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Four Big Features: * Faceting * Query auto-complete * Geospatial * Scaling Presented at a Meetup in Boston, 11 March 2014.
Solr: 4 big features
Solr: 4 big features
David Smiley
Presented by Patrick Beaucamp | Bpm-Conseil. See conference video - http://www.lucidimagination.com/devzone/events/conferences/lucene-revolution-2012 Vanilla, an Open Source business intelligence application by bpm-conseil.com, offers unique features such as report indexing through an embedded Lucene integration. Using Vanilla and Lucene, developers can manage both report indexing and external document indexing, which ultimately saves end users time when they search for specific keywords such as "product code," or "customer code." Vanilla can build upon an existing Solr/Lucene installation that takes care of all the indexing processes while Vanilla takes care of the Reporting/Dashboard creation. During this presentation, attendees will learn how we moved from embed Lucene Api to a Solr/Lucene platform and all the technical and business benefits from this architecture in terms of clustering, caching and access mode.
How to Gain Greater Business Intelligence from Lucene/Solr
How to Gain Greater Business Intelligence from Lucene/Solr
lucenerevolution
Multiple low-level speed and space optimizations for String faceting with Apache Solr, presented at Lucene/Solr Revolution 2015.
Faceting optimizations for Solr
Faceting optimizations for Solr
Toke Eskildsen
Lucene/Solr Revolution 2015
Faceting Optimizations for Solr: Presented by Toke Eskildsen, State & Univers...
Faceting Optimizations for Solr: Presented by Toke Eskildsen, State & Univers...
Lucidworks
Lucene/Solr Revolution 2015
Lessons From Sharding Solr At Etsy: Presented by Gregg Donovan, Etsy
Lessons From Sharding Solr At Etsy: Presented by Gregg Donovan, Etsy
Lucidworks
A talk given to DJUGL on the 26th July 2010, describing and introducing Solr, and discussing how we use it at Timetric to drive navigation across over a million dataseries.
Scaling search to a million pages with Solr, Python, and Django
Scaling search to a million pages with Solr, Python, and Django
tow21
SolrCloud is a set of features in Apache Solr that enable elastic scaling of search indexes using sharding and replication. In this presentation, Tim Potter will demonstrate how to provision, configure, and manage a SolrCloud cluster in Amazon EC2, using a Fabric/boto based solution for automating SolrCloud operations. Attendees will come away with a solid understanding of how to operate a large-scale Solr cluster, as well as tools to help them do it. Tim will also demonstrate these tools live during his presentation. Covered technologies, include: Apache Solr, Apache ZooKeeper, Linux, Python, Fabric, boto, Apache Kafka, Apache JMeter.
Deploying and managing SolrCloud in the cloud using the Solr Scale Toolkit
Deploying and managing SolrCloud in the cloud using the Solr Scale Toolkit
thelabdude
SolrCloud is a set of features in Apache Solr that enable elastic scaling of search indexes using sharding and replication. In this presentation, Tim Potter will provide an architectural overview of SolrCloud and highlight its most important features. Specifically, Tim covers topics such as: sharding, replication, ZooKeeper fundamentals, leaders/replicas, and failure/recovery scenarios. Any discussion of a complex distributed system would not be complete without a discussion of the CAP theorem. Mr. Potter will describe why Solr is considered a CP system and how that impacts the design of a search application.
Solr Exchange: Introduction to SolrCloud
Solr Exchange: Introduction to SolrCloud
thelabdude
Recommandé
Four Big Features: * Faceting * Query auto-complete * Geospatial * Scaling Presented at a Meetup in Boston, 11 March 2014.
Solr: 4 big features
Solr: 4 big features
David Smiley
Presented by Patrick Beaucamp | Bpm-Conseil. See conference video - http://www.lucidimagination.com/devzone/events/conferences/lucene-revolution-2012 Vanilla, an Open Source business intelligence application by bpm-conseil.com, offers unique features such as report indexing through an embedded Lucene integration. Using Vanilla and Lucene, developers can manage both report indexing and external document indexing, which ultimately saves end users time when they search for specific keywords such as "product code," or "customer code." Vanilla can build upon an existing Solr/Lucene installation that takes care of all the indexing processes while Vanilla takes care of the Reporting/Dashboard creation. During this presentation, attendees will learn how we moved from embed Lucene Api to a Solr/Lucene platform and all the technical and business benefits from this architecture in terms of clustering, caching and access mode.
