PPT-Locality Sensitive Hashing and Large Scale Image Search

Yunchao Gong UNC Chapel Hill yunchaocsuncedu The problem Large scale image search We have a candidate image Want to search a large database to find similar images Search the internet. Management. 7. . course. Reminder. Disk. . and RAM. RAID . Levels. Disk. . space. management. Buffering. Heap. . files. Page. . formats. Record. . formats. Today. System . catalogue. Hash-based. 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 . 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. Zhenhua . Guo. , Geoffrey Fox, Mo Zhou. Outline. Introduction. Data Locality and Fairness. Experiments. Conclusions. MapReduce Execution Overview. 3. Google File System. Read input data. Data locality. 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. Error-Correcting Codes. (Linear) Code . .. : Finite field with . elements.. . block length. . message length. : Rate of . 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 ?. 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. 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)...