Accelerating Online LCA with Functional Data Structures
Accelerating Online LCA with Functional Data Structures

Linked Lists
Linked Lists

Linked lists
Linked lists

...  Insert and remove can be expensive (a lot of copies) if we don’t want to leave holes in the middle of the array.  Continuous memory for easy index  Dynamic arrays  Limitations:  Capacity is dynamic, memory still may not be fully utilized, but better than static arrays.  Insert and remove can ...
A Fully Retroactive Priority Queues
A Fully Retroactive Priority Queues

... Naturally, we cannot hope to directly update this picture each time our priority queue changes and hope to retain efficient performance. A long cascade of this sort will incur a linear-cost update: much too expensive. Fortunately, in the partially retroactive case, we only need to be concerned with ...
Worksheet 33: Heaps and Priority Queues
Worksheet 33: Heaps and Priority Queues

Unit 7 Powerpoint Presentation
Unit 7 Powerpoint Presentation

... which elements may be deleted at one end called the “TOP” of stack. -- A stack is a last-in-first-out ( LIFO ) structure. -- Insertion operation is referred as “PUSH” and deletion operation is referred as “POP”. -- The most accessible element in the stack is the element at the position “TOP”. -- Sta ...
Scalable Address Spaces Using RCU Balanced Trees - PDOS-MIT
Scalable Address Spaces Using RCU Balanced Trees - PDOS-MIT

Scalable Address Spaces Using RCU Balanced Trees
Scalable Address Spaces Using RCU Balanced Trees

... with RCU, and none of the balanced trees in the Linux kernel use RCU. The B ONSAI tree introduced by this paper is one solution to applying RCU to complicated data structures. Trees with lock-free lookup. Fraser [6, 7] introduced a clever design for a red-black tree that allows read operations to pr ...
Chapter 11: Priority Queues and Heaps
Chapter 11: Priority Queues and Heaps

I n - Virginia Tech
I n - Virginia Tech

... • Are all data inserted into the data structure at the beginning, or are insertions interspersed with other operations? • Can data be deleted? • Are all data processed in some welldefined order, or is random access allowed? ...
File - MCA Galgotias University
File - MCA Galgotias University

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Red–black tree

A red–black tree is a binary search tree with an extra bit of data per node, its color, which can be either red or black. The extra bit of storage ensures an approximately balanced tree by constraining how nodes are colored from any path from the root to the leaf. Thus, it is a data structure which is a type of self-balancing binary search tree.Balance is preserved by painting each node of the tree with one of two colors (typically called 'red' and 'black') in a way that satisfies certain properties, which collectively constrain how unbalanced the tree can become in the worst case. When the tree is modified, the new tree is subsequently rearranged and repainted to restore the coloring properties. The properties are designed in such a way that this rearranging and recoloring can be performed efficiently.The balancing of the tree is not perfect but it is good enough to allow it to guarantee searching in O(log n) time, where n is the total number of elements in the tree. The insertion and deletion operations, along with the tree rearrangement and recoloring, are also performed in O(log n) time.Tracking the color of each node requires only 1 bit of information per node because there are only two colors. The tree does not contain any other data specific to its being a red–black tree so its memory footprint is almost identical to a classic (uncolored) binary search tree. In many cases the additional bit of information can be stored at no additional memory cost.
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