PPT-Optimal Data-Dependent Hashing for

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 . The analysis uses only very basic and intuitively understandable concepts of probability theory and is meant to be accessible even for undergraduates taking their 64257rst algorithms course 1 Introduction dictionary is a data structure for storing a Up to this point the greatest drawback of cuckoo hashing appears to be that there is a polynomially small but practically signicant probability that a failure occurs during the insertion of an item requiring an expensive rehashing of all items in th A. pproximate . N. ear . N. eighbors. Alexandr Andoni . (Simons Inst. . /. . Columbia). Ilya Razenshteyn . (MIT, now at IBM . Almaden. ). Near Neighbor Search. Dataset: . points in . , . Goal: . a data point within . Management. 7. . course. Reminder. Disk. . and RAM. RAID . Levels. Disk. . space. management. Buffering. Heap. . files. Page. . formats. Record. . formats. Today. System . catalogue. Hash-based. 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.. 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. Similarity Search. Alex . Andoni. (Columbia University). Nearest Neighbor Search (NNS). Preprocess: . a set . . of points. Query:. given a . query point . , report a point . with the smallest distance to . Hashing & Partitioning. 1. Peng Sun. Server Load Balancing. Balance load across servers. Normal techniques: . Round-robin? . 2. Limitations of Round Robin. Packets of a single connection spread over several servers. 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. 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 ?. 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.. Lecture . 19 . – . BigTable. , Spanner. , . Hashing. Overview. BigTable. Spanner. Hashing Tricks. 2. BigTable. Distributed storage system for managing structured data.. Designed to scale to a very large size. 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)...