'Time o' diaporamas de présentation

Suffix tree and suffix array techniques for pattern analysis in strings

Suffix tree and suffix array techniques for pattern analysis in strings. Esko Ukkonen Univ Helsinki Erice School 30 Oct 2005.

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Large- Treewidth Graph Decompositions and Applications

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Pattern Matching

Pattern Matching. Strings. A string is a sequence of characters Examples of strings: Java program HTML document DNA sequence Digitized image An alphabet S is the set of possible characters for a family of strings Example of alphabets: ASCII Unicode {0, 1} {A, C, G, T}.

1.94k views • 68 slides

Week 3: Minimum Spanning Trees

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Pseudo-randomness

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Analysis of Algorithms CS 477/677

Analysis of Algorithms CS 477/677. Instructor: Monica Nicolescu. The Heap Data Structure. Def: A heap is a nearly complete binary tree with the following two properties: Structural property: all levels are full, except possibly the last one, which is filled from left to right

750 views • 40 slides

IDS594 Special Topics in Big Data Analytics

IDS594 Special Topics in Big Data Analytics. Week5. Chaining Jobs. Many problems can be solved with MapReduce , by writing several MapReduce steps which run in series to accomplish a goal.

497 views • 38 slides

Tomography-based Overlay Network Monitoring

Tomography-based Overlay Network Monitoring. Yan Chen, David Bindel, and Randy H. Katz. UC Berkeley. Motivation. Infrastructure ossification led to thrust of overlay and P2P applications Such applications flexible on paths and targets, thus can benefit from E2E distance monitoring

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Area-I/O Flip-Chip Routing for Chip-Package Co-Design

Area-I/O Flip-Chip Routing for Chip-Package Co-Design. Jia -Wei Fang, Yao- Wen Chang. Outline. Introduction Problem Formulation The Routing Algorithm Global Routing RDL Routing Experimental Results Conclusions. Introduction.

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CS 332: Algorithms

CS 332: Algorithms. Linear-Time Sorting Continued Medians and Order Statistics. Review: Comparison Sorts. Comparison sorts: O(n lg n) at best Model sort with decision tree Path down tree = execution trace of algorithm Leaves of tree = possible permutations of input

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CSE332: Data Abstractions Lecture 20 : Parallel Prefix and Parallel Sorting

CSE332: Data Abstractions Lecture 20 : Parallel Prefix and Parallel Sorting. Dan Grossman Spring 2010. What next?. Done: Simple ways to use parallelism for counting, summing, finding Even though in practice getting speed-up may not be simple

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Parallel Prefix, Pack, and Sorting

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Efficient Approximation of Edit Distance

Efficient Approximation of Edit Distance. Robert Krauthgamer, Weizmann Institute of Science SPIRE 2013. TexPoint fonts used in EMF. Read the TexPoint manual before you delete this box .: A A A A A A A A A. Edit Distance (Levenshtein distance). Given two strings x ? ? n , y ? ? m :.

427 views • 27 slides

CS 253: Algorithms

CS 253: Algorithms. Chapter 22 Graphs. Credit : Dr. George Bebis. 2. 1. 3. 4. Graphs. Definition = a set of nodes (vertices) with edges (links) between them . G = (V, E) - graph V = set of vertices ? V ?= n E = set of edges ? E ?= m

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Section 5: More Parallel Algorithms

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CSE332: Data Abstractions Lecture 26: Amortized Analysis

CSE332: Data Abstractions Lecture 26: Amortized Analysis. Tyler Robison Summer 2010. Amortized Analysis . Recall our plain-old stack implemented as an array that doubles its size if it runs out of room How can we claim push is O ( 1 )?

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Graph Algorithms

Graph Algorithms. Overview. Graphs are very general data structures data structures such as dense and sparse matrices, sets, multi-sets, etc. can be viewed as representations of graphs Algorithms on matrices/sets/etc. can usually be interpreted as graph algorithms

335 views • 17 slides

Leveraging Linial's Locality Limit

Leveraging Linial's Locality Limit. Christoph Lenzen, Roger Wattenhofer. D istributed C omputing G roup. The problem. we have a sensor network, where nodes communicate by radio all radios have the same (normalized) range we want to minimize energy consumption for communication

247 views • 13 slides

Tries

Tries. Standard Tries Compressed Tries Suffix Tries. Text Processing. We have seen that preprocessing the pattern speeds up pattern matching queries

486 views • 11 slides