Autocomplete Multi-Language Search Using Ngram and EDismax Phrase Queries: Presented by Ivan Provalov, Netflix

Downtown SF Lucene/Solr Meetup: Developing Scalable User Search for PlayStati...

The document discusses developing a scalable user search feature for the PlayStation 4. It describes setting up a SolrCloud cluster with 300 million user documents distributed across 4 shards. Personalized search ranks results based on friendship connections by using a Lucene index to store close connections for each user. Challenges included instability in the initial Solr 4.8 cluster which was addressed through configuration changes. An upgrade to Solr 5.4 required fully reindexing the data due to schema changes.

What's New in Lucene/Solr Presented by Grant Ingersoll at SolrExchage DC

Lucidworks (Archived)

Lucene and Solr 4.8 include improvements to speed, flexibility, and scalability. Key updates include native near real-time support in Lucene, faster indexing with document writer per thread, and improved fuzzy and wildcard query processing. Solr 4 offers new faceting, geospatial, and distributed capabilities. Both projects provide easier configuration and more pluggable scoring and indexing options to improve search relevance and performance.

Cool bonsai cool - an introduction to ElasticSearch

clintongormley

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panjunyong

This document summarizes and compares the Xapian and Sphinx search engines. It discusses their key features, strengths, and weaknesses. Xapian is described as having fast search speeds, full-text search support, and low memory usage but lacks in areas like database recovery and field support. Sphinx is characterized as being faster than other solutions, integrating well with databases and NoSQL, and supporting complex queries, but its data source definitions and real-time indexing need more work. The document concludes that while Xapian is good, Sphinx appears better suited for general applications due to its capabilities.

MapQuest developed a search ahead feature for their mobile app to enable auto-complete searching across their large dataset. They used Solr and implemented various techniques to optimize performance, including custom routing, analysis during ETL, and extensive JVM tuning. Their architecture included multiple Solr clusters with different configurations. Through testing and monitoring, they were able to meet their sub-140ms response time requirement for queries.

Introduction to Apache Solr

Alexandre Rafalovitch

The ultimate guide for Elasticsearch plugins

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Practical Elasticsearch - real world use cases

Elasticsearch - a search and real-time analytics server based on Apache Lucene - is gaining a lot of popularity lately, and is being used world-wide to power many sophisticated systems. While many use it for the "standard" stuff (that is, simple full-text search and real-time log analysis), there are some really interesting usage patterns that can prove useful in many real-world scenarios. In this talk we will briefly talk about Elasticsearch and its common use-cases, and then showcase some less common use-cases leveraging Elasticsearch in an interesting and often times innovating ways.

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Organizations continue to adopt Solr because of its ability to scale to meet even the most demanding workflows. Recently, LucidWorks has been leading the effort to identify, measure, and expand the limits of Solr. As part of this effort, we've learned a few things along the way that should prove useful for any organization wanting to scale Solr. Attendees will come away with a better understanding of how sharding and replication impact performance. Also, no benchmark is useful without being repeatable; Tim will also cover how to perform similar tests using the Solr-Scale-Toolkit in Amazon EC2.

A Survey of Elasticsearch Usage

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This document discusses Elasticsearch and its uses. It outlines 6 common use cases for Elasticsearch: 1) site search, 2) related posts, 3) replacing WP_Query, 4) log analytics with Logstash, 5) content reranking, and 6) breaking the blog boundary. It also provides an overview of what Elasticsearch is, including that it is a search engine, distributed, scalable, and supports analytics and multiple languages.

Data Science with Solr and Spark

This document provides an overview of a data science conference where the keynote speaker will discuss using Apache Solr and Apache Spark together for data science applications. The speaker is the CTO of Lucidworks and will cover getting started with Solr and Spark, demoing how to index data, run analytics like clustering and classification, and more. Resources for learning more about Solr, Spark, and Lucidworks Fusion are also provided.

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Big data elasticsearch practical

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pandas.(to/from)_sql is simple but not fast

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Parallel SQL and Streaming Expressions in Apache Solr 6

Shalin Shekhar Mangar

Apache Solr is a powerful search and analytics engine with features such as full-text search, faceting, joins, sorting and capable of handling large amounts of data across a large number of servers. However, with all that power and scalability comes complexity. Solr 6 supports a Parallel SQL feature which provides a simplified, well-known interface to your data in Solr, performs key operations such as sorts and shuffling inside Solr for massive speedups, provides best-practices based query optimization and by leveraging the scalability of SolrCloud and a clever implementation, allows you to throw massive amounts of computation power behind analytical queries. In this talk, we will explore the why, what and how of Parallel SQL and its building block Streaming Expressions in Solr 6 with a hint of the exciting new developments around this feature.

