PPT-Hash Tables: Linear Probing

Uri Zwick Tel Aviv University Started April 2015 Last update January 12 2017 Hashing with open addressing Uniform probing Insert key in the first free position among   Sometimes assumed to be a . Spencer Dawson. Summary. What are rainbow tables?. A time and memory tradeoff in password cracking.. A piecewise approach to one-way hashes. What are the advantages/disadvantages. Best uses. Limitations. Lecture 3. Sets. A . set. is simply a collection of elements. Unlike lists, elements are not ordered. Very abstract, general concept with broad usefulness:. The set of all Google search queries from the past 24 hours. Motivating Applications. Large collection of datasets. Datasets are dynamic (insert, delete). Goal: efficient searching/insertion/deletion. Hashing is ONLY applicable for exact-match searching. Direct Address Tables. Comp 550. Dictionary . Dictionary:. Dynamic-set data structure for . storing items indexed using . keys. .. Supports . operations: Insert, Search, and . Delete . (. take . O. (1) time. ). .. Applications:. Hashing Part One. Reaching for the Perfect Search. Most of this material stolen from. "File Structures" by Folk, . Zoellick. and . Riccardi. Text File v. Binary File. Unordered Binary File. average search takes . Linear Probing. Uri Zwick. Tel Aviv University. Hashing with open addressing. “Uniform probing”. Insert key . in the first free position among.  . (Sometimes) assumed to be a . permutation. To search, follow the same order. 8. . Hashing. Hashing. : An implementation of a set. It provides O(1) expected time for set operations. Set operations. Make the set empty. Add an element to the set. Remove an element from the set. . Sections 5.3, 5.4, 5.5, 5.6. 2. Designing a Hash Table. Hash function: establishing a key with an indexed location in a hash table.. E.g. . Index = hash(key) % table_size;. Resolve conflicts: . Need to handle case where multiple keys mapped to the same index.. Plan. I spent the last decade advising on numerous cases where hash tables/functions were used. A few observations on . What data structures I’ve seen implemented and where. What do developers think, were they need help. Sahil. Singla. . (Carnegie Mellon University). Joint . Work (. Partly) With . ANUPAM GUPTA . and . VISWANATH NAGARAJAN. (2. nd. Feb, 2018). Combinatorial Optimization. Given a . Finite . Universe. Submit Prelim 2 conflicts by . Thursday night. A6 is due Nov 7 (. tomorrow!. ). 2. Ideal Data Structure . 3. Data Structure. add. (. val. x). get. (int i). contains. (. val. x). ArrayList. LinkedList. n. . . [from the . techspeak. ] (var. ‘hash clash’) . When used of people, signifies a confusion in associative memory or imagination, especially a persistent one.” . . -. The Hacker's Dictionary . CSE 373: Data Structures and Algorithms. Thanks to Kasey Champion, Ben Jones, Adam Blank, Michael Lee, Evan McCarty, Robbie Weber, Whitaker Brand, Zora Fung, Stuart . Reges. , Justin Hsia, Ruth Anderson, and many others for sample slides and materials .... Carisa Petris, Don Liu . Issue . 7. , 2017. A presentation to:. Meeting name. Date. Table of Contents. 01. Background. 02. Types. of studies. 03. Key results. 04. Tables (Risk of Bias/Forest Plots).