PDF-Lecture Universal and Perfect Hashing

1 Overview Hashing is a great practical tool with an interesting and su btle theory too In addition to its use as a dictionary data structure hashing also comes up in many di 64256erent areas including cryptography and complexity theory In this lectu. Recently bit minwise hashing has been applied to largescale learning and sublinear time near neighbor search The major drawback of minwise hashing is the expensive pre processing as the method requires applying eg 200 to 500 permutations on the dat 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. Management. 7. . course. Reminder. Disk. . and RAM. RAID . Levels. Disk. . space. management. Buffering. Heap. . files. Page. . formats. Record. . formats. Today. System . catalogue. Hash-based. Uri . Zwick. January 2014. Hashing. 2. Dictionaries. D .  . Dictionary() . – Create an empty dictionary. Insert(. D. ,. x. ) . – Insert item . x. into . D. Find(. D. ,. k. ) . – Find an item with key . Yunchao. Gong. UNC Chapel Hill. yunchao@cs.unc.edu. The problem. Large scale image search:. We have a candidate image. Want to search a . large database . to find similar images. Search the . internet. Approximate Near Neighbors. Ilya Razenshteyn (CSAIL MIT). Alexandr. . Andoni. (Simons Institute). Approximate Near Neighbors (ANN). Dataset:. . n. points in . d. dimensions. Query:. a point within . 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. 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.. 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. How do we do it?. Array?. Linked . List?. Binary Search Tree?. AVL Tree?. Binary Heap?. We want something better.... Finding problem:. Let’s go back to arrays:. . Arrays allow us to access data at an index quickly. kindly visit us at www.nexancourse.com. Prepare your certification exams with real time Certification Questions & Answers verified by experienced professionals! We make your certification journey easier as we provide you learning materials to help you to pass your exams from the first try. kindly visit us at www.nexancourse.com. Prepare your certification exams with real time Certification Questions & Answers verified by experienced professionals! We make your certification journey easier as we provide you learning materials to help you to pass your exams from the first try. 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)...