PDF-Lecture Hashing I Chaining Hash Functions

006 Fall 2009 Lecture 5 Hashing I Chaining Hash Functions Lecture Overview Dictionaries Motivation fast DNA comparison Hash functions Collisions Chaining Simple uniform hashing Good hash functions Readings CLRS Chapter 11 1 11 2 11 3 Dictionary Prob. Haim Kaplan . and. Uri . Zwick. January 2013. Hashing. 2. Dictionaries. D .  . Dictionary() . – Create an empty dictionary. Insert(. D. ,. x. ) . – Insert item . x. into . D. Find(. D. ,. k. Shannon Quinn. (with . thanks to William Cohen of Carnegie Mellon University, and J. . Leskovec. , A. . Rajaraman. , and J. Ullman of Stanford . University). First: Upcoming deadlines!. TONIGHT @ 11:59:59pm: Assignment 3!. Martin Åkerblad. William . bruce. What is . Hashing?. Index. Key. 1. 2. 3. 4. 5. 6. 7. Key. 56. 84. 23. 42. 71. 97. 55. Hash. . function. 56. 42. 71. 23. 84. 55. 97. Value. When to use hashing.. Quick searching in large databases. CIS 606. Spring 2010. Hash tables. Many applications require a dynamic set that supports only the . dictionary . operations . INSERT. , SEARCH, and DELETE. Example: a symbol table in a compiler.. A hash table is effective for implementing a dictionary.. CST203-2 Database Management Systems. Lecture 7. Disadvantages. on index structure:. We must access an index structure to locate data, or must use binary search, and that results in more I/O operations. Hash Functions. Sections 5.1 and 5.2. 2. Hashing . Data items are stored in an . array. of some fixed size. Hash table. Search performed using some part of the data item . key. Used for performing insertions, deletions, and finds in . Lecture Note #15. Hashing. For efficient look-up in a table. Objectives. 2. [CS1020 Lecture 15: Hashing]. References. 3. [CS1020 Lecture 15: Hashing]. Outline. Direct Addressing Table. Hash Table. Hash Functions. Consider a set of data with N data items stored in some data structure. We must be able to insert, delete & search for items. What are possible ways to do this? What is the complexity of each structure & method ?. In static hashing, function . h. maps search-key values to a fixed set of . B. . buckets, that contain a number of (K,V) entries.. . . Problem: d. atabases . grow . (or shrink) . with time. . If initial number of buckets is too small, and file grows, performance will degrade due to too much overflows.. Kai Li, Guo-Jun Qi, Jun Ye, Tuoerhongjiang Yusuph, Kien A. Hua. Department of Computer Science. University of Central Florida. ISM 2016. Presented by . Tuoerhongjiang Yusuph. Introduction. Massive amount of high-dimensional data, high computational costs …. What is a hashing function?. Fingerprint for a given piece of data. Typically generated by a mathematical algorithm. Produces a fixed length string as its . output. Hashes are sometimes . called a . checksum or message digests. Hashing for Large-Scale Visual Search. Shih-Fu . Chang. www.ee.columbia.edu/dvmm. Columbia University. December 2012. Joint work with . Junfeng. He (Facebook), . Sanjiv. Kumar (Google), Wei Liu (IBM Research), and Jun Wang (IBM . Nhan Nguyen. & . Philippas. . Tsigas. ICDCS 2014. Distributed Computing and Systems. Chalmers University of Technology. Gothenburg, Sweden. Our contributions: a concurrent hash table. Nhan D. Nguyen. Amjad. . Daoud. , Ph.D.. http://iswsa.acm.org/mphf. Practical Perfect Hashing for very large Key-Value Databases . Abstract. This presentation describes a practical algorithm for perfect hashing that is suitable for very large KV (key, value)...