PPT-An Efficient Trie -based Method for Approximate

Entity Extraction with EditDistance Constraints Dong Deng Tsinghua China Guoliang Li Tsinghua China Jianhua Feng Tsinghua China Outline Motivation Problem Formulation. CSE 303 Homework 4, Autumn 2008. T9 Predictive Text. What is T9? Demo. . T9onyms:. 1. . 22737: acres, bards, barer, bares, baser, bases, caper, capes, cards, cares, cases. 2. . 46637: goner, goods, goofs, homer, homes, . By Venkatesh Ganti, Mong Li Lee, and Raghu Ramakrishnan. CSE6339 – Data exploration. Raghavendra Madala. In this presentation…. Introduction. Icicles. Icicle Maintenance. Icicle-Based Estimators. A useful data structure of storing and retrieving Strings and things. Tries. A . trie. , also called digital tree and sometimes radix tree or prefix tree (as they can be searched by prefixes), is an ordered tree data structure that is used to store a dynamic set or associative array where the keys are usually strings.. Abbas Rahimi, . Andrea . Marongiu. ,. . Rajesh K. Gupta, Luca . Benini. UC San Diego, and . University of Bologna . Micrel.deis.unibo.it. /. MultiTherman. variability.org. Outline. Introduction. and . CSE . 374 Homework . 5. , Spring 2009. T9 Predictive Text. What is T9? Demo . T9onyms:. 1. . 22737: acres, bards, barer, bares, baser, bases, caper, capes, cards, cares, cases. 2. . 46637: goner, goods, goofs, homer, homes, . Abbas Rahimi, . Andrea . Marongiu. ,. . Rajesh K. Gupta, Luca . Benini. UC San Diego, and . University of Bologna . Micrel.deis.unibo.it. /. MultiTherman. variability.org. Outline. Introduction. and . Ulya. . R. . Karpuzcu. ukarpuzc@umn.edu. . 12/01/2015. Outline. Background. Pitfalls & Fallacies. Practical Guidelines. 2. 12/01/2015. On Quantification of Accuracy Loss in Approximate Computing. University of Washington. Adrian Sampson, . Hadi. Esmaelizadeh,. 1. Michael . Ringenburg. , . Reneé. St. Amant,. 2. . Luis . Ceze. , . Dan Grossman. , Mark . Oskin. , Karin Strauss,. 3. and Doug Burger. Computation Circuits. Wei-Ting Jonas Chan. 1. , Andrew B. Kahng. 1. , . Seokhyeong Kang. 1. , . Rakesh. Kumar. 2. , and John Sartori. 3. 1. VLSI . CAD LABORATORY, . UC San Diego. 2. PASSAT GROUP, Univ. of Illinois. Abbas Rahimi. , Amirali Ghofrani, Kwang-Ting Cheng, Luca Benini, Rajesh K. . Gupta. UC San Diego. , UC Santa Barbara, ETH Zurich. NSF Variability Expedition. ERC . MultiTherman. Motivation. Energy Efficiency in GPUs. (RST). R-way . trie. (RT). De la . Briandias. . trie. (DLB). Binary search tree (BST). Left branch is less than. Right branch is larger than. Create a tree with 0, 1, 2, 3 (in order). Create BST. Trie. -based Method for Approximate. Entity Extraction with Edit-Distance Constraints. Entity Extraction. A Document. An Efficient Filter for Approximate Membership Checking. . Venkaee. . shga. . Kamunshik. Matagorda County, TX. CE 394K Fall 2017. Sydney Kase. Essential Questions. Why does FEMA create the . NFHL. and . FIRM. maps?. Why does it take so long to produce effective floodplain maps?. What does the . Michael T. Goodrich. University of California, Irvine. The Multi-Pattern Matching Problem. Given a text, T, of length n and a set of k patterns, P. 1. , …, P. k. , each of length at most m, find the first (or each) occurrence of a given pattern in T..