Building Smarter Search Applications Using Built-In Knowledge Graphs and Quer...Lucidworks
This document discusses improving search precision through better phrase detection, such as recognizing noun phrases using autophrasing. It also describes implementing query autofiltering to map noun and verb phrases in queries to metadata fields, and providing a suggester component that leverages faceted metadata to provide contextual suggestions.
This document provides information about Boolean logic and search operators. It discusses:
- George Boole and the development of Boolean algebra for logic operations.
- The basic Boolean logic operators of AND, OR, and NOT and how they combine search terms.
- Additional Google search techniques like phrase searching, truncation, and advanced search options.
Querying your database in natural language was a presentation done during PyData Silicon Valley 2014, based on the quepy software project. More information at:
http://pydata.org/sv2014/abstracts/#197
https://github.com/machinalis/quepy
As the volume of content continues to grow exponentially helping search engines to understand context and the topical themes within your site is increasingly important. Understanding some of the concepts are covered and also ways to utilise these in your marketing strategy.
Natural Language Processing and Search Intent Understanding C3 Conductor 2019...Dawn Anderson MSc DigM
This talk looks at the ways in which search engines are evolving to understand further the nuance of linguistics in natural language processing and in understanding searcher intent.
Enhancing relevancy through personalization & semantic searchlucenerevolution
I. The document discusses how CareerBuilder uses Solr for search at scale, handling over 1 billion documents and 1 million searches per hour across 300 servers.
II. It then covers traditional relevancy scoring in Solr, which is based on TF-IDF, as well as ways to boost documents, fields, and terms.
III. Advanced relevancy techniques are described, including using custom functions to incorporate domain-specific knowledge into scoring, and context-aware weighting of relevancy parameters. Personalization and recommendation approaches are also summarized, including attribute-based and collaborative filtering methods.
This document provides an overview of advanced natural language and terms and connectors searching techniques in Westlaw. It discusses how to manipulate natural language searches by adding alternative terms, excluding terms, and conducting field searches. It also covers best practices for using terms, expanders, connectors and fields to refine terms and connectors searches, including how different connectors are processed. The document aims to help users get the most out of natural language and terms and connectors searching in Westlaw.
This document provides an overview of terms and connectors searching on Westlaw. It begins with an introduction to Boolean logic and terms and connectors searches. It then discusses constructing effective search queries using key terms, alternatives, and connectors. The document explains commonly used connectors like /p, /s, and &. It also covers other search techniques like phrase searching with quotation marks and field searching. The conclusion emphasizes that terms and connectors searching is based on simple logical rules rather than mathematics.
Building Smarter Search Applications Using Built-In Knowledge Graphs and Quer...Lucidworks
This document discusses improving search precision through better phrase detection, such as recognizing noun phrases using autophrasing. It also describes implementing query autofiltering to map noun and verb phrases in queries to metadata fields, and providing a suggester component that leverages faceted metadata to provide contextual suggestions.
This document provides information about Boolean logic and search operators. It discusses:
- George Boole and the development of Boolean algebra for logic operations.
- The basic Boolean logic operators of AND, OR, and NOT and how they combine search terms.
- Additional Google search techniques like phrase searching, truncation, and advanced search options.
Querying your database in natural language was a presentation done during PyData Silicon Valley 2014, based on the quepy software project. More information at:
http://pydata.org/sv2014/abstracts/#197
https://github.com/machinalis/quepy
As the volume of content continues to grow exponentially helping search engines to understand context and the topical themes within your site is increasingly important. Understanding some of the concepts are covered and also ways to utilise these in your marketing strategy.
Natural Language Processing and Search Intent Understanding C3 Conductor 2019...Dawn Anderson MSc DigM
This talk looks at the ways in which search engines are evolving to understand further the nuance of linguistics in natural language processing and in understanding searcher intent.
Enhancing relevancy through personalization & semantic searchlucenerevolution
I. The document discusses how CareerBuilder uses Solr for search at scale, handling over 1 billion documents and 1 million searches per hour across 300 servers.
II. It then covers traditional relevancy scoring in Solr, which is based on TF-IDF, as well as ways to boost documents, fields, and terms.
III. Advanced relevancy techniques are described, including using custom functions to incorporate domain-specific knowledge into scoring, and context-aware weighting of relevancy parameters. Personalization and recommendation approaches are also summarized, including attribute-based and collaborative filtering methods.
This document provides an overview of advanced natural language and terms and connectors searching techniques in Westlaw. It discusses how to manipulate natural language searches by adding alternative terms, excluding terms, and conducting field searches. It also covers best practices for using terms, expanders, connectors and fields to refine terms and connectors searches, including how different connectors are processed. The document aims to help users get the most out of natural language and terms and connectors searching in Westlaw.
This document provides an overview of terms and connectors searching on Westlaw. It begins with an introduction to Boolean logic and terms and connectors searches. It then discusses constructing effective search queries using key terms, alternatives, and connectors. The document explains commonly used connectors like /p, /s, and &. It also covers other search techniques like phrase searching with quotation marks and field searching. The conclusion emphasizes that terms and connectors searching is based on simple logical rules rather than mathematics.
This document provides an overview of how to effectively search for information using Google search. It discusses formulating search queries, using Boolean operators and search modifiers, filtering search results, and utilizing advanced search features. Examples of search engines, operators, and modifiers are given. Tips are provided for analyzing topics, using synonyms, describing needs concisely, and quoting phrases. Methods for saving useful websites located through searches are also outlined.
Our first presentation focusing on the basics of Boolean Searching for the Indian recruitment community. We have covered a few examples of search strings with a combination of Boolean Operators, Modifiers and field commands.
Please Note: Internet keeps changing and so the results displayed in this video may change in future.
Please follow us on Twitter:
@TheSourcePro
@SourcingAdda
You can also join our community:
http://sourcingadda.ning.com/
This document discusses search functionality in Sitecore. It provides an overview of Sitecore's ContentSearch API and how it allows indexing and searching of content in a provider-agnostic way. It also discusses important aspects of configuring analyzers and filters to customize the indexing and query process. Several examples are provided of building queries using different methods like Contains, Phrase, PredicateBuilder and boosting. The key lessons are that whole phrases, boosting, and content structure are important factors for relevance.
Techniques For Deep Query UnderstandingAbhay Prakash
The document summarizes techniques for deep query understanding in search systems. It discusses query understanding, which involves understanding a user's information need from their query. This allows for query correction, suggestion, expansion, classification and semantic tagging. Query correction reformulates ill-formed queries. Query suggestion provides similar queries. Query expansion adds synonyms to broaden results. Query classification determines the intent or topic of the query. Semantic tagging identifies entities in the query. The document outlines various models for these techniques, including using contextual information and graph representations of search logs.
1. The document discusses various techniques for searching online information, including using search engines, subject directories, and subject gateways.
2. It explains that search engines have huge databases but emphasize quantity over quality, while subject directories and subject gateways have smaller, more curated databases organized by subject.