Использование Elasticsearch для организации поиска по сайту

Olga Lavrentieva

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Scaling Recommendations, Semantic Search, & Data Analytics with solr

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This presentation is from the inaugural Atlanta Solr Meetup held on 2014/10/21 at Atlanta Tech Village. Description: CareerBuilder uses Solr to power their recommendation engine, semantic search, and data analytics products. They maintain an infrastructure of hundreds of Solr servers, holding over a billion documents and serving over a million queries an hour across thousands of unique search indexes. Come learn how CareerBuilder has integrated Solr into their technology platform (with assistance from Hadoop, Cassandra, and RabbitMQ) and walk through api and code examples to see how you can use Solr to implement your own real-time recommendation engine, semantic search, and data analytics solutions. Speaker: Trey Grainger is the Director of Engineering for Search & Analytics at CareerBuilder.com and is the co-author of Solr in Action (2014, Manning Publications), the comprehensive example-driven guide to Apache Solr. His search experience includes handling multi-lingual content across dozens of markets/languages, machine learning, semantic search, big data analytics, customized Lucene/Solr scoring models, data mining and recommendation systems. Trey is also the Founder of Celiaccess.com, a gluten-free search engine, and is a frequent speaker at Lucene and Solr-related conferences.

Apache Solr crash course

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The document provides an overview and agenda for an Apache Solr crash course. It discusses topics such as information retrieval, inverted indexes, metrics for evaluating IR systems, Apache Lucene, the Lucene and Solr APIs, indexing, searching, querying, filtering, faceting, highlighting, spellchecking, geospatial search, and Solr architectures including single core, multi-core, replication, and sharding. It also provides tips on performance tuning, using plugins, and developing a Solr-based search engine.

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Ramzi Alqrainy

This document provides an overview of how to retrieve information from Apache Solr. It discusses various query types and parameters including basic queries, matching multiple terms, fuzzy matching, range searches, sorting, faceting, and techniques for tuning relevance. The topics are covered through examples and explanations of Solr's query syntax and how it handles indexing and searching documents.

Elasticsearch Basics

Shifa Khan

This document provides an overview of Elasticsearch including: - Elasticsearch is a database server that is implemented using RESTful HTTP/JSON and is easily scalable. It is based on Lucene. - Features include being schema-free, real-time, easy to extend with plugins, automatic peer discovery in clusters, failover and replication, and community support. - Terminology includes index, type, document, and field which make up the data structure inside Elasticsearch. Searches can be performed across multiple indices. - Elasticsearch works using full-text searching via inverted indexing and analysis. Analysis extracts terms from text through techniques like removing stopwords, lowercase conversion, and stemming. - Elasticsearch can be accessed in a RESTful manner

Managing Your Content with Elasticsearch

Samantha Quiñones

This document provides an overview of Elasticsearch including: - Elasticsearch is a distributed, real-time search and analytics engine. It allows storing, searching, and analyzing big volumes of data in near real-time. - Documents are stored in indexes which can be queried using a RESTful API or with query languages like the Query DSL. - CRUD operations allow indexing, retrieving, updating, and deleting documents. More operations can be performed efficiently using the bulk API. - Documents are analyzed and indexed to support full-text search queries and structured queries against specific fields. Mappings and analyzers define how text is processed for searching.

Денис Головняк - Продвинутый поиск с помощью Search API

LEDC 2016

The document discusses the Search API module for Drupal. It provides an advanced search framework that abstracts from data sources and backend implementations. It allows creating searches that can index any data and integrate with Views. Extensions exist for backends like Solr, databases, and other search engines. Features include facets, autocomplete, spellcheck, and the ability to freely configure the indexing workflow. Recipes are provided for subtitle and location-based searches out of the box.

Part-of-speech Tagging for Web Search Queries Using a Large-scale Web Corpus

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Benchmarking Solr Performance at Scale

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A Survey of Elasticsearch Usage

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pandas.(to/from)_sql is simple but not fast

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Parallel SQL and Streaming Expressions in Apache Solr 6

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Tommaso Teofili

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Similar to Autocomplete Multi-Language Search Using Ngram and EDismax Phrase Queries: Presented by Ivan Provalov, Netflix

