PPT-HASHING

Author : tawny-fly | Published Date : 2016-05-03

COL 106 Shweta Agrawal Amit Kumar Slide Courtesy Linda Shapiro Uwash Douglas W Harder UWaterloo 122603 Hashing Lecture 10 2 The Need for Speed Data structures

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COL 106 Shweta Agrawal Amit Kumar Slide Courtesy Linda Shapiro Uwash Douglas W Harder UWaterloo 122603 Hashing Lecture 10 2 The Need for Speed Data structures we have looked at so far. 1071242Open Hashing (Chaining)0123456789abdgcf Constant Worst-Case Operations with a Succinct Representation. Yuriy. . Arbitman. . Moni. Naor Gil . Segev. Dynamic Dictionary. Data structure representing a set of words . S. From a Universe . and. Algorithms. Course slides: Hashing. www.mif.vu.lt. /~. algis. 2. Data Structures for Sets. Many applications deal with sets.. Compilers have symbol tables (set of . vars. , classes). Dictionary is a set of words.. 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 . H. ashing: . Scalable and Flexible Hashing on GPUs. Farzad Khorasani, Mehmet E. . Belviranli. , Rajiv Gupta, . Laxmi. N. . Bhuyan. University of California Riverside. What Stadium hashing addresses. 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. Hessam. . Zakerzadeh. ISAM. (. Indexed Sequential Access Method. ). Static index structure.. Effective when file is not frequently updated.. Data values reside in leaf nodes.. Leaf pages contain . data entries. 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. 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. 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 ?. Naifan Zhuang, Jun Ye, Kien A. Hua. Department of Computer Science. University of Central Florida. ICPR 2016. Presented by Naifan Zhuang. Motivation and Background. According to a report from Cisco, by 2019:. 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.

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