3. Effective search strategies discussed include phrase searching, truncation, wildcards, Boolean operators, and setting limits to focus searches.
Dr Alessandro Seganti from Cognitum presented basics of Semantic Technologies, OntorionCNL, Ontorion Semantic Framework and Fluent Editor during International Conference on Computer Science -- Research and Applications IBIZA 2014, UMCS Lublin.
To learn more visit: http://www.cognitum.eu/semantics/
Introduction to Ontology Engineering with Fluent Editor 2014Cognitum
An introductory course for Ontology Engineering using Controlled Natural Language. Fluent Editor (FE) is an ontology editor that is a tool for editing and manipulating ontologies. The main feature of Fluent Editor is that it uses controlled natural language (CNL) to communicate with a user. Communication with CNL is a more suitable for human users alternative to XML-based OWL editors.
The document discusses various search engine optimization techniques for improving search results, including using Boolean operators, wildcards, quotation marks, and search modifiers. It provides examples of advanced search strings for Google, Yahoo, Bing and other search engines that incorporate these techniques to refine results by keywords, file types, locations and other filters. Tips include combining search modifiers like intitle and inurl, using wildcards to find variations, and limiting searches to specific sites, languages or regions.
The document discusses various search engine optimization techniques for improving search results, including using Boolean operators, wildcards, quotation marks, and search modifiers like intitle, inurl, and site. It provides examples of advanced search strings for finding documents related to topics like resumes, skills, and technologies. The document also compares features of search engines like Google, Yahoo, Ask, and Live.
The document discusses creating and using ontologies. It defines an ontology as a representation of things in a domain, their characteristics and relationships. Ontologies are used to share a common understanding of a domain among people and machines. They make domain assumptions and knowledge explicit and separate domain knowledge from operational knowledge. The document provides an overview of the ontology development process including requirements analysis, conceptualization, and implementation. It discusses finding existing ontologies and provides examples of competency questions for requirements analysis.
This document discusses various internet search methods including keyword searches, field searches, Boolean logic searches, and miscellaneous search methods. Keyword searches involve entering a search string or phrase. Field searches allow searching within specific fields like title or domain. Boolean logic uses operators like AND and OR to refine searches. Miscellaneous methods support different languages, spell checking, phone number searches, and math/equivalents.
The document provides an overview of basic and advanced search features available on Google search. It describes how to perform different types of searches like phrase searches, negative searches, and advanced searches using operators. It also lists other features like safe search filtering, number of results, translation, and specific searches for weather, time, calculations, book searches, and more.
This document provides a 3-part summary of how Google search works:
1. Google has spiders that build an index of web pages by screening them for relevance to search terms. It analyzes links and popularity to evaluate each site.
2. When a search is performed, Google races through its index to find pages containing the search terms, analyzes relevance, and ranks pages based on usefulness.
3. Search operators like exclusion (-), inclusion (+), and phrase matching (" ") can help refine searches by including or excluding specific words. They provide more control over search results.
This document provides an overview of the Natural Language Toolkit (NLTK), a Python library for natural language processing. It discusses NLTK's modules for common NLP tasks like tokenization, part-of-speech tagging, parsing, and classification. It also describes how NLTK can be used to analyze text corpora, frequency distributions, collocations and concordances. Key functions of NLTK include tokenizing text, accessing annotated corpora, analyzing word frequencies, part-of-speech tagging, and shallow parsing.
This tutorial discusses phrases and their use in natural language processing tasks. It defines phrases as word combinations that can express ideas not obvious from individual words alone. Unsupervised methods like mutual information and supervised techniques are used to learn good phrases from text. Phrases are useful for tasks such as named entity recognition, sentiment analysis, and solving analogies. Current research focuses on evaluating learned phrases and developing unsupervised phrase learning models.
There are many examples of text-based documents (all in ‘electronic’ format…)
e-mails, corporate Web pages, customer surveys, résumés, medical records, DNA sequences, technical papers, incident reports, news stories and more…
Not enough time or patience to read
Can we extract the most vital kernels of information…
So, we wish to find a way to gain knowledge (in summarised form) from all that text, without reading or examining them fully first…!
Some others (e.g. DNA seq.) are hard to comprehend!
What is BERT? It is Google's neural network-based technique for natural language processing (NLP) pre-training. BERT stands for Bidirectional Encoder Representations from Transformers. It was opened-sourced last year and written about in more detail on the Google AI blog. In this presentation we look at what Google BERT means for SEOs and marketers and how Google BERT is and will continue to impact the search landscape. We also look at the back story to Google BERT, including transformers and natural language understanding and computational linguistics.
The document discusses enhancing discovery with Apache Solr, Lucene, and Mahout. It provides background on these tools, describing Solr as a search server built on Lucene, and Mahout as a machine learning library for tasks like recommendations, clustering, and classification. Specifically, it outlines how Mahout can be used for collaborative filtering to provide recommendations solely based on user preferences and similarities between items. The slope one algorithm is also described as a way to generate recommendations by assuming a linear relationship between a user's ratings.
This document provides an overview of Google BERT and what it means for SEOs and marketers. Some key points:
- BERT uses bidirectional transformers to better understand the context of words in search queries and content. It helps Google resolve ambiguity and understand nuanced language.
- BERT was first introduced as an academic research paper in 2018 and was quickly adopted by Google and other major tech companies to improve natural language understanding.
- While BERT only impacts around 10% of queries, it represents a major improvement in Google's ability to understand user intent and has important implications for SEO, international search, and conversational search.
Google provides a powerful search engine that indexes web pages. It allows for various search techniques like phrase searches using quotes, Boolean logic using AND, OR and parentheses, negation using dashes, and including synonyms using tildes. Google ignores common words by default but they can be explicitly included using plus signs. The site, inurl and related syntaxes allow narrowing searches to specific sites, URLs or related pages. Number ranges and wildcards can be used. The Feeling Lucky button directly takes users to the top search result. Within-results searching allows refining an initial result set.
This document discusses using natural language processing (NLP) techniques to enable natural language search in Apache Solr. It describes integrating Apache UIMA with Solr to allow NLP algorithms to analyze documents and queries. Custom Lucene analyzers and a QParserPlugin are used to index enriched fields and extract concepts from queries. The approach aims to improve search recall and precision by understanding language.
Using OpenNLP with Solr to improve search relevance and to extract named enti...Steve Rowe
Apache OpenNLP can be used with Lucene and Solr to tag words with part-of-speech, produce lemmas (words’ base forms), and to extract named entities: people, places, organizations, etc.
This document provides an overview of how to effectively search for information using Google search. It discusses formulating search queries, using Boolean operators and search modifiers, filtering search results, and utilizing advanced search features. Examples of search engines, operators, and modifiers are given. Tips are provided for analyzing topics, using synonyms, describing needs concisely, and quoting phrases. Methods for saving useful websites located through searches are also outlined.
