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DiskTrie: An Efficient Data Structure Using Flash Memory for Mobile
DiskTrie: An Efficient Data Structure Using Flash Memory for Mobile

... ways. First, every internal node now has the same degree, which is two. Second, no space is needed to remember the character corresponding to an outgoing edge. The obvious problem of using binary encoding is that each string will become longer and as a result search will take longer time. The soluti ...
Semi-Indexing Semi-Structured Data in Tiny Space
Semi-Indexing Semi-Structured Data in Tiny Space

Linked Lists - Computer Science@IUPUI
Linked Lists - Computer Science@IUPUI

... Linear collection of self-referential class objects, called nodes Connected by pointer links Accessed via a pointer to the first node of the list Link pointer in the last node is set to null to mark the list’s end ...
The R+-Tree: A Dynamic Index for Multi
The R+-Tree: A Dynamic Index for Multi

... that has been studied in the past is the case of points. The main idea is to divide the whole space into disjoint sub-regions, usually in such a way that each sub-region contains no more than C points. C is usually 1 if the data is stored in core, or it is the capacity of a disk page, that is the nu ...
List
List

... The linked list uses dynamic memory allocation, that is, it allocates memory for new list elements as needed. A linked list is made up of a series of objects, called the nodes of the list. List node is a distinct object (as opposed to simply a cell in an array). Every node contains the data item and ...
Sorting I
Sorting I

CMSC 420: Data Structures
CMSC 420: Data Structures

... points in the left subtree is equal to the number of points in the right subtree for each node. Generalize this argument to show that in dimension 3, an orthogonal plane (one that is orthogonal either to the x-, y-, or z-axis) intersects O(n2/3 ) leaf cells of any balanced kd-tree having n total poi ...
TECH REPORT
TECH REPORT

... take O(log log n) time and Store operations take O(log log n) amortized expected time. The algorithm uses linear space. Because any data structure can be implemented with an array, we can use the techniques described in this paper to make any data structure fully persistent at a cost of an extra fac ...
Compressed Data Structures for Range Searching
Compressed Data Structures for Range Searching

... in T (v) are contained in the range of v, so for every vertex u ∈ T (v), ru ⊆ rv . Leafs may store either points or ranges, and each point or range may be stored in several leafs. The data structure supports range queries that produce their result after evaluating the tree through a (partial) traver ...
Deletion without Rebalancing in Non
Deletion without Rebalancing in Non

... cases, while 22 cases have to be considered for the non-blocking chromatic tree. We prove the linearizability and progress property of a non-blocking ravl tree, and bound its height. The theoretical results of our research are summarized in the following theorem: I Theorem 1. The non-blocking ravl t ...
Data Structures for Dynamic Sets Operations on Dynamic Sets
Data Structures for Dynamic Sets Operations on Dynamic Sets

... BST Traversals ...
Trees
Trees

12 Abstract Data Types
12 Abstract Data Types

... At the ADT level, we use the queue and its four operations at the implementation level. We need to choose a data structure to implement it. A queue ADT can be implemented using either an array or a linked list. Figure 12.13 on page 329 shows an example of a queue ADT with five items. The figure also ...
optimizing algorithm using breadth first search manner
optimizing algorithm using breadth first search manner

On Hardness of Several String Indexing Problems
On Hardness of Several String Indexing Problems

... query processing, where p1 = |P1 | and p2 = |P2 | and k is the output size1 . Later Cohen and Porat [7] presented a space efficient structure of O(n log n)√ space, but with a higher query time of O(p1 + p2 + nk log n log2 n). The space and the query √ time of was improved by Hon et al. [11] to O(n) ...
PPT - UNSW
PPT - UNSW

... Begin by testing for a set of base cases (there should be at least one). Every possible chain of recursive calls must eventually reach a base case, and the handling of each base case should not use recursion. ...
PPT - UNSW
PPT - UNSW

BST_Hash
BST_Hash

... – Value of key field given to hash function – Location in a hash table is calculated ...
question-paper-Data-Structures-with-C-06cs35-10cs35
question-paper-Data-Structures-with-C-06cs35-10cs35

... (6marks) b. Write an algorithm for converting an infix expression to postfix. Trace the algorithm indicating the contents of stack for the expression ((A-(B+C))*(D$(E+F) (10 marks) c. Convert infix to postfix and prefix ((a-(b+c))*d$(e+f) (4 marks) ...
Lecture 5 (linked lists and vectors)
Lecture 5 (linked lists and vectors)

... We compare the incremental strategy and the doubling strategy by analyzing the total time T(n) needed to perform a series of n push operations We assume that we start with an empty stack represented by an array of size 1 We call amortized time of a push operation the average time taken by a push ove ...
Top-k Ranked Document Search in General Text Databases
Top-k Ranked Document Search in General Text Databases

Part I: 20 Multiple choice questions (2 points each)
Part I: 20 Multiple choice questions (2 points each)

5 Binary Trees
5 Binary Trees

... controlling which members of the collection will be visited. For example, some tree “traversals” might in fact visit only some tree nodes, while avoiding processing of others. Exercise 5.20 must solve exactly this problem in the context of a binary search tree. It must visit only those children of a ...
Engineering the LOUDS Succinct Tree Representation*
Engineering the LOUDS Succinct Tree Representation*

Chapter 7 Data Structures for Strings
Chapter 7 Data Structures for Strings

... a constant time operation, then running time is O(|s|). If we consider this deletion to take O(N) time, then the running time is O(|s| + N). An important di erence between Patricia trees and tries is that Patricia trees contain no nodes with only one child. Every node is either a leaf or has at leas ...
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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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