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CMSC132 Fall 2005 Midterm #2
CMSC132 Fall 2005 Midterm #2

Indexing Correlated Probabilistic Databases
Indexing Correlated Probabilistic Databases

... Each disk block is a serialized byte array with size BLOCK_SIZE Data is read and written in units of disk block Language for inserting new tuples and correlations into the probabilistic database ...
Persistent Data Structures (Version Control)
Persistent Data Structures (Version Control)

... O(n∙log n) preprocessing, O(log n) query ...
Starting Out with C++, 3 rd Edition
Starting Out with C++, 3 rd Edition

recursively
recursively

Lossless Compression of Binary Trees with Correlated Vertex Names
Lossless Compression of Binary Trees with Correlated Vertex Names

Chapter 17: Indexing Structures for Files and Indexing Structures for
Chapter 17: Indexing Structures for Files and Indexing Structures for

Augmenting Data Structures 1
Augmenting Data Structures 1

... • Knowing size of left subtree (number of nodes smaller than current) tells you which subtree the answer is in ...
Laboratory 6: Binary trees I. THEORETICAL ASPECTS
Laboratory 6: Binary trees I. THEORETICAL ASPECTS

... NOTE: Next we use some functions based on the global variable proot. Inserting a leaf node in a binary tree The function insnod inserts a node in the tree, according to the following steps: 1. It is allocated a memory area for the node to be inserted in the tree. Consider p being the pointer for th ...
dist
dist

... • O((m + n) logn) with binary heaps - found in Java's PriorityQueue • O(m + nlogn) with complex data structures o No advantage in practice over previous option o This is essentially optimal in terms of theoretical complexity ...
Data Structures and Algorithms(6)
Data Structures and Algorithms(6)

... • Level-order sequence of the binary tree with left link ...
Advanced Algorithm Design and Analysis (Lecture 1)
Advanced Algorithm Design and Analysis (Lecture 1)

Final Exam review
Final Exam review

data structure
data structure

... Q16. A structure having atleast one of its members of the same type as the structure itself: a. reference structure b. nested structure c. self-referential structure d. none of the above Q17. A one-way list is called: a. circular linked list b. array c. queue d. single linked list Q18. An orphaned b ...
Sample Final with solutions
Sample Final with solutions

Range Searching - CSE-IITM
Range Searching - CSE-IITM

... else Split P into two subsets with a horizontal line ` through the median ycoordinate of the points in P. Let P1 be the set of points below ` or on `, and let P2 be the set of points above `. ...
ppt part 1 - CS
ppt part 1 - CS

... • Case 1 is trivial; Case 2 is easy. • Case 3: If x doesn’t have a right child, then its successor is x’s first ancestor such that its left child is also an ancestor of x. (This includes the case that there is no such ancestor, and then x is the maximum and the successor is null.) • Proof: To prove ...
Interfaces Meeting Software Specifications
Interfaces Meeting Software Specifications

Trees
Trees

Algorithms and data structures—topic summary
Algorithms and data structures—topic summary

... are almost always locally defined: at the very least, either every node must specify its parent, or each node must store its children. 4.3 Tree traversals In stepping through all the entries in an array or linked list, one need only walk through the n entries. In a tree, this is more difficult. We h ...
PowerPoint - BYU Computer Science Students Homepage Index
PowerPoint - BYU Computer Science Students Homepage Index

... begin /* leaf node n is full – split */ copy n to temp; /* temp is an oversize leaf node to hold extra entry */ insert entry (P, K) in temp in correct position; /* temp now holds p+1 entries of the form (Pi, Ki) */ new  a new empty leaf node for the tree; ...
141209_Review_Slides_2
141209_Review_Slides_2

... All objects call their default constructor when declared like this: Foo a; Foo * a = new Foo(); Foo a(); The second iteration needs to be deleted manually. Upon manual or automatic (when does this happen?) deletion, a destructor is called. A copy constructor is a special constructor that is called h ...
Document
Document

Data Structure
Data Structure

... UNIT VI: Advanced concepts of Trees: Tree Travels using stack (non recursive), Threaded Binary Trees. Binary search tree, Basic concepts, BST operations: insertion, deletion, Balanced binary trees – need, basics and applications in computer science (No operations ) UNIT VII: Graphs: Basic concepts, ...
Efficient Data Storage and Searching for Location Based Services
Efficient Data Storage and Searching for Location Based Services

... [1] J Alsina, The Peano curve of Schoenberg is nowhere differentiable, Approx. Theory, Vol. 33, pp. 28-42, 1981. [2] M F Barnsley, Fractals everywhere, Academic Press, Second Edition, 1993. [3] B B Mandelbrot, Fractals: Form, Chance and Dimension, W H Freeman and Co., San Francisco, 1977. [4] L Velh ...
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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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