Our first presentation focusing on the basics of Boolean Searching for the Indian recruitment community. We have covered a few examples of search strings with a combination of Boolean Operators, Modifiers and field commands.
Please Note: Internet keeps changing and so the results displayed in this video may change in future.
Please follow us on Twitter:
@TheSourcePro
@SourcingAdda
You can also join our community:
http://sourcingadda.ning.com/
This document discusses search functionality in Sitecore. It provides an overview of Sitecore's ContentSearch API and how it allows indexing and searching of content in a provider-agnostic way. It also discusses important aspects of configuring analyzers and filters to customize the indexing and query process. Several examples are provided of building queries using different methods like Contains, Phrase, PredicateBuilder and boosting. The key lessons are that whole phrases, boosting, and content structure are important factors for relevance.
Techniques For Deep Query UnderstandingAbhay Prakash
The document summarizes techniques for deep query understanding in search systems. It discusses query understanding, which involves understanding a user's information need from their query. This allows for query correction, suggestion, expansion, classification and semantic tagging. Query correction reformulates ill-formed queries. Query suggestion provides similar queries. Query expansion adds synonyms to broaden results. Query classification determines the intent or topic of the query. Semantic tagging identifies entities in the query. The document outlines various models for these techniques, including using contextual information and graph representations of search logs.
1. The document discusses various techniques for searching online information, including using search engines, subject directories, and subject gateways.
2. It explains that search engines have huge databases but emphasize quantity over quality, while subject directories and subject gateways have smaller, more curated databases organized by subject.
3. Effective search strategies discussed include phrase searching, truncation, wildcards, Boolean operators, and setting limits to focus searches.
Dr Alessandro Seganti from Cognitum presented basics of Semantic Technologies, OntorionCNL, Ontorion Semantic Framework and Fluent Editor during International Conference on Computer Science -- Research and Applications IBIZA 2014, UMCS Lublin.
To learn more visit: http://www.cognitum.eu/semantics/
Introduction to Ontology Engineering with Fluent Editor 2014Cognitum
An introductory course for Ontology Engineering using Controlled Natural Language. Fluent Editor (FE) is an ontology editor that is a tool for editing and manipulating ontologies. The main feature of Fluent Editor is that it uses controlled natural language (CNL) to communicate with a user. Communication with CNL is a more suitable for human users alternative to XML-based OWL editors.
The document discusses various search engine optimization techniques for improving search results, including using Boolean operators, wildcards, quotation marks, and search modifiers. It provides examples of advanced search strings for Google, Yahoo, Bing and other search engines that incorporate these techniques to refine results by keywords, file types, locations and other filters. Tips include combining search modifiers like intitle and inurl, using wildcards to find variations, and limiting searches to specific sites, languages or regions.
The document discusses various search engine optimization techniques for improving search results, including using Boolean operators, wildcards, quotation marks, and search modifiers like intitle, inurl, and site. It provides examples of advanced search strings for finding documents related to topics like resumes, skills, and technologies. The document also compares features of search engines like Google, Yahoo, Ask, and Live.
The document discusses creating and using ontologies. It defines an ontology as a representation of things in a domain, their characteristics and relationships. Ontologies are used to share a common understanding of a domain among people and machines. They make domain assumptions and knowledge explicit and separate domain knowledge from operational knowledge. The document provides an overview of the ontology development process including requirements analysis, conceptualization, and implementation. It discusses finding existing ontologies and provides examples of competency questions for requirements analysis.
This document discusses various internet search methods including keyword searches, field searches, Boolean logic searches, and miscellaneous search methods. Keyword searches involve entering a search string or phrase. Field searches allow searching within specific fields like title or domain. Boolean logic uses operators like AND and OR to refine searches. Miscellaneous methods support different languages, spell checking, phone number searches, and math/equivalents.
The document provides an overview of basic and advanced search features available on Google search. It describes how to perform different types of searches like phrase searches, negative searches, and advanced searches using operators. It also lists other features like safe search filtering, number of results, translation, and specific searches for weather, time, calculations, book searches, and more.
This document provides a 3-part summary of how Google search works:
1. Google has spiders that build an index of web pages by screening them for relevance to search terms. It analyzes links and popularity to evaluate each site.
2. When a search is performed, Google races through its index to find pages containing the search terms, analyzes relevance, and ranks pages based on usefulness.
3. Search operators like exclusion (-), inclusion (+), and phrase matching (" ") can help refine searches by including or excluding specific words. They provide more control over search results.
This document provides an overview of the Natural Language Toolkit (NLTK), a Python library for natural language processing. It discusses NLTK's modules for common NLP tasks like tokenization, part-of-speech tagging, parsing, and classification. It also describes how NLTK can be used to analyze text corpora, frequency distributions, collocations and concordances. Key functions of NLTK include tokenizing text, accessing annotated corpora, analyzing word frequencies, part-of-speech tagging, and shallow parsing.
This tutorial discusses phrases and their use in natural language processing tasks. It defines phrases as word combinations that can express ideas not obvious from individual words alone. Unsupervised methods like mutual information and supervised techniques are used to learn good phrases from text. Phrases are useful for tasks such as named entity recognition, sentiment analysis, and solving analogies. Current research focuses on evaluating learned phrases and developing unsupervised phrase learning models.
There are many examples of text-based documents (all in ‘electronic’ format…)
e-mails, corporate Web pages, customer surveys, résumés, medical records, DNA sequences, technical papers, incident reports, news stories and more…
Not enough time or patience to read
Can we extract the most vital kernels of information…
So, we wish to find a way to gain knowledge (in summarised form) from all that text, without reading or examining them fully first…!
Some others (e.g. DNA seq.) are hard to comprehend!
What is BERT? It is Google's neural network-based technique for natural language processing (NLP) pre-training. BERT stands for Bidirectional Encoder Representations from Transformers. It was opened-sourced last year and written about in more detail on the Google AI blog. In this presentation we look at what Google BERT means for SEOs and marketers and how Google BERT is and will continue to impact the search landscape. We also look at the back story to Google BERT, including transformers and natural language understanding and computational linguistics.
The document discusses enhancing discovery with Apache Solr, Lucene, and Mahout. It provides background on these tools, describing Solr as a search server built on Lucene, and Mahout as a machine learning library for tasks like recommendations, clustering, and classification. Specifically, it outlines how Mahout can be used for collaborative filtering to provide recommendations solely based on user preferences and similarities between items. The slope one algorithm is also described as a way to generate recommendations by assuming a linear relationship between a user's ratings.
This document provides an overview of Google BERT and what it means for SEOs and marketers. Some key points:
- BERT uses bidirectional transformers to better understand the context of words in search queries and content. It helps Google resolve ambiguity and understand nuanced language.
- BERT was first introduced as an academic research paper in 2018 and was quickly adopted by Google and other major tech companies to improve natural language understanding.
- While BERT only impacts around 10% of queries, it represents a major improvement in Google's ability to understand user intent and has important implications for SEO, international search, and conversational search.