How to Gain Greater Business Intelligence from Lucene/Solr
How to Gain Greater Business Intelligence from Lucene/Solr
lucenerevolution
Multiple low-level speed and space optimizations for String faceting with Apache Solr, presented at Lucene/Solr Revolution 2015.
Faceting optimizations for Solr
Faceting optimizations for Solr
Toke Eskildsen
Lucene/Solr Revolution 2015
Faceting Optimizations for Solr: Presented by Toke Eskildsen, State & Univers...
Faceting Optimizations for Solr: Presented by Toke Eskildsen, State & Univers...
Lucidworks
Lucene/Solr Revolution 2015
Lessons From Sharding Solr At Etsy: Presented by Gregg Donovan, Etsy
Lessons From Sharding Solr At Etsy: Presented by Gregg Donovan, Etsy
Lucidworks
A talk given to DJUGL on the 26th July 2010, describing and introducing Solr, and discussing how we use it at Timetric to drive navigation across over a million dataseries.
Scaling search to a million pages with Solr, Python, and Django
Scaling search to a million pages with Solr, Python, and Django
tow21
SolrCloud is a set of features in Apache Solr that enable elastic scaling of search indexes using sharding and replication. In this presentation, Tim Potter will demonstrate how to provision, configure, and manage a SolrCloud cluster in Amazon EC2, using a Fabric/boto based solution for automating SolrCloud operations. Attendees will come away with a solid understanding of how to operate a large-scale Solr cluster, as well as tools to help them do it. Tim will also demonstrate these tools live during his presentation. Covered technologies, include: Apache Solr, Apache ZooKeeper, Linux, Python, Fabric, boto, Apache Kafka, Apache JMeter.
Deploying and managing SolrCloud in the cloud using the Solr Scale Toolkit
Deploying and managing SolrCloud in the cloud using the Solr Scale Toolkit
thelabdude
SolrCloud is a set of features in Apache Solr that enable elastic scaling of search indexes using sharding and replication. In this presentation, Tim Potter will provide an architectural overview of SolrCloud and highlight its most important features. Specifically, Tim covers topics such as: sharding, replication, ZooKeeper fundamentals, leaders/replicas, and failure/recovery scenarios. Any discussion of a complex distributed system would not be complete without a discussion of the CAP theorem. Mr. Potter will describe why Solr is considered a CP system and how that impacts the design of a search application.
Solr Exchange: Introduction to SolrCloud
Solr Exchange: Introduction to SolrCloud
thelabdude
Presented at Lucene/Solr Revolution 2014
Visualize Solr Data with Banana: Presented by Andrew Thanalertvisuti, Lucidworks
Visualize Solr Data with Banana: Presented by Andrew Thanalertvisuti, Lucidworks
Lucidworks
Search engines, and Apache Solr in particular, are quickly shifting the focus away from “big data” systems storing massive amounts of raw (but largely unharnessed) content, to “smart data” systems where the most relevant and actionable content is quickly surfaced instead. Apache Solr is the blazing-fast and fault-tolerant distributed search engine leveraged by 90% of Fortune 500 companies. As a community-driven open source project, Solr brings in diverse contributions from many of the top companies in the world, particularly those for whom returning the most relevant results is mission critical. Out of the box, Solr includes advanced capabilities like learning to rank (machine-learned ranking), graph queries and distributed graph traversals, job scheduling for processing batch and streaming data workloads, the ability to build and deploy machine learning models, and a wide variety of query parsers and functions allowing you to very easily build highly relevant and domain-specific semantic search, recommendations, or personalized search experiences. These days, Solr even enables you to run SQL queries directly against it, mixing and matching the full power of Solr’s free-text, geospatial, and other search capabilities with the a prominent query language already known by most developers (and which many external systems can use to query Solr directly). Due to the community-oriented nature of Solr, the ecosystem of capabilities also spans well beyond just the core project. In this talk, we’ll also cover several other projects within the larger Apache Lucene/Solr ecosystem that further enhance Solr’s smart data capabilities: bi-directional integration of Apache Spark and Solr’s capabilities, large-scale entity extraction, semantic knowledge graphs for discovering, traversing, and scoring meaningful relationships within your data, auto-generation of domain-specific ontologies, running SPARQL queries against Solr on RDF triples, probabilistic identification of key phrases within a query or document, conceptual search leveraging Word2Vec, and even Lucidworks’ own Fusion project which extends Solr to provide an enterprise-ready smart data platform out of the box. We’ll dive into how all of these capabilities can fit within your data science toolbox, and you’ll come away with a really good feel for how to build highly relevant “smart data” applications leveraging these key technologies.
