Workshop REMs

•Download as PPTX, PDF•

0 likes•154 views

M

dsafds

Education Technology Economy & Finance

Exemplo Slideshare

Slide 1 Loremipsumdolorsitamet, consecteturadipiscingelit. Nammollisvestibulumtincidunt. Donecerat mi, iaculissedrhoncuset, placeratinelit. Ut mi sem, vestibulum vitae semperhendrerit, varius vitae elit. Pellentesquenecenimegetsapieneuismodvehicula.

Slide 2 Loremipsumdolorsitamet, consecteturadipiscingelit. Nammollisvestibulumtincidunt. Donecerat mi, iaculissedrhoncuset, placeratinelit. Ut mi sem, vestibulum vitae semperhendrerit, varius vitae elit. Pellentesquenecenimegetsapieneuismodvehicula.

Slide 3 Loremipsumdolorsitamet, consecteturadipiscingelit. Nammollisvestibulumtincidunt. Donecerat mi, iaculissedrhoncuset, placeratinelit. Ut mi sem, vestibulum vitae semperhendrerit, varius vitae elit. Pellentesquenecenimegetsapieneuismodvehicula.

FIM

More Related Content

Viewers also liked

Search analytics what why how - By Otis Gospodnetic

Search analytics what why how - By Otis Gospodnetic

Search analytics what why how - By Otis Gospodnetic

lucenerevolution

European Childhood Obesity Group - MarieLaure Frelut, Prix ECOG et FLB 2009

European Childhood Obesity Group - MarieLaure Frelut, Prix ECOG et FLB 2009

European Childhood Obesity Group - MarieLaure Frelut, Prix ECOG et FLB 2009

Fondation Louis Bonduelle

Presented by Kai Chan | UCLA - See conference video - http://www.lucidimagination.com/devzone/events/conferences/lucene-revolution-2012 UCLA Communication Studies Archive hosts a collection of over 100,000 hours of digital television news, updated daily. Its search engine provides closed captioning search and online streaming of videos. The search engine allows researchers and students in various fields to study television news, images and language usage, in ways that were not possible before. In this presentation, we will show the setup of our Lucene/Solr-powered search engine, as well as how it is being used. We will discuss our work on custom result formats, such as linking search result text to the video at particular timestamps, counting occurrences of words, phrases or patterns, grouping the result by fields such as month or show, and creating interactive charts. We will also discuss our work on extending Lucene’s proximity searches, and creating custom query types, such as segment-enclosed (two or more words, phrases or patterns occurring within a story-based text segment), time-enclosed (two or more words, phrases or patterns occurring within a certain time), and multi-word regular expression queries. Future goals will also be discussed, such as supporting multiple languages, multiple sources (speech-to-text along side closed-captioning text), searching user-contributed and generated metadata (programs that identify story segments, objects in video, etc.), and syntactic tags (such as parts of speech).

Television news search and analysis with lucene solr

Television news search and analysis with lucene solr

Television news search and analysis with lucene solr

lucenerevolution

Outline of the case of Mark Long vs. John Kroger, Sean Riddle and the Oregon Dept of Justice (this document is public record) Most of the case is currently sealed pending the Federal Court Case. John Kroger learned from the bad guys and adopted their MO and taught people like Riddle and John Dunbar what to do. They are above the law and will do anything to further their careers at the expense of due process and what is right or wrong. Just my experience. Update: The State of Oregon settled the case for $1,000,000

Oregon dept of justice tactics

Oregon dept of justice tactics

Oregon dept of justice tactics

J. Patrick Lucas

Presented by Scott Stults | OpenSource Connections. See conference video - http://www.lucidimagination.com/devzone/events/conferences/lucene-revolution-2012 Amazon Web Services offers a quick and easy way to build a scalable search platform, a flexibility is especially useful when an initial data load is required but the hardware is no longer needed for day-to-day searching and adding new documents. This presentation will cover one such approach capable of enlisting hundreds of worker nodes to ingest data, track their progress, and relinquish them back to the cloud when the job is done. The data set that will be discussed is the collection of published patent grants available through Google Patents. A single Solr instance can easily handle searching the roughly 1 million patents issued between 2010 and 2005, but up to 50 worker nodes were necessary to load that data in a reasonable amount of time. Also, the same basic approach was used to make three sizes of PNG thumbnails of the patent grant TIFF images. In that case 150 worker nodes were used to generate 1.6 Tb of data over the course of three days. In this session, attendees will learn how to leverage EC2 as a scalable indexer and tricks for using XSLT on very large XML documents.