Google provides a powerful search engine that indexes web pages. It allows for various search techniques like phrase searches using quotes, Boolean logic using AND, OR and parentheses, negation using dashes, and including synonyms using tildes. Google ignores common words by default but they can be explicitly included using plus signs. The site, inurl and related syntaxes allow narrowing searches to specific sites, URLs or related pages. Number ranges and wildcards can be used. The Feeling Lucky button directly takes users to the top search result. Within-results searching allows refining an initial result set.
This document discusses using natural language processing (NLP) techniques to enable natural language search in Apache Solr. It describes integrating Apache UIMA with Solr to allow NLP algorithms to analyze documents and queries. Custom Lucene analyzers and a QParserPlugin are used to index enriched fields and extract concepts from queries. The approach aims to improve search recall and precision by understanding language.
Using OpenNLP with Solr to improve search relevance and to extract named enti...Steve Rowe
Apache OpenNLP can be used with Lucene and Solr to tag words with part-of-speech, produce lemmas (words’ base forms), and to extract named entities: people, places, organizations, etc.
Semantic & Multilingual Strategies in Lucene/SolrTrey Grainger
When searching on text, choosing the right CharFilters, Tokenizer, stemmers, and other TokenFilters for each supported language is critical. Additional tools of the trade include language detection through UpdateRequestProcessors, parts of speech analysis, entity extraction, stopword and synonym lists, relevancy differentiation for exact vs. stemmed vs. conceptual matches, and identification of statistically interesting phrases per language. For multilingual search, you also need to choose between several strategies such as: searching across multiple fields, using a separate collection per language combination, or combining multiple languages in a single field (custom code is required for this and will be open sourced). These all have their own strengths and weaknesses depending upon your use case. This talk will provide a tutorial (with code examples) on how to pull off each of these strategies as well as compare and contrast the different kinds of stemmers, review the precision/recall impact of stemming vs. lemmatization, and describe some techniques for extracting meaningful relationships between terms to power a semantic search experience per-language. Come learn how to build an excellent semantic and multilingual search system using the best tools and techniques Lucene/Solr has to offer!
Learning to Rank in Solr: Presented by Michael Nilsson & Diego Ceccarelli, Bl...Lucidworks
This document summarizes Bloomberg's use of machine learning for search ranking within their Solr implementation. It discusses how they process 8 million searches per day and need machine learning to automatically tune rankings over time as their index grows to 400 million documents. They use a Learning to Rank approach where features are extracted from queries and documents, training data is collected, and a ranking model is generated to optimize metrics like click-through rates. Their Solr Learning to Rank plugin allows this model to re-rank search results in Solr for improved relevance.
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.
Running Natural Language Queries on MongoDBMongoDB
This document outlines a natural language search solution. It identifies key elements in queries and converts them into connected expressions to query a MongoDB database. The solution includes a tokenizer to identify operands and operators. An expression parser uses the stream of tokens to build the equivalent MongoDB query. It supports various operators and integrates external knowledge bases to improve data intelligence. The search API acts as an endpoint for the natural language querying modules. The presentation concludes with an overview of QBurst's MongoDB expertise.
Semantic & Multilingual Strategies in Lucene/Solr: Presented by Trey Grainger...Lucidworks
This document outlines Trey Grainger's presentation on semantic and multilingual strategies in Lucene/Solr. It discusses text analysis, language-specific analysis chains, multilingual search strategies like having separate fields or indexes per language or putting all languages in one field. It also covers automatic language identification, semantic search, and concludes with a discussion of one field to handle all languages.
Webinar: Simpler Semantic Search with SolrLucidworks
Hear from Lucidworks Senior Solutions Consultant Ted Sullivan about how you can leverage Apache Solr and Lucidworks Fusion to improve semantic awareness of your search applications.
Real-Time Analytics with Solr: Presented by Yonik Seeley, ClouderaLucidworks
The document describes how Solr can be used for real-time analytics on large datasets. It discusses how Solr's inverted index, columnar storage, and multi-segment indexing enable fast search and analytics. Faceted search is described as a way to break results into buckets to filter and explore the data. The new Solr facet module aims to improve integration, performance, and ease of use for advanced analytics through faceting.
Large Scale Log Analytics with Solr: Presented by Rafał Kuć & Radu Gheorghe, ...Lucidworks
This document summarizes options for ingesting logs into Apache Solr using Logstash and rsyslog. It discusses sending logs from Logstash or rsyslog to Solr, and processing logs with Logstash, rsyslog, or using rsyslog with Redis and Logstash before indexing with Solr. Configuration examples are provided for Logstash and rsyslog to ingest logs and structure them as JSON for indexing in Solr.
Presented by Wes Caldwell, Chief Architect, ISS, Inc.
The customers in the Intelligence Community and Department of Defense that ISS services have a big data challenge. The sheer volume of data being produced and ultimately consumed by large enterprise systems has grown exponentially in a short amount of time. Providing analysts the ability to interpret meaning, and act on time-critical information is a top priority for ISS. In this session, we will explore our journey into building a search and discovery system for our customers that combines Solr, OpenNLP, and other open source technologies to enable analysts to "Shrink the Haystack" into actionable information.
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.
Galene - LinkedIn's Search Architecture: Presented by Diego Buthay & Sriram S...Lucidworks
LinkedIn's search architecture called Galene uses Lucene to index hundreds of millions of profiles. Galene improves search quality and scalability through techniques like offline indexing for complex features, live updates at fine granularity, static ranking to prioritize more popular profiles, and early termination to quickly return top results. The architecture includes base, live, and snapshot indexes to support these techniques.
This document provides an overview of Apache Solr 5 including its features like full-text search, real-time indexing, and REST APIs. It describes how to install Solr, configure cores and schemas, and run Solr in standalone and cloud modes. Details are given about indexing, querying, SolrCloud architecture with collections, shards and replicas, and best practices for production deployment.
These slide belonged to the presentation I hold to my colleagues in Göttingen as an introduction to Apache Solr open source search engine. In the structure I followed Trey Grainger and Timothy Potter excellent Solr in Action book (Manning, 2014), and I took some of the examples form there. Some others come from the examples bundeled with Solr, and from the projects I had opportunity to work with in the past (eXtensible Catalog and Europeana).
These slides don't go too deep, if you want to know more about the topic, just drop me an email, or consult with the references on the last slide.
Happy searching!
Webinar: Solr's example/files: From bin/post to /browse and BeyondLucidworks
Join Lucidworks cofounder, Sr. Solutions Architect, and Lucene/Solr committer, Erik Hatcher for a webinar to explore how to build a personal document search app with the ease and power of Solr.
Scaling SolrCloud to a Large Number of Collections: Presented by Shalin Shekh...Lucidworks
This document discusses scaling SolrCloud to support a large number of collections. It identifies four main problems in scaling: 1) large cluster state size, 2) overseer performance issues with thousands of collections, 3) difficulty moving data between collections, and 4) limitations in exporting full result sets. The document outlines solutions implemented to each problem, including splitting the cluster state, optimizing the overseer, improving data management between collections, and enabling distributed deep paging to export full result sets. Testing showed the ability to support 30 hosts, 120 nodes, 1000 collections, over 6 billion documents, and sustained performance targets.