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Named Entity Recognition (NER) is foundational for many downstream NLP tasks such as Information Retrieval, Relation Extraction, Question Answering, and Knowledge Base Construction. While many high-quality pre-trained NER models exist, they usually cover a small subset of popular entities such as people, organizations, and locations. But what if we need to recognize domain specific entities such as proteins, chemical names, diseases, etc? The Open Source Named Entity Recognition for Data Scientists (NERDS) toolkit, from the Elsevier Data Science team, was built to address this need. NERDS aims to speed up development and evaluation of NER models by providing a set of NER algorithms that are callable through the familiar scikit-learn style API. The uniform interface allows reuse of code for data ingestion and evaluation, resulting in cleaner and more maintainable NER pipelines. In addition, customizing NERDS by adding new and more advanced NER models is also very easy, just a matter of implementing a standard NER Model class. Our presentation will describe the main features of NERDS, then walk through a demonstration of developing and evaluating NER models that recognize biomedical entities. We will then describe a Neural Network based NER algorithm (a Bi-LSTM seq2seq model written in Pytorch) that we will then integrate into the NERDS NER pipeline. We believe NERDS addresses a real need for building domain specific NER models quickly and efficiently. NER is an active field of research, and the hope is that this presentation will spark interest and contributions of new NER algorithms and Data Adapters from the community that can in turn help to move the field forward.

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RIILP

This document describes improving hybrid translation tools using a full-text search engine approach. It discusses using natural language processing techniques and a translation memory database indexed with ElasticSearch to improve fuzzy matching. The goal is to maximize reuse of existing human translations by handling linguistic features like string transformations, part-of-speech tagging, and tokenization.

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This document discusses different approaches to text summarization, including extractive and abstractive summarization. It presents several naive extractive algorithms using word frequency, sentence intersection scores, and graph theory. It also discusses using neural networks with encoder-decoder models and attention mechanisms for abstractive summarization. The document provides resources for practicing summarization techniques and accessing text datasets.

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Rebecca Bilbro

This document provides an overview of natural language processing (NLP) for everyday applications. It discusses common NLP tasks like summarization, machine translation, and question answering. It also covers the nuts and bolts of NLP, including ingestion, parsing, tokenization, normalization, vectorization, and machine learning techniques. Finally, it recommends several popular open-source Python tools for NLP tasks like NLTK, Scikit-Learn, Gensim, and SpaCy.

Json - ideal for data interchange

Christoph Santschi

Some features that make it well-suited for data transfer: It's simultaneously human- and machine-readable format; It has support for Unicode, allowing almost any information in any human language to be communicated; The self-documenting format that describes structure and field names as well as specific values; The strict syntax and parsing requirements that allow the necessary parsing algorithms to remain simple, efficient, and consistent; The ability to represent the most general computer science data structures: records, lists and trees.

Deep Learning Summit (DLS01-4)

Amazon Web Services

AWS provides a wide range of language tools including machine translation, summarization, speech recognition, and optical character recognition. These tools utilize sequence models with object embeddings, LSTM/CNN sequences, and attention mechanisms to efficiently process input sequences and produce structured outputs through techniques like beam search. MXNet and Gluon provide a flexible engine for building and optimizing these types of models at scale on AWS infrastructure.

Lexing and parsing

Elizabeth Smith

Lexing and parsing involves breaking down input like code, markup languages, or configuration files into individual tokens and analyzing the syntax and structure according to formal grammars. Common techniques include using lexer generators to tokenize input and parser generators to construct parse trees and abstract syntax trees based on formal grammars. While regular expressions are sometimes useful, lexers and parsers are better suited for many formal language tasks and ensure well-formed syntax.

Евгений Бобров "Powered by OSS. Масштабируемая потоковая обработка и анализ б...

Технологии с открытым исходным кодом, такие как Microsoft Orleans и ElasticSearch, - ключевые элементы архитектуры YouScan. О том, как они помогают справляться с постоянно растущими объемами данных из социальных сетей, об эволюции архитектуры YouScan, я расскажу в данном докладе.

Message:Passing - lpw 2012

Tomas Doran

This document summarizes Tomas Doran's talk about his new Message::Passing Perl library for messaging, interoperability, and log aggregation. The library provides a framework for passing messages between different messaging systems and protocols. It uses a simple model where inputs produce events as hashes, outputs consume events, and filters can both produce and consume events. The core distribution includes roles for common inputs, outputs, and filters. Adaptors in separate modules provide connectivity to protocols like ZeroMQ, AMQP, STOMP, Elasticsearch, Redis PubSub, Syslog, MongoDB, and others. The goal is to simplify building complex message processing chains across different systems. Examples are provided to illustrate passing messages between protocols.

萌典與零時政府

Au Tang

This document describes the development of an open source Mandarin Chinese dictionary called Moedict. It began as a crowdsourced effort to digitize an existing government dictionary. Volunteers scraped data, performed OCR on rare characters, and designed schemas. Apps and integrations were then built by other volunteers. The dictionary strives for open standards by using open licenses, structured JSON data, and assigning URIs to entries. Later improvements included in-browser word segmentation, mobile apps, and adding a Taiwanese bilingual dictionary. The project shows how open data and crowdsourcing can collaboratively develop public resources.