Indexing big data in the cloud

Indexing big data in the cloud

Indexing big data in the cloud

lucenerevolution

Presented by Engy Ali | The Library of Alexandria - See conference video - http://www.lucidimagination.com/devzone/events/conferences/lucene-revolution-2012 Do you have a large collection of text content that you want to search? Facing challenges on how to facet after performing a full text search across metadata and content? Do you want to use Solr with personalization? Bibliotheca Alexandrina provides public access to digitized book collections that exceed 220,000 books, through a web-based search and browsing facility. The facility is completely built on Solr in five different languages. The website provides full text morphological search within the books’ metadata and content with result highlighting. Different personalization features like annotation tools and tagging are also implemented using Solr. This presentation will cover how Bibliotheca Alexandrina uses Solr to implement full text indexing and searching across the entire collection, faceting, search within the content of a book and result highlighting and techniques used for personalization.

How to Access Your Library Book Collections Using Solr

How to Access Your Library Book Collections Using Solr

How to Access Your Library Book Collections Using Solr

lucenerevolution

7 'Nãos' de que precisa

7 'Nãos' de que precisa

7 'Nãos' de que precisa

Oficina Psicologia

Baltimore day6 12breakouthandout

Baltimore day6 12breakouthandout

Baltimore day6 12breakouthandout

Presentatie peter vink back to the future, TASS technology solutions

Presentatie peter vink back to the future, TASS technology solutions

Presentatie peter vink back to the future, TASS technology solutions

See conference video - http://www.lucidimagination.com/devzone/events/conferences/ApacheLuceneEurocon2011 Metadata is widely understood to be a critical element of search, discovery and classification. But with the preponderance of unstructured data addressed by search technology, consistent native metadata is often in short supply. Organizations often find that the quality and depth of contextual metadata -- what documents are about – can maker or break search relevancy, precision and recall. Semaphore is an enterprise semantic platform that uniquely captures an organization‘s subjects and topics into a taxonomy or ontology (model), in a manner that adds context for enhanced navigation and findability. Semaphore augments traditional information management systems like Solr search by adding advanced content classification, metadata and navigation capabilities to deliver a more complete, higher quality enterprise information management experience. This talk will focus on the following: Deep dive into the technical integration of Semaphore with Apache/ Solr (including the connection points between Semaphore and Solr) Discuss the Semaphore modules (Ontology Manager, Classification Server, Semantic Enhancement Server and Search Application Server) and how they provide better findability Share a demonstration of Solr in action Present a client case study (Nordyske).

More Powerful Solr Search with Semaphore - Jeremy Bentley

More Powerful Solr Search with Semaphore - Jeremy Bentley

More Powerful Solr Search with Semaphore - Jeremy Bentley

lucenerevolution

Viewers also liked (10)

Search analytics what why how - By Otis Gospodnetic

Search analytics what why how - By Otis Gospodnetic

Search analytics what why how - By Otis Gospodnetic

European Childhood Obesity Group - MarieLaure Frelut, Prix ECOG et FLB 2009

European Childhood Obesity Group - MarieLaure Frelut, Prix ECOG et FLB 2009

European Childhood Obesity Group - MarieLaure Frelut, Prix ECOG et FLB 2009

Television news search and analysis with lucene solr

Television news search and analysis with lucene solr

Television news search and analysis with lucene solr

Oregon dept of justice tactics

Oregon dept of justice tactics

Oregon dept of justice tactics

Indexing big data in the cloud

Indexing big data in the cloud

Indexing big data in the cloud

How to Access Your Library Book Collections Using Solr

How to Access Your Library Book Collections Using Solr

How to Access Your Library Book Collections Using Solr

7 'Nãos' de que precisa

7 'Nãos' de que precisa

7 'Nãos' de que precisa

Baltimore day6 12breakouthandout

Baltimore day6 12breakouthandout

Baltimore day6 12breakouthandout

Presentatie peter vink back to the future, TASS technology solutions

Presentatie peter vink back to the future, TASS technology solutions

Presentatie peter vink back to the future, TASS technology solutions

More Powerful Solr Search with Semaphore - Jeremy Bentley

More Powerful Solr Search with Semaphore - Jeremy Bentley

More Powerful Solr Search with Semaphore - Jeremy Bentley

Workshop REMs

1. Exemplo Slideshare

2. Slide 1 Loremipsumdolorsitamet, consecteturadipiscingelit. Nammollisvestibulumtincidunt. Donecerat mi, iaculissedrhoncuset, placeratinelit. Ut mi sem, vestibulum vitae semperhendrerit, varius vitae elit. Pellentesquenecenimegetsapieneuismodvehicula.

3. Slide 2 Loremipsumdolorsitamet, consecteturadipiscingelit. Nammollisvestibulumtincidunt. Donecerat mi, iaculissedrhoncuset, placeratinelit. Ut mi sem, vestibulum vitae semperhendrerit, varius vitae elit. Pellentesquenecenimegetsapieneuismodvehicula.

4. Slide 3 Loremipsumdolorsitamet, consecteturadipiscingelit. Nammollisvestibulumtincidunt. Donecerat mi, iaculissedrhoncuset, placeratinelit. Ut mi sem, vestibulum vitae semperhendrerit, varius vitae elit. Pellentesquenecenimegetsapieneuismodvehicula.