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.
The document discusses various natural language processing (NLP) tasks including named entity recognition, entity linking, question answering, sentiment analysis, dependency parsing, and semantic role labeling. It provides examples and explanations of how each task can be approached, common challenges, and relevant datasets and resources.
Dealing with a search engine in your application - a Solr approach for beginnersElaine Naomi
This document provides an overview of using Apache Solr as a search engine for applications. It discusses some of the challenges of searching large amounts of unstructured data and introduces key concepts for information retrieval like tokenization, stopwords, stemming/lemmatization. It demonstrates how to install and configure a Solr core locally, index and search documents, and customize analyzers. The document also provides an introduction to integrating Solr with Ruby on Rails applications using REST clients.
Natural language processing (NLP) involves analyzing and understanding human language to allow interaction between computers and humans. The document outlines key steps in NLP including morphological analysis, syntactic analysis, semantic analysis, and pragmatic analysis to convert text into structured representations. It also discusses statistical NLP and real-world applications such as machine translation, question answering, and speech recognition.
Natural language processing (NLP) is introduced, including its definition, common steps like morphological analysis and syntactic analysis, and applications like information extraction and machine translation. Statistical NLP aims to perform statistical inference for NLP tasks. Real-world applications of NLP are discussed, such as automatic summarization, information retrieval, question answering and speech recognition. A demo of a free NLP application is presented at the end.
This document provides an overview of online resources for music therapy majors, including databases, search techniques, and strategies for locating information. It discusses vocabulary terms used for databases and searching, examples of keyword and alphabetical searching, and features of keyword search tools like Boolean logic operators, truncation, and proximity searching. Finally, it covers searching the internet and how web search engines and databases work.
The document provides tips and strategies for effectively searching the web using search engines or subject directories. It discusses using specific keywords, limiting keywords, quotation marks, synonyms, and refining searches. It also provides tips for evaluating websites, such as looking at the URL, perimeter of the page, and quality indicators. The document includes example search queries and answers.
A Multifaceted Look At Faceting - Ted Sullivan, LucidworksLucidworks
This document discusses using facets in Solr to facilitate relevant search. It provides an overview of facet history and how facets represent metadata that provides context about search results. Facets can be used for visualization, analytics, and understanding language semantics from text. The document argues that facets are dynamic context discovery tools that can be leveraged to find similar items and enhance search in various ways such as query autofiltering, typeahead suggestions, and text analytics.
Why Watson Won: A cognitive perspectiveJames Hendler
In this talk, we present how the Watson program, IBM's famous Jeopardy playing computer, works (based on papers published by IBM), we look at some aspects of potential scoring approaches, and we examine how Watson compares to several well known systems and some preliminary thoughts on using it in future artificial intelligence and cognitive science approaches.
Relation Extraction from the Web using Distant SupervisionIsabelle Augenstein
This paper proposes using distant supervision to extract relations from web text to populate knowledge bases without requiring manual effort. It does this by using an existing knowledge base to automatically label sentences with entity relations, training a classifier on this distant supervision data. The paper describes using statistical methods to select better training data and discard noisy examples, and shows this improves precision. It also introduces methods for integrating information across sentences which improves both precision and recall of extracted relations.
Social Tags and Music Information Retrieval (Part II)Paul Lamere
Part 2 of the slides for the Social Tags and Music Information Retrieval Tutorial - Abstract: Social Tags are free text labels that are applied to items such as artists, playlists and songs. These tags have the potential to have a positive impact on music information retrieval research. In this tutorial we describe the state of the art in commercial and research social tagging systems for music. We explore some of the motivations for tagging. We describe the factors that affect the quantity and quality of collected tags. We present a toolkit that MIR researchers can use to harvest and process tags. We look at how tags are collected and used in current commercial and research systems. We explore some of the issues and problems that are encountered when using tags. We present current MIR-related research centered on social tags and suggest possible areas of exploration for future resear
The document describes a song prediction algorithm that uses machine learning on historical setlist data from Setlist.fm to predict the songs a band will play at an upcoming concert. It trains a random forests model on over 50,000 past songs played by bands, using categories like song names, venues, tours, and cities as features. The algorithm can also integrate a user's listening history from Last.fm to personalize predictions to their top songs. Performance metrics for an Iron Maiden model are provided. Background is also given on the data scientist who created the algorithm.
This document discusses search functionality in Sitecore. It provides an overview of Sitecore's ContentSearch API and how it allows indexing and searching of content in a provider-agnostic way. It also discusses important aspects of configuring analyzers and filters, building queries, and tuning search results through techniques like boosting and phrase searching. The document uses examples to illustrate improving search relevance through refining the index configuration, queries, and content structure.
The document discusses sentiment analysis and opinion mining. It describes opinion mining as the process of analyzing text written in a natural language to classify it as positive, negative, or neutral based on the expressed sentiments. It outlines different levels of opinion mining including document, sentence, and aspect levels. It provides details on the typical architecture of an opinion mining system, including modules for preprocessing, part-of-speech tagging, aspect extraction, opinion identification, and orientation.
One of the biggest challenges in the data age is overcoming the problematic belief that data has all the answers. The truth is – data is a resource, not a solution. In order to extract valuable and actionable insights, it is necessary to ask and re-ask certain questions. This talk is about figuring out what these questions are and exposes some of the limitations of common, and seemingly intuitive, approaches to data problems. As an alternative, I introduce the concept of using human-centered design principles and an iterative process to approach what you do with Big (and small) Data. As exemplars, I will walk-through a quick informal example and a real Datascope client project to highlight the flexibility and speed of these techniques.
Spotify Discover Weekly: The machine learning behind your music recommendationsSophia Ciocca
In this presentation, I give an overview of the machine learning algorithms behind Spotify’s extraordinarily popular Discover Weekly playlist. I provide a brief introduction to what the playlist is, explain how music recommendation engines have evolved over time, then break down the three main algorithm types powering Spotify’s recommendations: (1) collaborative filtering, (2) Natural Language Processing (NLP), and (3) Raw audio analysis.
Video of the presentation can be found here: https://www.youtube.com/watch?v=PUtYNjInopA
The document discusses how content analytics can enhance search capabilities. It provides examples of how key phrases, collocations, and statistically improbable phrases can be used to power related searches, cluster results, and enable faceted search. Beyond search, these content analytics techniques can be applied to applications like product recommendations, social media analysis, and customer experience analytics.
Using data science techniques like natural language processing, a random forest model can be built to analyze hotel reviews and descriptions of dream vacations to provide personalized vacation recommendations. The model analyzes text data by converting it into structured bag-of-words representations after preprocessing like removing stop words and case. This allows building a model to predict good vacation matches despite challenges like relative word frequencies and lack of context in the text.
