'Inverted lists' presentation slideshows

CS232A: Database System Principles INDEXING

CS232A: Database System Principles INDEXING. Indexing. Given condition on attribute find qualified records Attr = value Condition may also be Attr>value Attr>=value. Qualified records. ?. value. value. value. Indexing.

1.43k views • 143 slides

CS232A: Database System Principles Indexing

CS232A: Database System Principles Indexing. Goal of Indexing. Given condition(s) on attribute(s) find qualified records Attr = value Condition may also be Attr>value Attr>=value. Qualified records. ?. value. value. value. Indexes (or Indices).

1.54k views • 138 slides

CS232A: Database System Principles INDEXING

CS232A: Database System Principles INDEXING. Indexing. Given condition on attribute find qualified records Attr = value Condition may also be Attr>value Attr>=value. Qualified records. ?. value. value. value. Indexing.

1.41k views • 137 slides

XRANK: Ranked Keyword Search over XML Documents

XRANK: Ranked Keyword Search over XML Documents. Lin Guo Feng Shao Chavdar Botev Jayavel Shanmugasundaram. Presentation by: Meghana Kshirsagar Nitin Gupta Indian Institute of Technology, Bombay. Outline. Motivation Problem Definition, Query Semantics Ranking Function

911 views • 72 slides

Ranking with Index

Ranking with Index. Rong Jin. Inverted Index. Find plays of Shakespeare related to Brutus and Calpurnia?. Inverted Index. A simple approach: linear scan, compute a score for each doc Assume idf(Brutus) = idf(Calpurnia) = 1 Slow for large collections.

925 views • 69 slides

Processing of large document collections

Processing of large document collections. Part 5. In this part:. Indexing querying index construction. Indexing. An index is a mechanism for locating a given term in a text Index in a book it is possible to find information without browsing the pages

667 views • 66 slides

Efficient Merging and Filtering Algorithms for Approximate String Searches

Efficient Merging and Filtering Algorithms for Approximate String Searches. Jiaheng Lu, University of California, Irvine Joint work with Chen Li, Yiming Lu. Example: a movie database. Find movies starred Schwarrzenger. In general: Gap between Queries and Data. Errors in the query

681 views • 51 slides

Shuai Ding, Jinru He, Hao Yan, Torsten Suel

Using Graphics Processors for High Performance IR Query Processing. Shuai Ding, Jinru He, Hao Yan, Torsten Suel. April,23 2009. The problem?. Search engine: 1000s queries/sec on billions of pages Large hardware investment Graphical processing units (GPUs)

633 views • 48 slides

Space-Constrained Gram-Based Indexing for Efficient Approximate String Search

455 views • 34 slides

Query Processing and Online Architectures

493 views • 34 slides

CS 430: Information Discovery

342 views • 23 slides

Efficient and Self-tuning Incremental Query Expansions for Top-k Query Processing

325 views • 19 slides

Efficient and Self-tuning Incremental Query Expansions for Top-k Query Processing

192 views • 19 slides

Indexing Semistructured Data

Indexing Semistructured Data. J. McHugh, J. Widom, S. Abiteboul, Q. Luo, and A. Rajaraman Stanford University January 1998 http://www-db.stanford.edu/lore/. EECS 684 02/21/2000 Presented by Weiming Zhou . Outline. Introduction - Data Model - Query Language

266 views • 15 slides