Formal machines for Streaming XML Querying

JustAnotherAbstraction

General guide in nlp

Heng-Xiu Xu

This document provides a summary of machine learning techniques for natural language processing (NLP). It introduces topics like machine learning pipelines, tokenization, bag-of-words models, topic modeling and libraries for implementing NLP models. Topic modeling is defined as a method to analyze large volumes of text by clustering words that frequently occur together into topics. The document outlines common NLP preprocessing steps and machine learning algorithms for tasks like classification and clustering.

Messaging, interoperability and log aggregation - a new framework

Tomas Doran

In this talk, I will talk about why log files are horrible, logging log lines, and more structured performance metrics from large scale production applications as well as building reliable, scaleable and flexible large scale software systems in multiple languages. Why (almost) all log formats are horrible will be explained, and why JSON is a good solution for logging will be discussed, along with a number of message queuing, middleware and network transport technologies, including STOMP, AMQP and ZeroMQ. The Message::Passing framework will be introduced, along with the logstash.net project which the perl code is interoperable with. These are pluggable frameworks in ruby/java/jruby and perl with pre-written sets of inputs, filters and outputs for many many different systems, message formats and transports. They were initially designed to be aggregators and filters of data for logging. However they are flexible enough to be used as part of your messaging middleware, or even as a replacement for centralised message queuing systems. You can have your cake and eat it too - an architecture which is flexible, extensible, scaleable and distributed. Build discrete, loosely coupled components which just pass messages to each other easily. Integrate and interoperate with your existing code and code bases easily, consume from or publish to any existing message queue, logging or performance metrics system you have installed. Simple examples using common input and output classes will be demonstrated using the framework, as will easily adding your own custom filters. A number of common messaging middleware patterns will be shown to be trivial to implement. Some higher level use-cases will also be explored, demonstrating log indexing in ElasticSearch and how to build a responsive platform API using webhooks. Interoperability is also an important goal for messaging middleware. The logstash.net project will be highlighted and we'll discuss crossing the single language barrier, allowing us to have full integration between java, ruby and perl components, and to easily write bindings into libraries we want to reuse in any of those languages.

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With ecommerce experiencing explosive growth, it seems intuitive that the B2B segment of that ecosystem is mirroring the same trajectory. That said, B2B has very different needs when it comes to transacting with the same style of experiences that we see in B2C. For instance, B2B ecommerce is about precision findability, whereas B2C customers can convert at higher rates when they’re just browsing online. In order for the B2B buying experience to be successful, search needs to be tuned to meet the unique needs of the segment. In this webinar with Forrester senior analyst Joe Cicman, you’ll learn: -Which verticals in B2B will drive the most growth, and how machine-learning powered personalization tactics can be deployed to support those specific verticals -Why an omnichannel selling approach must be deployed in order to see success in B2B -How deploying content search capabilities will support a longer sales cycle at scale -What the next steps are to support a robust B2B commerce strategy supported by new technology Speakers Joe Cicman, Senior Analyst, Forrester Jenny Gomez, VP of Marketing, Lucidworks

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Policing in the next decade is anticipated to be very different from historical methods. More data driven, more focused on the intricacies of communities they serve and more open and collaborative to make informed recommendations a reality. Whether its social populations, NIBRS or organization improvement that’s the driver, the IT requirement is largely the same. Provide 360 access to large volumes of siloed data to gain a full 360 understanding of existing connections and patterns for improved insight and recommendation. Join us for a round table discussion of how the Toronto Police Service is better serving their community through deploying a unified intelligent data platform. Data innovation improves officers' engagement with existing data and streamlines investigation workflows by enhancing collaboration. This improved visibility into existing police data allows for a more intelligent and responsive police force. In this webinar, we'll cover: The technology needs of an intelligent police force. How a Global Search improves an officer's interaction with existing data. Featuring -Simon Taylor, VP, Worldwide Channels & Alliances, Lucidworks -Michael Cizmar, Managing Director, MC+A -Ian Williams, Manager of Analytics & Innovation, Toronto Police Service

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Watch our on-demand webinar showcasing Smart Answers on Lucidworks Fusion. This technology makes existing chatbots and virtual assistants more intelligent and more valuable to the people you serve. In this webinar, we’ll cover off: -How search and deep learning extend conversational frameworks for improved experiences -How Smart Answers improves customer care, call deflection, and employee self-service -A live demo of Smart Answers for multi-channel self-service support

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