This document discusses how data science can be used to help people plan vacations by analyzing reviews of hotels and other destinations. It describes building a model using natural language processing techniques like bag-of-words modeling and decision trees on hotel review data to match people's descriptions of their dream vacations to the most suitable locations. Some limitations of this approach are also outlined, like not accounting for word frequency or context between words. The document promotes an online data science bootcamp for learning skills like those used in this example.
Presentation given as part of the New Models and Technology for Sharing, Access and Delivery panel at the Online Video and the Future of Television conference, Friday, September 30, 2005.
Similar to Webinar: Natural Language Search with Solr (20)
Search is the Tip of the Spear for Your B2B eCommerce StrategyLucidworks
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
Customer loyalty starts with quickly responding to your customer’s needs. When it comes to resolving open support cases, time is of the essence. Time spent searching for answers adds up and creates inefficiencies in resolving cases at scale. Relevant answers need to be a few clicks away and easily accessible for agents directly from their service console.
We will explore how Lucidworks’ Agent Insights application automatically connects agents with the correct answers and resources. You’ll learn how to:
-Configure a proactive widget in an agent’s case view page to access resources across third-party systems (such as Sharepoint, Confluence, JIRA, Zendesk, and ServiceNow).
-Easily set up query pipelines to autonomously route assets and resources that are relevant to the case-at-hand—directly to the right agent.
-Identify subject matter experts within your support data and access tribal knowledge with lightning-fast speed.
How Crate & Barrel Connects Shoppers with Relevant ProductsLucidworks
Lunch and Learn during Retail TouchPoints #RIC21 virtual event.
***
Crate & Barrel’s previous search solution couldn’t provide its shoppers with an online search and browse experience consistent with the customer-centric Crate & Barrel brand. Meanwhile, Crate & Barrel merchandisers spent the bulk of their time manually creating and maintaining search rules. The search experience impacted customer retention, loyalty, and revenue growth.
Join this lunch & learn for an interactive chat on how Crate & Barrel partnered with Lucidworks to:
-Improve search and browse by modernizing the technology stack with ML-based personalization and merchandising solutions
-Enhance the experience for both shoppers and merchandisers
-Explore signals to transform the omnichannel shopping experience
Questions? Visit https://lucidworks.com/contact/
Learn how to guide customers to relevant products using eCommerce search, hyper-personalisation, and recommendations in our ‘Best-In-Class Retail Product Discovery’ webinar.
Nowadays, shoppers want their online experience to be engaging, inspirational and fulfilling. They want to find what they’re looking for quickly and easily. If the sought after item isn’t available, they want the next best product or content surfaced to them. They want a website to understand their goals as though they were talking to a sales assistant in person, in-store.
In this webinar, we explore IMRG industry data insights and a best-in-class example of retail product discovery. You’ll learn:
- How AI can drive increased revenue through hyper-personalised experiences
- How user intent can be easily understood and results displayed immediately
- How merchandisers can be empowered to curate results and product placement – all without having to rely on IT.
Presented by:
Dave Hawkins, Principal Sales Engineer - Lucidworks
Matthew Walsh, Director of Data & Retail - IMRG
Connected Experiences Are Personalized ExperiencesLucidworks
Many companies claim personalization and omnichannel capabilities are top priorities. Few are able to deliver on those experiences.
For a recent Lucidworks-commissioned study, Forrester Consulting surveyed 350+ global business decision-makers to see what gets in the way of achieving these goals. They discovered that inefficient technology, lack of behavioral insights, and failure to tie initiatives to enterprise-wide goals are some of the most frequent blockers to personalization success.
Join guest speaker, Forrester VP and Principal Analyst, Brendan Witcher, and Lucidworks CEO, Will Hayes, to hear the results of the Forrester Consulting study, how to avoid “digital blindness,” and how to apply VoC data in real-time to delight customers with personalized experiences connected across every touchpoint.
In this webinar, you’ll learn:
- Why companies who utilize real-time customer signals report more effective personalization
- How to connect employees and customers in a shared experience through search and browse
- How Lucidworks clients Lenovo, Morgan Stanley and Red Hat fast-tracked improvements in conversion, engagement and customer satisfaction
Featuring
- Will Hayes, CEO, Lucidworks
- Brendan Witcher, VP, Principal Analyst, Forrester
Intelligent Insight Driven Policing with MC+A, Toronto Police Service and Luc...Lucidworks
Intelligent Policing. Leveraging Data to more effectively Serve Communities.
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
[Webinar] Intelligent Policing. Leveraging Data to more effectively Serve Com...Lucidworks
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
Preparing for Peak in Ecommerce | eTail Asia 2020Lucidworks
This document provides a framework for prioritizing onsite search problems and key performance indicators (KPIs) to measure for e-commerce search optimization. It recommends prioritizing fixing searches that yield no results, improving relevance of results, and reducing false positives. The most essential KPIs to measure include query latency, throughput, result relevance through click-through rates and NDCG scores. The document also provides tips for self-benchmarking search performance and examples of search performance benchmarks across nine e-commerce sites from various industries.
Accelerate The Path To Purchase With Product Discovery at Retail Innovation C...Lucidworks
Wish your conversion rates were higher? Can’t figure out how to efficiently and effectively serve all the visitors on your site? Embarrassed by the quality of your product discovery experience? The bar is high and the influx of online shopping over recent months has reminded us that the opportunities are real. We’re all deep in holiday prep, but let’s take a few minutes to think about January 2021 and beyond. How can we position ourselves for success with our customers and against our competition?
Grab your lunch and let’s dive into three strategies that need to be part of your 2021 roadmap. You don’t need an army to get there. But you do need to take action and capitalize on the shoppers abandoning the product discovery journey on your site.
In this session, attendees will find out how to:
-Take control of merchandising at scale;
-Implement hands-free search relevancy; and
-Address personalization challenges.
AI-Powered Linguistics and Search with Fusion and RosetteLucidworks
For a personalized search experience, search curation requires robust text interpretation, data enrichment, relevancy tuning and recommendations. In order to achieve this, language and entity identification are crucial.
For teams working on search applications, advanced language packages allow them to achieve greater recall without sacrificing precision.
Join us for a guided tour of our new Advanced Linguistics packages, available in Fusion, thanks to the technology partnership between Lucidworks and Basistech.
We’ll explore the application of language identification and entity extraction in the context of search, along with practical examples of personalizing search and enhancing entity extraction.
In this webinar, we’ll cover:
-How Fusion uses the Rosette Basic Linguistics and Entity Extraction packages
-Tips for improving language identification and treatment as well as data enrichment for personalization
-Speech2 demo modeling Active Recommendation
-Use Rosette’s packages with Fusion Pipelines to build custom entities for specific domain use cases
Featuring:
-Radu Miclaus, Director of Product, AI and Cloud, Lucidworks, Lucidworks
-Robert Lucarini, Senior Software Engineer, Lucidworks
-Nick Belanger, Solutions Engineer, Basis Technology
The Service Industry After COVID-19: The Soul of Service in a Virtual MomentLucidworks
Before COVID-19, almost 80% of the US workforce worked service in jobs that involve in-person interaction with strangers. Now, leaders of service organizations must reshape their offerings during the pandemic and prepare for whatever the new normal turns out to be. Our three panelists will share ideas for adapting their service businesses, now that closer-than-six-feet isn’t an option.
