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Kernel Data Structures
Kernel Data Structures

... search of Linux’s page cache for a chunk of a file Each inode has its own rbtree, keyed off of page offsets into file This function thus searches the given inode’s rbtree for a matching offset ...
Minimum Bounding Boxes
Minimum Bounding Boxes

... each edge there are 3 two-dimensional planes in which the minimum bounding boxes can be placed. Each minimum bounding box has n possible rotations, giving us an overall complexity of O(n3) running time to find a minimum volume bounding box in three dimensions. Minimum bounding boxes are often used a ...
Singly-Linked List
Singly-Linked List

... Work well in a lot of cases. ...
Improving the Performance of Region Quadtrees
Improving the Performance of Region Quadtrees

Fundamental Data Structures
Fundamental Data Structures

... In computer science, an abstract data type (ADT) is a mathematical model for a certain class of data structures that have similar behavior; or for certain data types of one or more programming languages that have similar semantics. An abstract data type is defined indirectly, only by the operations ...
A Fully-Functional Static and Dynamic Succinct Trees
A Fully-Functional Static and Dynamic Succinct Trees

ch12
ch12

... Before and after deleting “Downtown”  The removal of the leaf node containing “Downtown” did not result in its parent having too little pointers. So the cascaded deletions stopped with the deleted leaf node’s parent. Database System Concepts - 5th Edition, Aug 12, 2005. ...
Data Structures and Algorithms for Data
Data Structures and Algorithms for Data

... operations that augment collection libraries in many languages today. Data collection operations like reduction, filtering or mapping can be executed by a single processor or many processors at once. However, there are multiple challenges to overcome when parallelizing collection operations. First, ...
INTRODUCTION TO C - Technicalsymposium
INTRODUCTION TO C - Technicalsymposium

space-efficient data structures for string searching and retrieval
space-efficient data structures for string searching and retrieval

... decomposed into O(p) subproblems, each of them are 3-sided range reporting queries in 2d (which can be solved optimally). This inevitably adds an additive O(p) term in the query time, however it will not change the query time as O(p) time is needed for initial pattern search using suffix tree. Histo ...
Chapter 8 DYNAMIC DATA STRUCTURES AND LISTS 8.1 Pointers
Chapter 8 DYNAMIC DATA STRUCTURES AND LISTS 8.1 Pointers

... -----------------------------------------Often more than one pointer will point to the same object. For this reason, you must be careful when returning the storage occupied by a record to the heap. If memory is reallocated after it is returned, errors may result. Make sure that you have no need for ...
CHAPTER 18 Linked Lists, Stacks, Queues, and Priority
CHAPTER 18 Linked Lists, Stacks, Queues, and Priority

... introduces linked lists. A linked list can be used just like a list. The difference lies in performance. Using linked lists is more efficient for inserting and removing elements from the beginning of the list than using a list. Using a list is more efficient for retrieving an element via an index th ...
Fundamental Data Structures - University of North Florida
Fundamental Data Structures - University of North Florida

... In computing, an abstract data type (ADT) is a mathematical model for a certain class of data structures that have similar behavior; or for certain data types of one or more programming languages that have similar semantics. An abstract data type is defined indirectly, only by the operations that ma ...
Chapter 11: Indexing and Hashing
Chapter 11: Indexing and Hashing

... 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 and Li+1. Then in parent node Ki must be equal to V ...
Chapter 11: Indexing and Hashing
Chapter 11: Indexing and Hashing

On Approximate Range Counting and Depth
On Approximate Range Counting and Depth

COMP 202 The Linked List Static vs. Dynamic Structures Object
COMP 202 The Linked List Static vs. Dynamic Structures Object

... – Structures that can be built (or re-formed) at run-time (called dynamic structures) ...
Course 3:Linked List - 天府学院数据结构精品课程
Course 3:Linked List - 天府学院数据结构精品课程

Improving the efficiency of priority-queue structures
Improving the efficiency of priority-queue structures

... the same asymptotical bound in the worst-case sense as binomial queues and the comparison complexity of delete is 3 lg n + O(1) given that find -min has the worst-case cost of O(1). Similar to binomial queues the basic components of run-relaxed heaps are binomial trees. Apart from the differences in ...
Paper
Paper

... tree by replacing three edges at a saddle point with a single new edge, based on the height of the edge. [Takahashi et al. 2004b] use the approximate volume of the region represented by the subtree that is discarded. Saddles are processed until only a few remain, then a transfer function is construc ...
Worst Case Efficient Data Structures
Worst Case Efficient Data Structures

... arrays of size O(log n). Our bounds match the best known amortized bounds (achieved by respectively binomial queues and Fibonacci heaps). The data structures presented are the first achieving these worst case bounds, in particular supporting Meld in worst case constant time. We show that these time ...
File format for documents containing both logical
File format for documents containing both logical

Interfaces
Interfaces

... message, the object is using its own internal state (instances variables). Because an object sends messages to itself so frequently, Java provides a shortcut: this and the dot are not really necessary before the method name. Whereas the keyword this is sometimes required, it was not really necessary ...
Indexing Structures for Searching in Metric Spaces
Indexing Structures for Searching in Metric Spaces

... In some applications, the metric space turns out to be of a particular type called vector space, where the elements consist of k coordinates (often termed feature vectors). For example, images are often represented by color and shape histograms [38, 48], typically consisting of 64 or 256 values. A l ...
recdata
recdata

... Proof of Property 3 Show (append x (append y z)) = (append (append x y) z) Base case. x = nil. (append nil (append y z)) = (append y z) = (append (append nil y) z) Assume property holds for (rest x)  (append (append x y) z)  (append (cons (first x) (append (rest x) y)) z) [by def]  (cons (fir ...
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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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