Solr is a great tool to have in the data scientist toolbox. In this talk, I walk through several demos of using Solr to data science activities as well as explore various use cases for Solr and data science
This document discusses how search has evolved beyond traditional text search to support additional use cases like recommendations and analytics. It introduces LucidWorks' products like Solr and SiLK that leverage Hadoop to power search and discovery across large datasets. New features in Solr 4 like reduced memory usage and joins are highlighted. Demos are presented on applications in ecommerce, healthcare, and finance.
A 1 hour intro to search, Apache Lucene and Solr, and LucidWorks Search. Contains a quick start with LucidWorks Search and a demo using financial data (See Github prj: http://bit.ly/lws-financial) as well as some basic vocab and search explanations
Data IO: Next Generation Search with Lucene and Solr 4Grant Ingersoll
The document summarizes new features and capabilities in Lucene and Solr 4 for search. Key highlights include Lucene being faster and more memory efficient through improvements like native near real-time support and string handling. Solr 4 adds new features for search, faceting, relevance, indexing and geospatial search. It also improves capabilities for scaling Solr through distribution and dynamic scaling in SolrCloud. The document provides examples of how Lucene and Solr can be applied to problems beyond traditional search like recommendations, backups and indexing of documents.
This document summarizes how Solr and Lucidworks Fusion can be used for big data search and analytics. It discusses indexing strategies like using MapReduce, Spark, and Fusion connectors to index structured and unstructured data from HDFS. It also covers topics like Solr on HDFS, auto add replicas, security, cluster sizing, and using the lambda architecture with Spark streaming to enable real-time search over batch-processed historical data. The document promotes Lucidworks Fusion as a search platform that can handle massive scales of data, provide real-time search capabilities, and work with any data source securely.
Searching for Better Code: Presented by Grant Ingersoll, LucidworksLucidworks
The document discusses Lucidworks' Fusion product, which is a search platform that enhances Apache Solr. It provides connectors to various data sources, integrated ETL pipelines, built-in recommendations, and security features. The document outlines Fusion's architecture, demo use cases for basic and code search, and next steps for integrating additional analysis tools like OpenGrok.
Building a Real-Time News Search Engine: Presented by Ramkumar Aiyengar, Bloo...Lucidworks
The document discusses the challenges of building a news search engine at Bloomberg L.P. It describes how Bloomberg uses Apache Solr/Lucene to index millions of news stories and handle complex search queries from customers. Some key challenges discussed include optimizing searches over huge numbers of documents and metadata fields, handling arbitrarily complex queries, and developing an alerting system to notify users of new matching results. The system has been scaled up to include thousands of Solr cores distributed across data centers to efficiently search and retrieve news content.
http://sigir2013.ie/industry_track.html#GrantIngersoll
Abstract: Apache Lucene and Solr are the most widely deployed search technology on the planet, powering sites like Twitter, Wikipedia, Zappos and countless applications across a large array of domains. They are also free, open source, extensible and extremely scalable. Lucene and Solr also contain a large number of features for solving common information retrieval problems ranging from pluggable posting list compression and scoring algorithms to faceting and spell checking. Increasingly, Lucene and Solr also are being (ab)used to power applications going way beyond the search box. In this talk, we'll explore the features and capabilities of Lucene and Solr 4.x, as well as look at how to (ab)use your search engine technology for fun and profit.
Ubiquitous Solr - A Database's Not-So-Evil Twin: Presented by Ayon Sinha, Wal...Lucidworks
This document discusses how Walmart uses Apache Solr as a "not-so-evil twin" to complement their source of truth database and help scale their data infrastructure. It describes how Walmart abstracts the complexity of managing databases, caches, search queries, and messaging to provide scalable querying across database shards. The use of Solr has allowed Walmart to offload queries, recurring reads, analytics
Webinar: Rapid Solr Development with FusionLucidworks
The document discusses Lucidworks Fusion, a platform that enables rapid development of search applications using Apache Solr. It provides concise summaries of key points about Lucidworks' contributions to Solr, the features and support levels of Fusion and Solr Enterprise, the architecture of Fusion, new connectors in version 1.3 of Fusion, and instructions for downloading and starting a demo of Fusion.
Fusion is a scalable search and analytics platform built on Apache Lucene and Solr. It allows users to easily ingest and analyze large amounts of data to power machine learning, recommendations, and personalization. Fusion leverages proven frameworks like Spark and Solr to handle large datasets and scales to support billions of documents and millions of users. It provides data exploration, visualization, natural language processing and out-of-the-box recommender systems.
This document discusses building a data-driven log analysis application using LucidWorks SILK. It begins with an introduction to LucidWorks and discusses the continuum of search capabilities from enterprise search to big data search. It then describes how SILK can enable big data search across structured and unstructured data at massive scale. The solution components involve collecting log data from various sources using connectors, ingesting it into Solr, and building visualizations for analysis. It concludes with a demo and contact information.
Valentyn Kropov, Big Data Solutions Architect has recently attended "Hadoop World / Strata" – biggest and coolest Big Data conference in a World, and he can't wait to share fresh trends and topics straight from New-York. Come and learn how Hadoop cluster will help NASA to explore Mars, how Netflix build 10PB platform, what are the latest trends in Spark, to learn about newest, just announced storage engine from Cloudera called Kudu and many many more interesting stuff.
This document summarizes Sarah Guido's talk on using Apache Spark for data science at Bitly. She discusses how Bitly uses Spark to extract, explore, and model subsets of their data including decoding Bitly links, performing topic modeling using LDA, and trend detection. While Spark provides performance benefits over MapReduce for these tasks, she notes issues with Hadoop servers, JVM, and lack of documentation that must be addressed for full production usage at Bitly.