Join Lucidworks as we talk shop with 3 service business leaders, covering:
-Common impacts of the pandemic on service businesses (and what to do about them),
-How service teams can maintain a human touch across virtual channels, and
-Plans for the future, before and after the pandemic subsides.
Featuring
-Sara Nathan, President & CEO, AMIGOS
-Anthony Carruesco, Founder, AC Fly Fishing
-sara bradley, chef and proprietor, freight house
-Justin Sears, VP Product Marketing, Lucidworks
Webinar: Smart answers for employee and customer support after covid 19 - EuropeLucidworks
The COVID-19 pandemic has forced companies to support far more customers and employees through digital channels than ever before. Many are turning to chatbots to help meet increasing demand, but traditional rules-based approaches can’t keep up. Our new Smart Answers add-on to Lucidworks Fusion makes existing chatbots and virtual assistants more intelligent and more valuable to the people you serve.
Smart Answers for Employee and Customer Support After COVID-19Lucidworks
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
Applying AI & Search in Europe - featuring 451 ResearchLucidworks
In the current climate, it’s now more important than ever to digitally enable your workforce and customers.
Hear from Simon Taylor, VP Global Partners & Alliances, Lucidworks and Matt Aslett, Research Vice President, 451 Research to get the inside scoop on how industry leaders in Europe are developing and executing their digital transformation strategies.
In this webinar, we’ll discuss:
The top challenges and aspirations European business and technology leaders are solving using AI and search technology
Which search and AI use cases are making the biggest impact in industries such as finance, healthcare, retail and energy in Europe
What technology buyers should look for when evaluating AI and search solutions
Webinar: Accelerate Data Science with Fusion 5.1Lucidworks
This document introduces Fusion 5.1 and its new capabilities for integrating with data science tools like Tensorflow, Scikit-Learn, and Spacy.
It provides an overview of Fusion's capabilities for understanding content, users, and delivering insights at scale. The document then demonstrates Fusion's Jupyter Notebook integration for reading and writing data and running SQL queries.
Finally, it shows how Fusion integrates with Seldon Core to easily deploy machine learning models with tools like Tensorflow and Scikit-Learn. A live demo is provided of deploying a custom model and using it in Fusion's query and indexing pipelines.
Webinar: 5 Must-Have Items You Need for Your 2020 Ecommerce StrategyLucidworks
In this webinar with 451 Research, you'll understand how retailers are using AI to predict customer intent and learn which key performance metrics are used by more than 120 online retailers in Lucidworks’ 2019 Retail Benchmark Survey.
In this webinar, you’ll learn:
● What trends and opportunities are facing the ecommerce industry in 2020
● Why search is the universal path to understanding customer intent
● How large online retailers apply AI to maximize the effectiveness of their personalization efforts
Where Search Meets Science and Style Meets Savings: Nordstrom Rack's Journey ...Lucidworks
Nordstrom Rack | Hautelook curates and serves customers a wide selection of on-trend apparel, accessories, and shoes at an everyday savings of up to 75 percent off regular prices. With over a million visitors shopping across different platforms every day, and a realization that customers have become accustomed to robust and personalized search interactions, Nordstrom Rack | Hautelook launched an initiative over a year ago to provide data science-driven digital experiences to their customers.
In this session, we’ll discuss Nordstrom Rack | Hautelook’s journey of operationalizing a hefty strategy, optimizing a fickle infrastructure, and rallying troops around a single vision of building an expansible machine-learning driven product discovery engine.
The audience will learn about:
-The key technical challenges and outcomes that come with onboarding a solution
-The lessons learned of creating and executing operational design
-The use of Lucidworks Fusion to plug custom data science models into search and browse applications to understand user intent and deliver personalized experiences
Apply Knowledge Graphs and Search for Real-World Decision IntelligenceLucidworks
Knowledge graphs and machine learning are on the rise as enterprises hunt for more effective ways to connect the dots between the data and the business world. With newer technologies, the digital workplace can dramatically improve employee engagement, data-driven decisions, and actions that serve tangible business objectives.
In this webinar, you will learn
-- Introduction to knowledge graphs and where they fit in the ML landscape
-- How breakthroughs in search affect your business
-- The key features to consider when choosing a data discovery platform
-- Best practices for adopting AI-powered search, with real-world examples
Webinar: Building a Business Case for Enterprise SearchLucidworks
The document discusses building a business case for enterprise search. It notes that 85% of information is unstructured data locked in various locations and applications. Many knowledge workers spend a significant portion of their day searching across multiple systems for information. The rise of unstructured data and AI capabilities can help organizations unlock value from their information assets. Effective enterprise search powered by AI can provide real-time intelligence, personalized information, and more efficient research to help knowledge workers.
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
“An Outlook of the Ongoing and Future Relationship between Blockchain Technologies and Process-aware Information Systems.” Invited talk at the joint workshop on Blockchain for Information Systems (BC4IS) and Blockchain for Trusted Data Sharing (B4TDS), co-located with with the 36th International Conference on Advanced Information Systems Engineering (CAiSE), 3 June 2024, Limassol, Cyprus.
Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Fueling AI with Great Data with Airbyte WebinarZilliz
This talk will focus on how to collect data from a variety of sources, leveraging this data for RAG and other GenAI use cases, and finally charting your course to productionalization.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Programming Foundation Models with DSPy - Meetup SlidesZilliz
Prompting language models is hard, while programming language models is easy. In this talk, I will discuss the state-of-the-art framework DSPy for programming foundation models with its powerful optimizers and runtime constraint system.
Ocean lotus Threat actors project by John Sitima 2024 (1).pptxSitimaJohn
Ocean Lotus cyber threat actors represent a sophisticated, persistent, and politically motivated group that poses a significant risk to organizations and individuals in the Southeast Asian region. Their continuous evolution and adaptability underscore the need for robust cybersecurity measures and international cooperation to identify and mitigate the threats posed by such advanced persistent threat groups.
Taking AI to the Next Level in Manufacturing.pdfssuserfac0301
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
Best 20 SEO Techniques To Improve Website Visibility In SERPPixlogix Infotech
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
4. What I will talk about …
Why does context matter?
Phrase and contextual ambiguities in search
• Recent advances in Query Autofiltering that attack the context
problem by adding “verb/preposition” disambiguation *
Traditional ways of visualizing context in search - forging search “loops”
• Facets
• Typeahead
https://lucidworks.com/blog/2015/11/19/query-autofiltering-chapter-4-a-novel-approach-to-natural-language-processing/*
5. Adding metadata context to Suggestions using Facets
Using Pivot Facets to create semantically rich suggestions
Facets to bring user-centric context to suggestions
• Entitlements: Security trimming of suggestions
• User session context: Dynamic On-The-Fly Predictive Analytics!
