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Text Classification Powered by Apache Mahout and Lucene
lucenerevolution
Presented by Markus Klose, Search + Big Data Consultant SHI Elektronische Medien GmbH at Lucene/Solr Revolution 2013 Dublin Kibana4Solr is search-driven, scalable, browser based and extremely user friendly (also for non-technical users). Logs are everywhere. Any device, system or human can potentially produce a huge amount of information saved in logs. The amount of available logs and their semi-structured nature make a meaningful processing in real-time quite a difficult task. Thus, valuable business insights stored in logs might be not found. Kibana4Solr is a search-driven approach to handle that challenge. It offers user-friendly and browser-based dashboard which can be easily customized to particular needs. In the session the Kibana4Solr will be introduced. Some light will be shed on the architectural features of Kibana4Solr. Some ideas will be given in terms of possible business uses cases. And finally a live demo of Kibana4Solr will be shown. Configure
State of the Art Logging. Kibana4Solr is Here!
State of the Art Logging. Kibana4Solr is Here!
lucenerevolution
Search at Twitter
Search at Twitter
lucenerevolution
Presented by Daniel Beach, Search Application Developer, OpenSource Connections Solr is a powerful search engine, but creating a custom user interface can be daunting. In this fast paced session I will present an overview of how to implement a client-side search application using Solr. Using open-source frameworks like SpyGlass (to be released in September) can be a powerful way to jumpstart your development by giving you out-of-the box results views with support for faceting, autocomplete, and detail views. During this talk I will also demonstrate how we have built and deployed lightweight applications that are able to be performant under large user loads, with minimal server resources.
Building Client-side Search Applications with Solr
Building Client-side Search Applications with Solr
lucenerevolution
Presented by Timothy Potter, Founder, Text Centrix Storm is a real-time distributed computation system used to process massive streams of data. Many organizations are turning to technologies like Storm to complement batch-oriented big data technologies, such as Hadoop, to deliver time-sensitive analytics at scale. This talk introduces on an emerging architectural pattern of integrating Solr and Storm to process big data in real time. There are a number of natural integration points between Solr and Storm, such as populating a Solr index or supplying data to Storm using Solr’s real-time get support. In this session, Timothy will cover the basic concepts of Storm, such as spouts and bolts. He’ll then provide examples of how to integrate Solr into Storm to perform large-scale indexing in near real-time. In addition, we'll see how to embed Solr in a Storm bolt to match incoming tuples against pre-configured queries, commonly known as percolator. Attendees will come away from this presentation with a good introduction to stream processing technologies and several real-world use cases of how to integrate Solr with Storm.
Integrate Solr with real-time stream processing applications
Integrate Solr with real-time stream processing applications
lucenerevolution
Configure your Solr cluster to handle hundreds of millions of documents without even noticing, handle queries in milliseconds, use Near Real Time indexing and searching with document versioning. Scale your cluster both horizontally and vertically by using shards and replicas. In this session you'll learn how to make your indexing process blazing fast and make your queries efficient even with large amounts of data in your collections. You'll also see how to optimize your queries to leverage caches as much as your deployment allows and how to observe your cluster with Solr administration panel, JMX, and third party tools. Finally, learn how to make changes to already deployed collections —split their shards and alter their schema by using Solr API.
Scaling Solr with SolrCloud
Scaling Solr with SolrCloud
lucenerevolution
Presented by Rafal Kuć, Consultant and Software engineer, , Sematext Group, Inc. Even though Solr can run without causing any troubles for long periods of time it is very important to monitor and understand what is happening in your cluster. In this session you will learn how to use various tools to monitor how Solr is behaving at a high level, but also on Lucene, JVM, and operating system level. You'll see how to react to what you see and how to make changes to configuration, index structure and shards layout using Solr API. We will also discuss different performance metrics to which you ought to pay extra attention. Finally, you'll learn what to do when things go awry - we will share a few examples of troubleshooting and then dissect what was wrong and what had to be done to make things work again.
Administering and Monitoring SolrCloud Clusters
Administering and Monitoring SolrCloud Clusters
lucenerevolution
In a recent project with the United States Patent and Trademark Office, Opensource Connections was asked to prototype the next generation of patent search - using Solr and Lucene. An important aspect of this project was the implementation of BRS, a specialized search syntax used by patent examiners during the examination process. In this fast paced session we will relate our experiences and describe how we used a combination of Parboiled (a Parser Expression Grammar [PEG] parser), Lucene Queries and SpanQueries, and an extension of Solr's QParserPlugin to build BRS search functionality in Solr. First we will characterize the patent search problem and then define the BRS syntax itself. We will then introduce the Parboiled parser and discuss various considerations that one must make when designing a syntax parser. Following this we will describe the methodology used to implement the search functionality in Lucene/Solr. Finally, we will include an overview our syntactic and semantic testing strategies. The audience will leave this session with an understanding of how Solr, Lucene, and Parboiled may be used to implement their own custom search parser.