Never Stop Exploring - Pushing the Limits of Solr: Presented by Anirudha Jadh...Lucidworks
This document discusses optimizing Solr for near real-time indexing of large datasets. The author describes benchmarking different indexing configurations, finding that batching documents by time, size or number provides much higher indexing throughput than single documents. The author proposes a PID controller to dynamically adjust batching parameters based on indexing performance. Future work includes refining the PID controller, integrating it with benchmarking tools, and using it for hardware sizing.
Chicago Solr Meetup - June 10th: This Ain't Your Parents' Search EngineLucidworks (Archived)
The document discusses how search has evolved beyond traditional keyword search to include more complex tasks like recommendations, classifications, and analytics using distributed technologies like Hadoop. It provides an overview of new capabilities in Lucene/Solr like reduced memory usage, pluggable codecs, and spatial search upgrades. LucidWorks offers products like Solr and SiLK that integrate with Hadoop and provide search and analytics capabilities across distributed data.
Big Data Warehousing Meetup: Developing a super-charged NoSQL data mart using...Caserta
Big Data Warehousing Meetup: Developing a super-charged NoSQL data mart using Solr sponsored by O'Reilly Media!
Caserta Concepts shared one of their innovative DW projects using Solr. See how open source search technology can serve high performance analytic use cases. Presentation and solution walk-through given by Caserta Concepts' Joe Caserta and Elliott Cordo.
For more information, visit www.casertaconcepts.com
The United States Patent and Trademark Office wanted a simple, lightweight, yet modern and rich discovery interface for Chinese patent data. This is the story of the Global Patent Search Network, the next generation multilingual search platform for the USPTO. GPSN, http://gpsn.uspto.gov, was the first public application deployed in the cloud, and allowed a very small development team to build a discovery interface across millions of patents.
This case study will cover:
• How we leveraged Amazon Web Services platform for data ingestion, auto scaling, and deployment at a very low price compared to traditional data centers.
• We will cover some of the innovative methods for converting XML formatted data to usable information.
• Parsing through 5 TB of raw TIFF image data and converting them to modern web friendly format.
• Challenges in building a modern Single Page Application that provides a dynamic, rich user experience.
• How we built “data sharing” features into the application to allow third party systems to build additional functionality on top of GPSN.
Uber has created a Data Science Workbench to improve the productivity of its data scientists by providing scalable tools, customization, and support. The Workbench provides Jupyter notebooks for interactive coding and visualization, RStudio for rapid prototyping, and Apache Spark for distributed processing. It aims to centralize infrastructure provisioning, leverage Uber's distributed backend, enable knowledge sharing and search, and integrate with Uber's data ecosystem tools. The Workbench manages Docker containers of tools like Jupyter and RStudio running on a Mesos cluster, with files stored in a shared file system. It addresses the problems of wasted time from separate infrastructures and lack of tool standardization across Uber's data science teams.
Efficient Scalable Search in a Multi-Tenant Environment: Presented by Harry H...Lucidworks
This document discusses efficient scalable search in a multi-tenant environment. It describes Bloomberg Vault, which hosts large volumes of enterprise communications and documents for compliance. The system uses a distributed architecture with shards that are loaded on demand to serve search queries. Security is ensured by dynamically generating field values that encapsulate access permissions for each user's view of a document.
TweetMogaz - The Arabic Tweets Platform: Presented by Ahmed Adel, BADRLucidworks
The document summarizes TweetMogaz, an Arabic tweets platform developed by BADR. It describes the key modules of the system including tweets processing, indexing, event detection, archiving and analytics. The system collects and analyzes Arabic tweets in real-time using Apache Solr, identifies trending topics and events, and allows users to browse, search and visualize tweets and analytics. It addresses challenges of analyzing micro-blogs and Arabic language variations. Future work includes improving the adaptive classifier and integrating statistical processing with R.
With Search, developers and data engineers can run more relevant and responsive queries on the data in Hadoop and integrate with external tools to build custom real-time applications.
This document provides a summary of using Apache Spark for continuous analytics and optimization. It discusses using Spark for collecting data from various sources, processing the data using Spark's capabilities for streaming, machine learning and SQL queries, and reporting insights. An example use case is presented for social media analysis using Spark Streaming to process a real-time data stream from Kafka and analyze the data using both the Spark SQL and core Spark APIs in Scala.
Chris Bradford & Matt Overstreet review several Cassandra use cases we’ve encountered in state and federal government. C* solves many big data problems when storing, enriching and improving access to data.
Uof memphis nosql mike king dell v1.5 feb18Mike King
This document discusses NoSQL databases as an essential component of big data architectures. It provides an overview of Mike King's background and experience in big data. It then discusses Hadoop and common data sources. The document outlines the main types of NoSQL databases and compares their commonalities and differences. It provides guidance on which NoSQL databases may be suitable for different use cases. Finally, it presents some example use cases and problems for selecting the appropriate NoSQL database, and offers to help further discuss solution architectures.
Distributed End-to-End Drug Similarity Analytics and Visualization Workflow w...Databricks
The majority of a data scientist’s time is spent cleaning and organizing data before insights can be derived. Frequently, with large datasets, a lack of integration with visualization tools makes it hard to know what’s most interesting in the data and also creates challenges for validating numerical insights from models. Given the vast number of tools available in the ecosystem, it is hard to experiment with different tools to pick the most suitable one, especially given the complexity involved in integrating them with one’s solution.