The Apache Solr Smart Data Ecosystem
The Apache Solr Smart Data Ecosystem
Trey Grainger
functional and matrix organization
functional and matrix organization
Bhavana Rohidekar
class room presentation
Matrix organization
Matrix organization
koshicatamang
Enterprise search can grow big, really big! And growing. Tens, yes hundreds of servers may be involved, locally or in the cloud. Managing this has been complex and time consuming - until now :) SolrCloud to the rescue Using the world's most popular Open Source search engine, Apache Solr™, we will show you how the new upcoming version 4.0 makes scaling search in the cloud really simple and robust. A new feature called SolrCloud adds centralized configuration, distributed indexing & searching, automatic failover, recovery and leader election. Scaling is now as simple as adding a new server to your cluster and it will find its role where it is most needed and start serving searches.
Scaling search with Solr Cloud
Scaling search with Solr Cloud
Cominvent AS
clase de esfuerzo de una masa de suelo del Ing. Pablo Cesar PERI DOMINGUEZ profesor de la Universidad Nacional de Ingenieria - Facultad de Ingenieria Civil Lima,Peru.
Esfuerzo en una masa de suelo
Esfuerzo en una masa de suelo
Carlos Vidal Pareja Ramos
THERE'S A NEW VERSION AVAILABLE: https://www.slideshare.net/ricardo.vargas/pmbok-guide-processes-flow-6th-edition-simplified-version In this simplified version of the PMBOK® Guide 5th edition Processes Flow only the 47 processes names are show, without their inputs, tools and techniques and outputs.
PMBOK® Guide 5th edition Processes Flow in English - Simplified Version
PMBOK® Guide 5th edition Processes Flow in English - Simplified Version
Ricardo Viana Vargas
Process groups and knowledge areas PMBOK 5th Edition
Process groups and knowledge areas PMBOK 5th Edition
Process groups and knowledge areas PMBOK 5th Edition
Ahmed Rami Elsherif, PMP, ITBMC
From Solr committer Mark Miller
Solr+Hadoop = Big Data Search
Solr+Hadoop = Big Data Search
Cloudera, Inc.
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Business analyst job description key roles | Business Analyst Roles
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Agile Business Analyst Responsibilities
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Voice search optimization | How to Optimize Your Website For Voice Search
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Functional testing vs non functional testing | Difference Between Functional ...
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Phishing For Beginners | Phishing Tutorial | What is Phishing | Intellipaat
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How To Prevent Cyber Attacks | Types of Cyber Attack | What is Cyber Attack |...
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How Edge Computing Works | What is Edge Computing | Edge Computing Benefits |...
