PPT-Theory of Locality Sensitive Hashing

CS246 Mining Massive Datasets Jure Leskovec Stanford University httpcs246stanfordedu Recap Finding similar documents Task Given a large number N in the millions or billions of documents find near duplicates. Optimization. Techniques. . Presented by . Preethi Rajaram. CSS 548 Introduction to Compilers . Professor Carol Zander. Fall 2012 . Why?. Processor Speed . -. increasing at a faster rate than the memory speed. Zhenhua . Guo. , Geoffrey Fox, Mo Zhou. Outline. Introduction. Analysis of Data Locality. Optimality of Data Locality. Experiments. Conclusions. MapReduce Execution Overview. 3. Google File System. Read input data. 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 . Progress to Date. Irvine. Social isolation - all ages. Low level mental health and wellbeing - young people.. Musculoskeletal issues. North Coast. Social isolation - older people. Young people’s - stress & anxiety. Cyryptocurrency Project Proposal - Spring 2015. SHA Use in Bitcoin. SHA256 used heavily as bitcoin’s underlying cryptographic hashing function. Two examples (of many) are its use in the Merkle tree hash, as well as the proof of work calculation. Arran 20. th. June 2016. Agenda. 6.30 – 6.45 . Introduction . and Background to Locality Partnerships .  . 6.45 – 7.15 . Data . – Information we have on Localities.  . 7.15 – 7.30 . Madhu Sudan. Harvard. April 9, 2016. Skoltech: Locality in Coding Theory. 1. Outline of this Part. Three ideas in Testing:. Tensor Products. Composition/Recursion. Symmetry (esp. affine-invariance. ). UCI Annual Meeting. City of Hampton. May 25, 2017. David . S. Jarman. , P.E.. Transportation Project Management Supervisor. City of Virginia . Beach. Project Delivery Options Available. UCI . Member. 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.. 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:. CS246: Mining Massive Datasets. Jure Leskovec, . Stanford University. http://cs246.stanford.edu. Recap: Finding similar documents. Task:. . Given a large number (. N. in the millions or billions) of documents, find “near duplicates”. Daniel Burk. What is a Predictive Model?. A GIS based model attempting to determine fossil locality potential. 1. Start with known fossil localities. 2. Compare their characteristics to other places. 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)...