The speakers will present an easy to use workflow that solves this integration challenge by combining various open source libraries, databases (e.g. Hive, Postgres, MySQL, HBase etc.) and visualization with distributed analytics. Intel developed a highly scalable library built over Apache Spark with novel graph, statistical and machine learning algorithms that also enhances the user experience of Apache Spark via easier to use APIs.
This session will showcase how to address the above mentioned issues for a drug similarity use case. We’ll go from ETL operations on raw drug data to deriving relevant features from the drug’s chemical structure using statistical and graph algorithms, using techniques to identify best model and parameters for this data to derive insights, and then demonstrating the ease of connectivity to different databases and visualization tools.
Solr anti-patterns discusses common issues when migrating from older Solr versions to newer ones, including improperly configured request handlers, indexing errors, and lack of configuration for threads and caching. The document provides recommendations for updating configurations to address these issues, such as using newer field types, configuring thread pools, and tuning cache sizes and refresh settings.
Webinar: Rapid Solr Development with FusionLucidworks
The document discusses Lucidworks Fusion, a platform that enables rapid development of search applications using Apache Solr. It provides concise summaries of key points about Lucidworks' contributions to Solr, the features and support levels of Fusion and Solr Enterprise, the architecture of Fusion, new connectors in version 1.3 of Fusion, and instructions for downloading and starting a demo of Fusion.
Fusion is a scalable search and analytics platform built on Apache Lucene and Solr. It allows users to easily ingest and analyze large amounts of data to power machine learning, recommendations, and personalization. Fusion leverages proven frameworks like Spark and Solr to handle large datasets and scales to support billions of documents and millions of users. It provides data exploration, visualization, natural language processing and out-of-the-box recommender systems.
This document discusses building a data-driven log analysis application using LucidWorks SILK. It begins with an introduction to LucidWorks and discusses the continuum of search capabilities from enterprise search to big data search. It then describes how SILK can enable big data search across structured and unstructured data at massive scale. The solution components involve collecting log data from various sources using connectors, ingesting it into Solr, and building visualizations for analysis. It concludes with a demo and contact information.
Valentyn Kropov, Big Data Solutions Architect has recently attended "Hadoop World / Strata" – biggest and coolest Big Data conference in a World, and he can't wait to share fresh trends and topics straight from New-York. Come and learn how Hadoop cluster will help NASA to explore Mars, how Netflix build 10PB platform, what are the latest trends in Spark, to learn about newest, just announced storage engine from Cloudera called Kudu and many many more interesting stuff.
This document summarizes Sarah Guido's talk on using Apache Spark for data science at Bitly. She discusses how Bitly uses Spark to extract, explore, and model subsets of their data including decoding Bitly links, performing topic modeling using LDA, and trend detection. While Spark provides performance benefits over MapReduce for these tasks, she notes issues with Hadoop servers, JVM, and lack of documentation that must be addressed for full production usage at Bitly.
Never Stop Exploring - Pushing the Limits of Solr: Presented by Anirudha Jadh...Lucidworks
This document discusses optimizing Solr for near real-time indexing of large datasets. The author describes benchmarking different indexing configurations, finding that batching documents by time, size or number provides much higher indexing throughput than single documents. The author proposes a PID controller to dynamically adjust batching parameters based on indexing performance. Future work includes refining the PID controller, integrating it with benchmarking tools, and using it for hardware sizing.
Chicago Solr Meetup - June 10th: This Ain't Your Parents' Search EngineLucidworks (Archived)
The document discusses how search has evolved beyond traditional keyword search to include more complex tasks like recommendations, classifications, and analytics using distributed technologies like Hadoop. It provides an overview of new capabilities in Lucene/Solr like reduced memory usage, pluggable codecs, and spatial search upgrades. LucidWorks offers products like Solr and SiLK that integrate with Hadoop and provide search and analytics capabilities across distributed data.
Big Data Warehousing Meetup: Developing a super-charged NoSQL data mart using...Caserta
Big Data Warehousing Meetup: Developing a super-charged NoSQL data mart using Solr sponsored by O'Reilly Media!
Caserta Concepts shared one of their innovative DW projects using Solr. See how open source search technology can serve high performance analytic use cases. Presentation and solution walk-through given by Caserta Concepts' Joe Caserta and Elliott Cordo.
For more information, visit www.casertaconcepts.com
The United States Patent and Trademark Office wanted a simple, lightweight, yet modern and rich discovery interface for Chinese patent data. This is the story of the Global Patent Search Network, the next generation multilingual search platform for the USPTO. GPSN, http://gpsn.uspto.gov, was the first public application deployed in the cloud, and allowed a very small development team to build a discovery interface across millions of patents.
This case study will cover:
• How we leveraged Amazon Web Services platform for data ingestion, auto scaling, and deployment at a very low price compared to traditional data centers.
• We will cover some of the innovative methods for converting XML formatted data to usable information.
• Parsing through 5 TB of raw TIFF image data and converting them to modern web friendly format.
• Challenges in building a modern Single Page Application that provides a dynamic, rich user experience.
• How we built “data sharing” features into the application to allow third party systems to build additional functionality on top of GPSN.
Uber has created a Data Science Workbench to improve the productivity of its data scientists by providing scalable tools, customization, and support. The Workbench provides Jupyter notebooks for interactive coding and visualization, RStudio for rapid prototyping, and Apache Spark for distributed processing. It aims to centralize infrastructure provisioning, leverage Uber's distributed backend, enable knowledge sharing and search, and integrate with Uber's data ecosystem tools. The Workbench manages Docker containers of tools like Jupyter and RStudio running on a Mesos cluster, with files stored in a shared file system. It addresses the problems of wasted time from separate infrastructures and lack of tool standardization across Uber's data science teams.