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Contenu connexe
En vedette
Presented at Lucene/Solr Revolution 2014
Visualize Solr Data with Banana: Presented by Andrew Thanalertvisuti, Lucidworks
Visualize Solr Data with Banana: Presented by Andrew Thanalertvisuti, Lucidworks
Lucidworks
Search engines, and Apache Solr in particular, are quickly shifting the focus away from “big data” systems storing massive amounts of raw (but largely unharnessed) content, to “smart data” systems where the most relevant and actionable content is quickly surfaced instead. Apache Solr is the blazing-fast and fault-tolerant distributed search engine leveraged by 90% of Fortune 500 companies. As a community-driven open source project, Solr brings in diverse contributions from many of the top companies in the world, particularly those for whom returning the most relevant results is mission critical. Out of the box, Solr includes advanced capabilities like learning to rank (machine-learned ranking), graph queries and distributed graph traversals, job scheduling for processing batch and streaming data workloads, the ability to build and deploy machine learning models, and a wide variety of query parsers and functions allowing you to very easily build highly relevant and domain-specific semantic search, recommendations, or personalized search experiences. These days, Solr even enables you to run SQL queries directly against it, mixing and matching the full power of Solr’s free-text, geospatial, and other search capabilities with the a prominent query language already known by most developers (and which many external systems can use to query Solr directly). Due to the community-oriented nature of Solr, the ecosystem of capabilities also spans well beyond just the core project. In this talk, we’ll also cover several other projects within the larger Apache Lucene/Solr ecosystem that further enhance Solr’s smart data capabilities: bi-directional integration of Apache Spark and Solr’s capabilities, large-scale entity extraction, semantic knowledge graphs for discovering, traversing, and scoring meaningful relationships within your data, auto-generation of domain-specific ontologies, running SPARQL queries against Solr on RDF triples, probabilistic identification of key phrases within a query or document, conceptual search leveraging Word2Vec, and even Lucidworks’ own Fusion project which extends Solr to provide an enterprise-ready smart data platform out of the box. We’ll dive into how all of these capabilities can fit within your data science toolbox, and you’ll come away with a really good feel for how to build highly relevant “smart data” applications leveraging these key technologies.
The Apache Solr Smart Data Ecosystem
The Apache Solr Smart Data Ecosystem
Trey Grainger
functional and matrix organization
functional and matrix organization
Bhavana Rohidekar
class room presentation
Matrix organization
Matrix organization
koshicatamang
Enterprise search can grow big, really big! And growing. Tens, yes hundreds of servers may be involved, locally or in the cloud. Managing this has been complex and time consuming - until now :) SolrCloud to the rescue Using the world's most popular Open Source search engine, Apache Solr™, we will show you how the new upcoming version 4.0 makes scaling search in the cloud really simple and robust. A new feature called SolrCloud adds centralized configuration, distributed indexing & searching, automatic failover, recovery and leader election. Scaling is now as simple as adding a new server to your cluster and it will find its role where it is most needed and start serving searches.
Scaling search with Solr Cloud
Scaling search with Solr Cloud
Cominvent AS
clase de esfuerzo de una masa de suelo del Ing. Pablo Cesar PERI DOMINGUEZ profesor de la Universidad Nacional de Ingenieria - Facultad de Ingenieria Civil Lima,Peru.
Esfuerzo en una masa de suelo
Esfuerzo en una masa de suelo
Carlos Vidal Pareja Ramos
THERE'S A NEW VERSION AVAILABLE: https://www.slideshare.net/ricardo.vargas/pmbok-guide-processes-flow-6th-edition-simplified-version In this simplified version of the PMBOK® Guide 5th edition Processes Flow only the 47 processes names are show, without their inputs, tools and techniques and outputs.
PMBOK® Guide 5th edition Processes Flow in English - Simplified Version
PMBOK® Guide 5th edition Processes Flow in English - Simplified Version
Ricardo Viana Vargas
Process groups and knowledge areas PMBOK 5th Edition
Process groups and knowledge areas PMBOK 5th Edition
Process groups and knowledge areas PMBOK 5th Edition
Ahmed Rami Elsherif, PMP, ITBMC
From Solr committer Mark Miller
Solr+Hadoop = Big Data Search
Solr+Hadoop = Big Data Search
Cloudera, Inc.
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Visualize Solr Data with Banana: Presented by Andrew Thanalertvisuti, Lucidworks
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The Apache Solr Smart Data Ecosystem
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functional and matrix organization
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Matrix organization
Scaling search with Solr Cloud
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Esfuerzo en una masa de suelo
Esfuerzo en una masa de suelo
PMBOK® Guide 5th edition Processes Flow in English - Simplified Version
PMBOK® Guide 5th edition Processes Flow in English - Simplified Version
Process groups and knowledge areas PMBOK 5th Edition
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Solr+Hadoop = Big Data Search
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