Implementing a Custom Search Syntax using Solr, Lucene, and Parboiled
Implementing a Custom Search Syntax using Solr, Lucene, and Parboiled
lucenerevolution
Many of us tend to hate or simply ignore logs, and rightfully so: they’re typically hard to find, difficult to handle, and are cryptic to the human eye. But can we make logs more valuable and more usable if we index them in Solr, so we can search and run real-time statistics on them? Indeed we can, and in this session you’ll learn how to make that happen. In the first part of the session we’ll explain why centralized logging is important, what valuable information one can extract from logs, and we’ll introduce the leading tools from the logging ecosystems everyone should be aware of - from syslog and log4j to LogStash and Flume. In the second part we’ll teach you how to use these tools in tandem with Solr. We’ll show how to use Solr in a SolrCloud setup to index large volumes of logs continuously and efficiently. Then, we'll look at how to scale the Solr cluster as your data volume grows. Finally, we'll see how you can parse your unstructured logs and convert them to nicely structured Solr documents suitable for analytical queries.
Using Solr to Search and Analyze Logs
Using Solr to Search and Analyze Logs
lucenerevolution
Enhancing relevancy through personalization & semantic search
Enhancing relevancy through personalization & semantic search
lucenerevolution
Building real-time notification systems is often limited to basic filtering and pattern matching against incoming records. Allowing users to query incoming documents using Solr's full range of capabilities is much more powerful. In our environment we needed a way to allow for tens of thousands of such query subscriptions, meaning we needed to find a way to distribute the query processing in the cloud. By creating in-memory Lucene indices from our Solr configuration, we were able to parallelize our queries across our cluster. To achieve this distribution, we wrapped the processing in a Storm topology to provide a flexible way to scale and manage our infrastructure. This presentation will describe our experiences creating this distributed, real-time inverted search notification framework.
Real-time Inverted Search in the Cloud Using Lucene and Storm
Real-time Inverted Search in the Cloud Using Lucene and Storm
lucenerevolution
Like many Web-Applications in the past, the Solr Admin UI up until 4.0 was entirely server based. It used separate code on the server to generate their Dashboards, Overviews and Statistics. All that code had to be maintained and still ... you weren't really able to use that kind of data for the things you needed it for. It was wrapped into HTML, most of the time difficult to extract and changed the structure from time to time w/o announcement. After a short look back, we're going to look into the current state of the Solr Admin UI - a client-side application, running completely in your browser. We'll see how it works, where it gets its data from and how you can get the very same data and wire that into your own custom applications, dashboards and/oder monitoring systems.
Solr's Admin UI - Where does the data come from?
Solr's Admin UI - Where does the data come from?
lucenerevolution
Steve will show how and why to use Solr’s new Schemaless Mode, under which document indexing can be performed with no up-front schema configuration. Solr uses content clues to choose among a predefined set of field types and then automatically add previously unseen fields to the schema.
Schemaless Solr and the Solr Schema REST API
Schemaless Solr and the Solr Schema REST API
lucenerevolution
Presented by Renaud Delbru, Co-Founder, SindiceTech In this presentation, we will discuss how Lucene and Solr can be used for very efficient search of tree-shaped schemaless document, e.g. JSON or XML, and can be then made to address both graph and relational data search. We will discuss the capabilities of SIREn, a Lucene/Solr plugin we have developed to deal with huge collections of tree-shaped schemaless documents, and how SIREn is built using Lucene extensibility capabilities (Analysis, Codec, Flexible Query Parser). We will compare it with Lucene's BlockJoin Query API in nested schemaless data intensive scenarios. We will then go through use cases that show how relational or graph data can be turned into JSON documents using Hadoop and Pig, and how this can be used in conjunction with SIREn to create relational faceting systems with unprecedented performance. Take-away lessons from this session will be awareness about using Lucene/Solr and Hadoop for relational and graph data search, as well as the awareness that it is now possible to have relational faceted browsers with sub-second response time on commodity hardware.
High Performance JSON Search and Relational Faceted Browsing with Lucene
High Performance JSON Search and Relational Faceted Browsing with Lucene
lucenerevolution
In this session we will show how to build a text classifier using the Apache Lucene/Solr with libSVM libraries. We classify our corpus of job offers into a number of predefined categories. Each indexed document (a job offer) then belongs to zero, one or more categories. Known machine learning techniques for text classification include naïve bayes model, logistic regression, neural network, support vector machine (SVM), etc. We use Lucene/Solr to construct the features vector. Then we use the libsvm library known as the reference implementation of the SVM model to classify the document. We construct as many one-vs-all svm classifiers as there are classes in our setting, then using the Hadoop MapReduce Framework we reconcile the result of our classifiers. The end result is a scalable multi-class classifier. Finally we outline how the classifier is used to enrich basic solr keyword search.