Efficient Scalable Search in a Multi-Tenant Environment: Presented by Harry H...Lucidworks
This document discusses efficient scalable search in a multi-tenant environment. It describes Bloomberg Vault, which hosts large volumes of enterprise communications and documents for compliance. The system uses a distributed architecture with shards that are loaded on demand to serve search queries. Security is ensured by dynamically generating field values that encapsulate access permissions for each user's view of a document.
TweetMogaz - The Arabic Tweets Platform: Presented by Ahmed Adel, BADRLucidworks
The document summarizes TweetMogaz, an Arabic tweets platform developed by BADR. It describes the key modules of the system including tweets processing, indexing, event detection, archiving and analytics. The system collects and analyzes Arabic tweets in real-time using Apache Solr, identifies trending topics and events, and allows users to browse, search and visualize tweets and analytics. It addresses challenges of analyzing micro-blogs and Arabic language variations. Future work includes improving the adaptive classifier and integrating statistical processing with R.
With Search, developers and data engineers can run more relevant and responsive queries on the data in Hadoop and integrate with external tools to build custom real-time applications.
This document provides a summary of using Apache Spark for continuous analytics and optimization. It discusses using Spark for collecting data from various sources, processing the data using Spark's capabilities for streaming, machine learning and SQL queries, and reporting insights. An example use case is presented for social media analysis using Spark Streaming to process a real-time data stream from Kafka and analyze the data using both the Spark SQL and core Spark APIs in Scala.
Chris Bradford & Matt Overstreet review several Cassandra use cases we’ve encountered in state and federal government. C* solves many big data problems when storing, enriching and improving access to data.
Uof memphis nosql mike king dell v1.5 feb18Mike King
This document discusses NoSQL databases as an essential component of big data architectures. It provides an overview of Mike King's background and experience in big data. It then discusses Hadoop and common data sources. The document outlines the main types of NoSQL databases and compares their commonalities and differences. It provides guidance on which NoSQL databases may be suitable for different use cases. Finally, it presents some example use cases and problems for selecting the appropriate NoSQL database, and offers to help further discuss solution architectures.
Distributed End-to-End Drug Similarity Analytics and Visualization Workflow w...Databricks
The majority of a data scientist’s time is spent cleaning and organizing data before insights can be derived. Frequently, with large datasets, a lack of integration with visualization tools makes it hard to know what’s most interesting in the data and also creates challenges for validating numerical insights from models. Given the vast number of tools available in the ecosystem, it is hard to experiment with different tools to pick the most suitable one, especially given the complexity involved in integrating them with one’s solution.
The speakers will present an easy to use workflow that solves this integration challenge by combining various open source libraries, databases (e.g. Hive, Postgres, MySQL, HBase etc.) and visualization with distributed analytics. Intel developed a highly scalable library built over Apache Spark with novel graph, statistical and machine learning algorithms that also enhances the user experience of Apache Spark via easier to use APIs.
This session will showcase how to address the above mentioned issues for a drug similarity use case. We’ll go from ETL operations on raw drug data to deriving relevant features from the drug’s chemical structure using statistical and graph algorithms, using techniques to identify best model and parameters for this data to derive insights, and then demonstrating the ease of connectivity to different databases and visualization tools.
Solr anti-patterns discusses common issues when migrating from older Solr versions to newer ones, including improperly configured request handlers, indexing errors, and lack of configuration for threads and caching. The document provides recommendations for updating configurations to address these issues, such as using newer field types, configuring thread pools, and tuning cache sizes and refresh settings.
Lucene 4 was recently released with key features including improved language analysis support for over 30 languages, faster indexing and storage capabilities, and pluggable similarity models. The large and diverse Lucene community is always testing to improve performance and relevance. Lucene remains an open source option for text search in applications beyond traditional search engines.
Large Scale Search, Discovery and Analytics with Hadoop, Mahout and SolrGrant Ingersoll
This document discusses large scale search, discovery, and analytics using Apache Solr, Apache Mahout, and Apache Hadoop. It provides an overview of using these tools together for an integrated system that allows for search, discovery of related content, and analytics over large datasets. It describes challenges in building such a system and achieving relevance, performance, and scalability across different components for search, discovery, and analytics functions.
Grant Ingersoll discussed using open source projects like Lucene for building an open search lab (OSL). Lucene is part of a large ecosystem of open source projects including Solr, Hadoop, Mahout, and others. It provides functionality for indexing, searching, and analyzing large amounts of data. The OSL could use a service-oriented architecture with Lucene and related projects to build a distributed, scalable system for content acquisition, storage, search and machine learning. Lucene is well-suited for information retrieval and data structure research.
Grant Ingersoll, CTO of LucidWorks, presented on new features and capabilities in Lucene 4 and Solr 4. Key highlights include major performance improvements in Lucene through optimizations like DocValues and native Near Real Time support. Solr 4 features faster indexing and querying, improved geospatial support, and enhancements to SolrCloud including transaction logging for reliability. LucidWorks is continuing to advance Lucene and Solr to provide more flexible, scalable, and robust open source search capabilities.
Presentation from March 18th, 2013 Triangle Java User Group on Taming Text. Presentation covers search, question answering, clustering, classification, named entity recognition, etc. See http://www.manning.com/ingersoll for more.
