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QUAD TREE STRUCTURES FOR IMAGE COMPRESSION
QUAD TREE STRUCTURES FOR IMAGE COMPRESSION

Fundamental Data Structures
Fundamental Data Structures

... data structure is index-based data structures, such as lists and hashtables. Each element is accessed by an index, which points to the position the element within the data structure. This is covered in Chapter 6 of the primary textbook[Conery(2011)]. In the three chapters of this handout, we will be ...
sorted
sorted

... A binary tree is either NULL or contains a node with the key/value pair, and a left and right child which are themselves trees. In a binary search tree (BST) the keys in the left child are smaller than the keys at the node and the values in the right child are greater than the value at the node. (Re ...
advanced algorithms
advanced algorithms

Min-Max Heaps and Generalized Priority Queues
Min-Max Heaps and Generalized Priority Queues

Unit III Linked Lists Variations
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... One of the problems in dealing with pointer based ordered lists is writing code to take care of special cases. For example, if we wish to insert a node in an ordered linked list, we MUST take care of the special case of inserting this node in the beginning of the list. This is a special case because ...
Indexing and Hashing.key
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Linked Lists, Stacks, Queues
Linked Lists, Stacks, Queues

Data Structure Fusion - Stanford CS Theory
Data Structure Fusion - Stanford CS Theory

... data structures, namely a binary tree mapping each vertex to a list of its successors, together with the corresponding edge weights, and a binary tree mapping each vertex to a list of its predecessors, and the corresponding edge weights. One problem with our proposed ML representation is that the su ...
Spatial Data Structures
Spatial Data Structures

Searching: Binary Trees and Hash Tables
Searching: Binary Trees and Hash Tables

pptx
pptx

... • Maintain as unordered list – add() put new element at front – O(1) – poll() must search the list – O(n) – peek() must search the list – O(n) • Maintain as ordered list – add() must search the list – O(n) – poll() min element at front – O(1) – peek() O(1) Can we do better? ...
Advancing Front Method
Advancing Front Method

... Consecutive points are joined by straight lines to form sides. In order to determine, the position and the number of nodes on each curve component, the following steps are followed: i- Subdivide each curve into smaller segments until their length is smaller that a certain prescribe value. The length ...
Recent developments in linear quadtree
Recent developments in linear quadtree

Balanced BSTs
Balanced BSTs

... of O(lg n) is guaranteed when implementing a dynamic set of n items. • Examples: ...
Linked Lists
Linked Lists

Generalizing Database Access Methods
Generalizing Database Access Methods

(pdf of the updated version.)
(pdf of the updated version.)

Fully-online Construction of Suffix Trees for Multiple Texts
Fully-online Construction of Suffix Trees for Multiple Texts

Selection and Search
Selection and Search

... 1. Divide n elements into groups of 5 2. Find median of each group (How? How long?) 3. Use Select() recursively to find median x of the n/5 ...
Linked lists, skip lists
Linked lists, skip lists

... class SkipNode { E data; SkipNode links[]; ...
R-Trees
R-Trees

... MINMAXDIST(p,R) is the minimum of the maximum distance to each pair of faces of R  MaxDistanceToFace(p,R,k) = distance between p and Maxk= (M1,M2,..,Mk-1mk,MK+1,..,Mn)  mi = closer of the two boundary points along i axis  Mi = farther of the two boundary points along i axis ...
Non-blocking Patricia Tries with Replace Operations
Non-blocking Patricia Tries with Replace Operations

Shallow Bounding Volume Hierarchies for Fast SIMD Ray Tracing of
Shallow Bounding Volume Hierarchies for Fast SIMD Ray Tracing of

AN EXAMINATION OF FAST SIMILARITY SEARCH TREES
AN EXAMINATION OF FAST SIMILARITY SEARCH TREES

... I would like to express my gratitude to my supervisor, Dr. Jeffrey Uhlmann, whose expertise, understanding, and patience, added considerably to my graduate experience. I have been amazingly fortunate to have an advisor who gave me the freedom to explore on my own, and at the same time the guidance t ...
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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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