Text Classification with Lucene/Solr, Apache Hadoop and LibSVM
Text Classification with Lucene/Solr, Apache Hadoop and LibSVM
lucenerevolution
Faceted search is a powerful technique to let users easily navigate the search results. It can also be used to develop rich user interfaces, which give an analyst quick insights about the documents space. In this session I will introduce the Facets module, how to use it, under-the-hood details as well as optimizations and best practices. I will also describe advanced faceted search capabilities with Lucene Facets.
Faceted Search with Lucene
Faceted Search with Lucene
lucenerevolution
Presented by Shai Erera, Researcher, IBM Lucene's arsenal has recently expanded to include two new modules: Index Sorting and Replication. Index sorting lets you keep an index consistently sorted based on some criteria (e.g. modification date). This allows for efficient search early-termination as well as achieve better index compression. Index replication lets you replicate a search index to achieve high-availability, fault tolerance as well as take hot index backups. In this talk we will introduce these modules, discuss implementation and design details as well as best practices.
Recent Additions to Lucene Arsenal
Recent Additions to Lucene Arsenal
lucenerevolution
As part of their work with large media monitoring companies, Flax has developed a technique for applying tens of thousands of stored Lucene queries to a document in under a second. We'll talk about how we built intelligent filters to reduce the number of actual queries applied and how we extended Lucene to extract the exact hit positions of matches, the challenges of implementation, and how it can be used, including applications that monitor hundreds of thousands of news stories every day.
Turning search upside down
Turning search upside down
lucenerevolution
Presented by Xavier Sanchez Loro, Ph.D, Trovit Search SL This session aims to explain the implementation and use case for spellchecking in Trovit search engine. Trovit is a classified ads search engine supporting several different sites, one for each on country and vertical. Our search engine supports multiple indexes in multiple languages, each with several millions of indexed ads. Those indexes are segmented in several different sites depending on the type of ads (homes, cars, rentals, products, jobs and deals). We have developed a multi-language spellchecking system using solr and lucene in order to help our users to better find the desired ads and avoid the dreaded 0 results as much as possible. As such our goal is not pure orthographic correction, but also suggestion of correct searches for a certain site.
Spellchecking in Trovit: Implementing a Contextual Multi-language Spellchecke...
Spellchecking in Trovit: Implementing a Contextual Multi-language Spellchecke...
lucenerevolution
Shrinking the haystack wes caldwell - final
Shrinking the haystack wes caldwell - final
lucenerevolution
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Text Classification Powered by Apache Mahout and Lucene
Text Classification Powered by Apache Mahout and Lucene
State of the Art Logging. Kibana4Solr is Here!
State of the Art Logging. Kibana4Solr is Here!
Search at Twitter
Search at Twitter
Building Client-side Search Applications with Solr
Building Client-side Search Applications with Solr
Integrate Solr with real-time stream processing applications
Integrate Solr with real-time stream processing applications
Scaling Solr with SolrCloud
Scaling Solr with SolrCloud
Administering and Monitoring SolrCloud Clusters
Administering and Monitoring SolrCloud Clusters
Implementing a Custom Search Syntax using Solr, Lucene, and Parboiled
Implementing a Custom Search Syntax using Solr, Lucene, and Parboiled
Using Solr to Search and Analyze Logs
Using Solr to Search and Analyze Logs
Enhancing relevancy through personalization & semantic search
Enhancing relevancy through personalization & semantic search
Real-time Inverted Search in the Cloud Using Lucene and Storm
Real-time Inverted Search in the Cloud Using Lucene and Storm
Solr's Admin UI - Where does the data come from?
Solr's Admin UI - Where does the data come from?
Schemaless Solr and the Solr Schema REST API
Schemaless Solr and the Solr Schema REST API
High Performance JSON Search and Relational Faceted Browsing with Lucene
High Performance JSON Search and Relational Faceted Browsing with Lucene
Text Classification with Lucene/Solr, Apache Hadoop and LibSVM
Text Classification with Lucene/Solr, Apache Hadoop and LibSVM
Faceted Search with Lucene
Faceted Search with Lucene
Recent Additions to Lucene Arsenal
Recent Additions to Lucene Arsenal
Turning search upside down
Turning search upside down
Spellchecking in Trovit: Implementing a Contextual Multi-language Spellchecke...
Spellchecking in Trovit: Implementing a Contextual Multi-language Spellchecke...
Shrinking the haystack wes caldwell - final
Shrinking the haystack wes caldwell - final
Search analytics what why how - By Otis Gospodnetic
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Search Analytics What?
Why? How? Otis Gospodneti ć – Sematext International
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Data Collection
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Notas do Editor
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