Visualize Solr Data with Banana: Presented by Andrew Thanalertvisuti, LucidworksLucidworks
Banana is a fork of Kibana that works with Apache Solr data. It uses Kibana's dashboard capabilities and ports key panels to work with Solr, providing additional capabilities like new D3.js panels. Banana aims to create rich and flexible UIs, enable rapid application development, and leverage Solr's power. To build a custom panel in Banana, you need an editor HTML file for settings, a module HTML file for display, and a module JS file containing panel logic.
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.
The document discusses the open source enterprise search platform Apache Solr. It provides an overview of Solr's features, which include powerful and scalable full-text search capabilities, real-time indexing, RESTful APIs, and support for large volumes of data. The document also compares Solr to other open source and proprietary search solutions, discusses how much data Solr can typically handle, and lists some major companies that use Solr.
Fusion 3.1 comes with exciting new features that will make your search more personal and better targeted. Join us for a webinar to learn more about Fusion's features, what's new in this release, and what's around the corner for Fusion.
Solr Under the Hood at S&P Global- Sumit Vadhera, S&P Global Lucidworks
This document summarizes S&P Global's use of Solr for search capabilities across their large datasets. It discusses how S&P Global indexes over 50 million documents into Solr monthly and handles over 5 million queries per week. It outlines challenges faced with an on-premise Solr deployment and how migrating to Solr Cloud helped address issues like performance, availability, and scalability. Next steps discussed include improving relevancy through data science, continuing to leverage new Solr features, and exploring ways to integrate machine learning into search capabilities.
This document provides a summary of the Solr search platform. It begins with introductions from the presenter and about Lucid Imagination. It then discusses what Solr is, how it works, who uses it, and its main features. The rest of the document dives deeper into topics like how Solr is configured, how to index and search data, and how to debug and customize Solr implementations. It promotes downloading and experimenting with Solr to learn more.
Analytics in Search
Many companies including Lucidworks have embraced the Kibana open source code to add visualization and analytics to enhance search management. Ravi Krishnamurthy , VP of Professional Services at Lucidworks, will show Silk, Lucid's implementation of Kibana, which provides all the capabilities of the open source code but adds enterprise-critical capabilities like authentication and security to protect restricted content.
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.
Presented on Tuesday, August 7, at the 2018 LRCN (Librarians' Registration Council of Nigeria) National Workshop on Electronic Resource Management Systems in Libraries, held at the University of Nigeria, Nsukka, Enugu State, Nigeria
This document summarizes key learnings from a presentation about SharePoint 2013 and Enterprise Search. It discusses how to run a successful search project through planning, development, testing and deployment. It also covers infrastructure needs and capacity testing findings. Additionally, it provides examples of how to customize the user experience through display templates and Front search. Methods for crawling thousands of file shares and enriching indexed content are presented. The document concludes with discussions on relevancy, managing property weighting, changing ranking models, and tuning search results.
This presentation was given in one of the DSATL Mettups in March 2018 in partnership with Southern Data Science Conference 2018 (www.southerndatascience.com)
War stories from building the Global Patent Search Network, and why Data folks need to think more about UX and Discovery, and UX folks need to think more about Data.
Solr 101 was a presentation about the Solr search platform. It introduced Solr, explaining that it is an open-source enterprise search platform built on Lucene. It covered key Solr concepts like indexing, documents, fields, queries and facets. The presentation also discussed Solr features, how it works, and how to scale Solr through techniques like multicore, replication and sharding. Finally, it provided two case studies on how Sparebank1 and Komplett implemented Solr to improve their search capabilities.
Scaling Recommendations, Semantic Search, & Data Analytics with solrTrey Grainger
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.
Let's Build an Inverted Index: Introduction to Apache Lucene/SolrSease
The University Seminar series aim to provide a basic understanding of Open Source Information Retrieval and its application in the real world through the Apache Lucene/Solr technologies.
Webinar: Site Search in an Hour with FusionLucidworks
Using Lucidworks View and Fusion 3, you can easily build and deploy site search in less than one hour. Even with multiple data sources, data transformations, and user interface development, a full enterprise search project can be completed in just an hour compared to the usual 6 months.
* Open source search with Solr/Lucene gives you the power to turn a wide range of information into fast, useful, relevant results!
* LucidWorks for Solr gives you a tested, release-stable certified distribution of open source search with enhanced tools and installation for building search apps quickly and reliably.
http://www.lucidimagination.com/How-We-Can-Help/webinar-from-search-to-found
Building Search & Recommendation EnginesTrey Grainger
In this talk, you'll learn how to build your own search and recommendation engine based on the open source Apache Lucene/Solr project. We'll dive into some of the data science behind how search engines work, covering multi-lingual text analysis, natural language processing, relevancy ranking algorithms, knowledge graphs, reflected intelligence, collaborative filtering, and other machine learning techniques used to drive relevant results for free-text queries. We'll also demonstrate how to build a recommendation engine leveraging the same platform and techniques that power search for most of the world's top companies. You'll walk away from this presentation with the toolbox you need to go and implement your very own search-based product using your own data.
Anshum Gupta is an Apache Lucene/Solr committer who works at Lucidworks. He discusses the history and capabilities of Apache Lucene, an open source information retrieval library, and Apache Solr, an enterprise search platform built on Lucene. Solr has over 8 million downloads and is used by many large companies for search capabilities including indexing, faceting, auto-complete, and scalability to handle large datasets. Major updates in Solr 5 include improved performance, security features, and analytics capabilities.
Building a Lightweight Discovery Interface for China's Patents@NYC Solr/Lucen...OpenSource Connections
The document discusses building a lightweight discovery interface for Chinese patents using Solr/Lucene. It describes parsing various patent file formats using Tika and building custom parsers. It also emphasizes the importance of making the search solution accessible by allowing users to export data and share results.
