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Art of Multiprocessor Programming
Art of Multiprocessor Programming

Data Structures and Algorithm Analysis in C
Data Structures and Algorithm Analysis in C

... orientation) for the presence of the word. This amounts to lots of nested for loops but is basically straightforward. Alternatively, for each ordered quadruple (row, column, orientation, number of characters) that doesn't run off an end of the puzzle, we can test whether the word indicated is in the ...
Matthias Petri - J. Shane Culpepper
Matthias Petri - J. Shane Culpepper

Page 264, Exercise 1
Page 264, Exercise 1

... a subrange of the integers starting at (INT_MIN) and ending at the maximum integer (INT_MAX) on the computer functions: for all x, y ∈ Set; TRUE, FALSE ∈ Boolean and the Boolean operations defined in Problem 4 are available (not, and, or,...) and where ==, Ø, +, head(s), tail(s) are the usual set op ...
Java Structures: Data Structures for the Principled Programmer
Java Structures: Data Structures for the Principled Programmer

... This book focuses on data structures implemented in the Python language. Python is a scripting language. This means that it can be used to solve a wide variety of problems and its programs are presented as scripts that are directly interpreted. In traditional programming languages, like C or Java, p ...
Practical Parallel Data Structures
Practical Parallel Data Structures

... concept in this thesis. We start by designing the first wait-free linked-list with practical performance. We then generalize the technique, and offer an automatic transformation that allows even a non-expert to design efficient wait-free data structures. We use the proposed transformation to obtain ...
Compressed Suffix Arrays and Suffix Trees with Applications to Text
Compressed Suffix Arrays and Suffix Trees with Applications to Text

Consistency Oblivious Programming
Consistency Oblivious Programming

Addison Wesley - Algorithms in Java, Parts 1-4, 3rd Edition
Addison Wesley - Algorithms in Java, Parts 1-4, 3rd Edition

ABSTRACT SCALABLE ONTOLOGY SYSTEMS Octavian Udrea, Doctor of Philosophy, 2008 Professor V.S. Subrahmanian
ABSTRACT SCALABLE ONTOLOGY SYSTEMS Octavian Udrea, Doctor of Philosophy, 2008 Professor V.S. Subrahmanian

Data Structures and Algorithms: Table of Contents
Data Structures and Algorithms: Table of Contents

... expensive for large graphs, no matter how efficient we try to make the program. A second approach would be to look for additional information about the problem at hand. It may turn out that the graph has some special properties, which make it unnecessary to try all possibilities in finding an optima ...
Dynamic Data Structures for Document Collections and Graphs
Dynamic Data Structures for Document Collections and Graphs

... as listing or counting all labels related to an object, listing or counting all objects related to a label, and telling whether a label and an object are related. Their method reduces queries on a binary relation R to rank, select, and access queries on SR and BR . Another data structure that stores ...
Data Structures and Algorithms in Java - Go Green
Data Structures and Algorithms in Java - Go Green

... Copy Editor Chuck Hutchinson ...
Algorithms and Data Structures for Games Programming
Algorithms and Data Structures for Games Programming

... (STL)) provides very efficient implementations of the most common data structures and algorithms. When I’m using Standard Library (STL) features on something I’m not too sure about, I have (Josuttis 1999), (Reese 2007), (Meyers 2001) and (Stroustrup 1997) by my right hand. For both STL and C++, (Str ...
Towards  Constant  Bandwidth  Overhead E.
Towards Constant Bandwidth Overhead E.

... logarithmic number of hashes must be read each time the data is accessed. One proposed use of a hash tree is in a single-chip secure processor [5, 10, 19], where it is used to check the integrity of external memory. A secure processor can be used to help license software programs, where it seeks to ...
Lower Bound Techniques for Data Structures
Lower Bound Techniques for Data Structures

Algorithms and Compressed Data Structures for Information Retrieval
Algorithms and Compressed Data Structures for Information Retrieval

... In this thesis we address the problem of the efficiency in Information Retrieval by presenting new compressed data structures and algorithms that can be used in different application domains and achieve interesting space/time properties. We propose (i) a new variable-length encoding scheme for sequence ...
Compressed Data Structures for Dynamic Sequences
Compressed Data Structures for Dynamic Sequences

... number of elements that are marked as deleted is bounded by O(n/r). If a subsequence Si contains many elements marked as deleted, it is re-built: we create a new instance of Si that does not contain deleted symbols. If a symbol sequence S0 contains too many elements, we insert the elements of S0 int ...
Compressed Suffix Trees: Design, Construction, and Applications
Compressed Suffix Trees: Design, Construction, and Applications

Improving Lookup Time Complexity of Compressed Suffix Arrays
Improving Lookup Time Complexity of Compressed Suffix Arrays

C Programming: Data Structures and Algorithms
C Programming: Data Structures and Algorithms

New data structures and algorithms for the efficient management of
New data structures and algorithms for the efficient management of

... Our data structures can be used in several application domains. We propose specific variants and combinations of our generic proposals to represent temporal graphs, RDF databases, OLAP databases, binary or general raster data, and temporal raster data. We also propose a new algorithm to jointly quer ...
Persistent Predecessor Search and Orthogonal Point Location on
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I n - Read
I n - Read

I n - Computer Science at Virginia Tech
I n - Computer Science at Virginia Tech

... Some Questions to Ask • 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? ...
1 2 3 4 5 ... 76 >

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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