This document provides an introduction to Apache Solr, an open-source enterprise search platform built on Apache Lucene. It discusses how Solr indexes content, processes search queries, and returns results with features like faceting, spellchecking, and scaling. The document also outlines how Solr works, how to configure and use it, and examples of large companies that employ Solr for search.
Migrating to SharePoint Online - How Micosoft Does ITKaruana Gatimu
Lessons learned from our migration to SharePoint online inside Microsoft. See more content at https://www.microsoft.com/itshowcase
This document discusses scalable machine learning using Apache Hadoop and Apache Mahout. It describes what scalable machine learning means in the context of large datasets, provides examples of common machine learning use cases like search and recommendations, and outlines approaches for scaling machine learning algorithms using Hadoop. It also describes the capabilities of the Apache Mahout machine learning library for collaborative filtering, clustering, classification and other tasks on Hadoop clusters.
Large Scale Search, Discovery and Analytics in ActionGrant Ingersoll
The document discusses large scale search, discovery, and analysis. It describes how search has evolved beyond basic keyword search to require a holistic view of both user data and user interactions. It provides examples of use cases where advanced search, discovery, and analytics can provide insights from large amounts of data. Key challenges discussed include balancing performance, relevance, and operations across computation and storage systems.
Large Scale Search, Discovery and Analytics with Hadoop, Mahout and SolrGrant Ingersoll
This document discusses large scale search, discovery, and analytics using Apache Solr, Apache Mahout, and Apache Hadoop. It provides an overview of using these tools together for an integrated system that allows for search, discovery of related content, and analytics over large datasets. It describes challenges in building such a system and achieving relevance, performance, and scalability across different components for search, discovery, and analytics use cases.
The document summarizes some unexpected uses of the Apache Lucene library beyond traditional text search. In 3 sentences: Lucene can be used as a fast key-value store, to index and store content in various file formats, and for machine learning tasks like classifying unlabeled documents into predefined categories using vector space models and analyzing document similarity. It also discusses using Lucene for record linkage, question answering systems, randomized testing to improve code quality, and performance improvements in newer Lucene versions.
Starfish: A Self-tuning System for Big Data AnalyticsGrant Ingersoll
Slides from Shivnath Babu's talk at the Triangle Hadoop User Group's April 2011 meeting on Starfish. See also http://www.trihug.org
Machine learning is used widely on the web today. Apache Mahout provides scalable machine learning libraries for common tasks like recommendation, clustering, classification and pattern mining. It implements many algorithms like k-means clustering in a MapReduce framework allowing them to scale to large datasets. Mahout functionality includes collaborative filtering, document clustering, categorization and frequent pattern mining.
Intro talk for UNC School of Information and Library Science. Covers basics of Lucene and Solr as well as info on Lucene/Solr jobs, opportunities, etc.
Machine learning is used widely on the internet for applications like search, recommendations, and social networking. Apache Mahout is an open source machine learning library that provides scalable machine learning algorithms to analyze large datasets. Mahout includes algorithms for recommendations, clustering, classification, and pattern mining. Many Mahout algorithms are implemented using MapReduce to allow them to scale to large datasets on Hadoop. One example is K-means clustering, which is parallelized across MapReduce jobs to iteratively calculate cluster centroids.
Intelligent Apps with Apache Lucene, Mahout and FriendsGrant Ingersoll
This document discusses building intelligent applications using various open source tools such as Apache Lucene, Mahout, OpenNLP, and others. It defines intelligent applications as those that learn from past behavior and data to adapt and provide personalized insights. Examples of intelligent applications mentioned include Netflix and Amazon. The document then provides an overview of various tools that can be used as building blocks for different components of intelligent applications, such as acquisition, language analysis, search, organization, and user modeling. It also gives examples of how to tie these tools together in an intelligent application and provides resources for further information.
This document provides an overview of Apache Lucene, Apache Nutch, and Apache Solr for search and indexing large amounts of structured and unstructured data. It discusses how Hadoop fits into the search ecosystem for distributed indexing and querying capabilities. Key components discussed include Lucene for indexing, Nutch for web crawling and indexing, Solr for search infrastructure, and ZooKeeper for coordination across distributed search nodes.
This document provides an overview of machine learning and the Apache Mahout project. It defines machine learning and common use cases such as recommendations, classification, and pattern mining. It then describes what Mahout is, how to get started with Mahout including preparing data, and examples of algorithms like recommendations, clustering, topic modeling, and frequent pattern mining. Future plans for Mahout are also mentioned.
#StandardsGoals for 2025: Standards & certification roundup - Tech Forum 2025BookNet Canada
Book industry standards are evolving rapidly. In the first part of this session, we’ll share an overview of key developments from 2024 and the early months of 2025. Then, BookNet’s resident standards expert, Tom Richardson, and CEO, Lauren Stewart, have a forward-looking conversation about what’s next.
Link to recording, transcript, and accompanying resource: https://bnctechforum.ca/sessions/standardsgoals-for-2025-standards-certification-roundup/
Presented by BookNet Canada on May 6, 2025 with support from the Department of Canadian Heritage.
Quantum Computing Quick Research Guide by Arthur MorganArthur Morgan
This is a Quick Research Guide (QRG).
QRGs include the following:
- A brief, high-level overview of the QRG topic.
- A milestone timeline for the QRG topic.
- Links to various free online resource materials to provide a deeper dive into the QRG topic.
- Conclusion and a recommendation for at least two books available in the SJPL system on the QRG topic.
QRGs planned for the series:
- Artificial Intelligence QRG
- Quantum Computing QRG
- Big Data Analytics QRG
- Spacecraft Guidance, Navigation & Control QRG (coming 2026)
- UK Home Computing & The Birth of ARM QRG (coming 2027)
Any questions or comments?
- Please contact Arthur Morgan at [email protected].
100% human made.
Book industry standards are evolving rapidly. In the first part of this session, we’ll share an overview of key developments from 2024 and the early months of 2025. Then, BookNet’s resident standards expert, Tom Richardson, and CEO, Lauren Stewart, have a forward-looking conversation about what’s next.
Link to recording, presentation slides, and accompanying resource: https://bnctechforum.ca/sessions/standardsgoals-for-2025-standards-certification-roundup/
Presented by BookNet Canada on May 6, 2025 with support from the Department of Canadian Heritage.
IT help desk outsourcing Services can assist with that by offering availability for customers and address their IT issue promptly without breaking the bank.
UiPath Community Berlin: Orchestrator API, Swagger, and Test Manager APIUiPathCommunity
Join this UiPath Community Berlin meetup to explore the Orchestrator API, Swagger interface, and the Test Manager API. Learn how to leverage these tools to streamline automation, enhance testing, and integrate more efficiently with UiPath. Perfect for developers, testers, and automation enthusiasts!
📕 Agenda
Welcome & Introductions
Orchestrator API Overview
Exploring the Swagger Interface
Test Manager API Highlights
Streamlining Automation & Testing with APIs (Demo)
Q&A and Open Discussion
Perfect for developers, testers, and automation enthusiasts!
👉 Join our UiPath Community Berlin chapter: https://community.uipath.com/berlin/
This session streamed live on April 29, 2025, 18:00 CET.
Check out all our upcoming UiPath Community sessions at https://community.uipath.com/events/.
Role of Data Annotation Services in AI-Powered ManufacturingAndrew Leo
From predictive maintenance to robotic automation, AI is driving the future of manufacturing. But without high-quality annotated data, even the smartest models fall short.
Discover how data annotation services are powering accuracy, safety, and efficiency in AI-driven manufacturing systems.
Precision in data labeling = Precision on the production floor.
Vaibhav Gupta BAML: AI work flows without Hallucinationsjohn409870
Shipping Agents
Vaibhav Gupta
Cofounder @ Boundary
in/vaigup
boundaryml/baml
Imagine if every API call you made
failed only 5% of the time
boundaryml/baml
Imagine if every LLM call you made
failed only 5% of the time
boundaryml/baml
Imagine if every LLM call you made
failed only 5% of the time
boundaryml/baml
Fault tolerant systems are hard
but now everything must be
fault tolerant
boundaryml/baml
We need to change how we
think about these systems
Aaron Villalpando
Cofounder @ Boundary
Boundary
Combinator
boundaryml/baml
We used to write websites like this:
boundaryml/baml
But now we do this:
boundaryml/baml
Problems web dev had:
boundaryml/baml
Problems web dev had:
Strings. Strings everywhere.
boundaryml/baml
Problems web dev had:
Strings. Strings everywhere.
State management was impossible.
boundaryml/baml
Problems web dev had:
Strings. Strings everywhere.
State management was impossible.
Dynamic components? forget about it.
boundaryml/baml
Problems web dev had:
Strings. Strings everywhere.
State management was impossible.
Dynamic components? forget about it.
Reuse components? Good luck.
boundaryml/baml
Problems web dev had:
Strings. Strings everywhere.
State management was impossible.
Dynamic components? forget about it.
Reuse components? Good luck.
Iteration loops took minutes.
boundaryml/baml
Problems web dev had:
Strings. Strings everywhere.
State management was impossible.
Dynamic components? forget about it.
Reuse components? Good luck.
Iteration loops took minutes.
Low engineering rigor
boundaryml/baml
React added engineering rigor
boundaryml/baml
The syntax we use changes how we
think about problems
boundaryml/baml
We used to write agents like this:
boundaryml/baml
Problems agents have:
boundaryml/baml
Problems agents have:
Strings. Strings everywhere.
Context management is impossible.
Changing one thing breaks another.
New models come out all the time.
Iteration loops take minutes.
boundaryml/baml
Problems agents have:
Strings. Strings everywhere.
Context management is impossible.
Changing one thing breaks another.
New models come out all the time.
Iteration loops take minutes.
Low engineering rigor
boundaryml/baml
Agents need
the expressiveness of English,
but the structure of code
F*** You, Show Me The Prompt.
boundaryml/baml
<show don’t tell>
Less prompting +
More engineering
=
Reliability +
Maintainability
BAML
Sam
Greg Antonio
Chris
turned down
openai to join
ex-founder, one
of the earliest
BAML users
MIT PhD
20+ years in
compilers
made his own
database, 400k+
youtube views
Vaibhav Gupta
in/vaigup
[email protected]
boundaryml/baml
Thank you!
Andrew Marnell: Transforming Business Strategy Through Data-Driven InsightsAndrew Marnell
With expertise in data architecture, performance tracking, and revenue forecasting, Andrew Marnell plays a vital role in aligning business strategies with data insights. Andrew Marnell’s ability to lead cross-functional teams ensures businesses achieve sustainable growth and operational excellence.
HCL Nomad Web – Best Practices and Managing Multiuser Environmentspanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-nomad-web-best-practices-and-managing-multiuser-environments/
HCL Nomad Web is heralded as the next generation of the HCL Notes client, offering numerous advantages such as eliminating the need for packaging, distribution, and installation. Nomad Web client upgrades will be installed “automatically” in the background. This significantly reduces the administrative footprint compared to traditional HCL Notes clients. However, troubleshooting issues in Nomad Web present unique challenges compared to the Notes client.
Join Christoph and Marc as they demonstrate how to simplify the troubleshooting process in HCL Nomad Web, ensuring a smoother and more efficient user experience.
In this webinar, we will explore effective strategies for diagnosing and resolving common problems in HCL Nomad Web, including
- Accessing the console
- Locating and interpreting log files
- Accessing the data folder within the browser’s cache (using OPFS)
- Understand the difference between single- and multi-user scenarios
- Utilizing Client Clocking
AI and Data Privacy in 2025: Global TrendsInData Labs
In this infographic, we explore how businesses can implement effective governance frameworks to address AI data privacy. Understanding it is crucial for developing effective strategies that ensure compliance, safeguard customer trust, and leverage AI responsibly. Equip yourself with insights that can drive informed decision-making and position your organization for success in the future of data privacy.
This infographic contains:
-AI and data privacy: Key findings
-Statistics on AI data privacy in the today’s world
-Tips on how to overcome data privacy challenges
-Benefits of AI data security investments.
Keep up-to-date on how AI is reshaping privacy standards and what this entails for both individuals and organizations.
Social Media App Development Company-EmizenTechSteve Jonas
EmizenTech is a trusted Social Media App Development Company with 11+ years of experience in building engaging and feature-rich social platforms. Our team of skilled developers delivers custom social media apps tailored to your business goals and user expectations. We integrate real-time chat, video sharing, content feeds, notifications, and robust security features to ensure seamless user experiences. Whether you're creating a new platform or enhancing an existing one, we offer scalable solutions that support high performance and future growth. EmizenTech empowers businesses to connect users globally, boost engagement, and stay competitive in the digital social landscape.
The Evolution of Meme Coins A New Era for Digital Currency ppt.pdfAbi john
Analyze the growth of meme coins from mere online jokes to potential assets in the digital economy. Explore the community, culture, and utility as they elevate themselves to a new era in cryptocurrency.
Mastering Advance Window Functions in SQL.pdfSpiral Mantra
How well do you really know SQL?📊
.
.
If PARTITION BY and ROW_NUMBER() sound familiar but still confuse you, it’s time to upgrade your knowledge
And you can schedule a 1:1 call with our industry experts: https://spiralmantra.com/contact-us/ or drop us a mail at [email protected]
Spark is a powerhouse for large datasets, but when it comes to smaller data workloads, its overhead can sometimes slow things down. What if you could achieve high performance and efficiency without the need for Spark?
At S&P Global Commodity Insights, having a complete view of global energy and commodities markets enables customers to make data-driven decisions with confidence and create long-term, sustainable value. 🌍
Explore delta-rs + CDC and how these open-source innovations power lightweight, high-performance data applications beyond Spark! 🚀
4. Solr in a nutshell
8M+ total
downloads
Solr is both established & growing
250,000+
monthly downloads
Largest community of developers.
2500+open Solr jobs.
Solr most widely used search
solution on the planet.
Lucidworks
Unmatched Solr expertise.
1/3
of the active
committers
70%
of the open source
code is committed
Lucene/Solr Revolution
world’s largest open source user
conference dedicated to Lucene/Solr.
Solr has tens of thousands
of applications in production.
You use
Solr everyday.
5. Solr’s Key Features
• Full text search (Info Retr.)
• Facets/Guided Nav galore!
• Lots of data types
• Spelling, auto-complete,
highlighting
• Cursors
• More Like This
• De-duplication
• Apache Lucene
• Grouping and Joins
• Stats, expressions,
transformations and more
• Lang. Detection
• Extensible
• Massive Scale/Fault tolerance
7. It is increasingly important to know
what is important!
Corollary: The faster you know what is important, the better
11. • Feature Selection
• Analyzers for all types
• Easily get weights for terms
• Term Vectors
• Data Reduction
• Filters
• Analyzers
• Data quality tools
Feature Selection and Data Reduction
12. • Quick and dirty:
• kNN, others
• Carrot^2 integration for search result
clustering
• Integration with Mahout
• Lucene provides Bayesian classifiers
built on index
• Easily build training and test sets via
filter queries
Classification and Clustering
13. • Built in expressions, stats, function
queries make custom ranking a snap!
• Search is essentially vector * matrix
• Lucene index is a ranking optimized
matrix
• More coming!
Math
14. Clicks, tweets, ratings, locations and much more can all
be leveraged to provide high quality recommendations
to users and deeper insight for data scientists
!
Signals power relevance
Query Modification
Increase the findability of
documents and records with
automatic creation of tags, fields
and meta-data
Curate the user experience in
your application with artificial
result ranking, document
injections and obfuscation
Result ManipulationIndex Time Enrichment
Perform real time decision
making and routing in order to
map a users intention or
enterprise policy
16. • Data ingest:
• JSON, CSV, XML, Rich types (PDF, etc.), custom
• Clients for Python, R, Java, .NET and more
• http://cran.r-project.org/web/packages/solr/index.html, amongst
others
• Output formats: JSON, CSV, XML, custom
Solr and Your Tools
17. • Vector Space or Probabilistic, it’s your choice!
• Killer FST
• Wicked fast
• Pluggable compression, queries, indexing and
more
• Advanced Similarity Models
• Lang. Modeling, Divergence from Random,
more
• Easy to plug-in ranking
for Data Science