What I will talk about …
6. Why Does Context Matter?
Relevance is contextual - relevant to whom under what circumstances?
Language / User Intent / Social and business factors
Ambiguities in search are often due to an failure/inability to detect context.
So, what can we do about this - or is this talk just some obvious hand-waving
BS that we’ve heard a thousand times? Hopefully, not.
But that said - maybe just a little theory first …
7. Contextual Relationships
Semantic Context - Language, Lexicon
User Context - Intent, Agendas,Permissions, Demographics, Location
Social Context - Popularity, Common Behaviors => Recommendations
Business Context - Rules, Organization, Domain, Security
Context == Relationships
Within and between metadata “objects”
Search is an exchange of one metadata object - the query - for others -
the results.
8. Things are related to other Things
Relationships provide context
• Static or known Relationships - defined by a knowledge graph
such as an Ontology
• Discovered Relationships - computed by data mining
Knowledge Graphs - connected-ness
Usage Logs (query logs, other captured events or signals) -
behavioral contexts
Clustering - unsupervised learning algorithms
Natural Language Processing - semantic contexts - noun phrases -
statements
Machine Learning - supervised learning => Feature extraction
9. Apple
Tim Cook
Times Square
Granny Smith
White Album
iPhone Macintosh Computer Tablet Steve Jobs Lisa iTunes
Broadway Wall Street Empire State Building Bronx Zoo
Pie Fritters Season Sauce Cider Picking Tree McIntosh
Records Beatles George Martin Capitol White Album
Feature Sets
10. Resolving Ambiguities
Phrase or syntactic ambiguities - detecting nouns
Autophrasing - unstructured data
Query Autofiltering - structured data
Contextual or semantic ambiguities (subject-verb-object) - detecting intent
Traditional NLP - POS detection, Machine Learning
Query Autofiltering with verb/preposition disambiguation
12. Discovery and Focus
Enough abstractions - give me some examples!
Medical Ontology
Disease
Condition Symptom
Drug
Treatment
13. Query Autofiltering
“songs Eric Clapton wrote” vs. “songs Eric Clapton performed”
Without Verb support get:
(performer_ss:”Eric Clapton” OR composer_ss:”Eric Clapton”) AND composition_type:Song
For either.
With Verb support
Now we get:
songs Eric Clapton wrote => composer_ss:”Eric Clapton” AND composition_type:Song
songs Eric Clapton performed => performer_ss:”Eric Claptpn” AND composition_type:Song
Verb/Preposition context rules
written,wrote,composed =>composer_ss
performed,played,sang,recorded:performer_ss
14. Query Autofiltering
“Bands that Eric Clapton was in”
No context rules (raw autofiltering):
((name_s:Band OR musician_type_ss:Band) AND (name_s:"Eric Clapton" OR
original_performer_s:"Eric Clapton" OR composer_ss:"Eric Clapton" OR
performer_ss:"Eric Clapton" OR groupMembers_ss:"Eric Clapton”))
Add context rule
members,member,was in,is in,who's in,who's in the,is in the,was in the =>
memberOfGroup_ss,groupMembers_ss
((name_s:Band OR musician_type_ss:Band) AND groupMembers_ss:"Eric Clapton")
Verb/Preposition context rules
15. Query Autofiltering Verb/Preposition context rules
Who’s in The Who
raw autofiltering
((name_s:"The Who" OR original_performer_s:"The Who" OR
performer_ss:"The Who" OR memberOfGroup_ss:"The Who”))
16. Query Autofiltering Verb/Preposition context rules
Who’s in The Who
raw autofiltering
((name_s:"The Who" OR original_performer_s:"The Who" OR performer_ss:
"The Who" OR memberOfGroup_ss:"The Who”))
with context rule
members,member,was in,is in,who's in,who's in the,is in the,was in the =>
memberOfGroup_ss,groupMembers_ss
query is now:
(memberOfGroup_ss:"The Who")
17. Query Autofiltering
Drugs that treat abdominal pain
treatment_type:Drug AND has_indication:”abdominal pain”
Drugs that cause abdominal pain
treatment_type:Drug AND has_side_effect:”abdominal pain”
vs.
treatment_type:Drug AND (has_indication:”abdominal pain” OR
has_side_effect:”abdominal pain”)
Verb/Preposition context rules
treat,for,indicated => has_indication
cause,produce => has_side_effect
18. Query Autofiltering
Beatles Songs covered vs Songs Beatles covered
covers by other artists of songs written by the Beatles
vs covers by Beatles of songs by other songwriters
Robert Johnson Songs that Eric Clapton covered
works the same as:
Eric Clapton covers of Robert Johnson Songs
Insomnia Drugs - are just indicated drugs
Noun-Noun Phrases
Robert Johnson Songs
Beatles Songs
Robert Johnson Songs
Insomnia Drugs
covered,covers:performer_ss | version_s:Cover |
original_performer_s:_ENTITY_,recording_type_ss:Song=>original_performer_s:_ENTITY_
19. Facets provide Context
Visualization and the search “conversation”: Discovery and Focus
• Post-query visualization- facet display - aggregated attributes of found things
• Pre-query visualization - query suggestion or typeahead - can use facets too
(stay tuned).
• The Good, The Bad and The Ugly aspects of Facets
New and Improved: Statistics, Analytics and APIs - Oh My!
• Dashboards and Dynamic Business Intelligence
• Heatmap Faceting
• Pivot Facets and Ad-Hoc Object Hierarchies - now with stats!
•JSON Facet API
20. How can we use facets to improve typeahead?
Put more precision and more context into a suggester.
=> Using metadata - guide the user to more precise queries
that we can be really GOOD at!
To do this, we can build a specialized suggester collection - then
we can use facet contexts to build semantic and behavioral
relationships within and between searches.
* Shameless Monty Python’s Flying Circus reference
And now for something completely
different! *
21. Suggester Buildware
Query Collectors or Fetchers
Gather sets of query suggestions - Interface with multiple
implementations possible
Suggester Builder
• Validates suggestions
• Adds context to suggestions using faceting
• Submits suggestion and metadata to Solr Index
Query Logs
Terms Component
Curated Lists
Pivot Facet CollectorPivot Facet Collector
Databases - SQL or Not
22. Pivot Facet Query Collector
Uses “Field Pattern Templates” to generate semantically rich suggestions
Structured data - metadata fields contain object attributes
Can combine these attributes into phrases - semantically (or not)
Machine doesn’t know semantics.
Example
Bob Jobs Accountant Cincinnati Ohio
makes sense
Ohio Accountant Jones Cincinnati Bob
doesn’t
first_name last_name occupation city state
23. Pivot Facet Query Collector
${musician_type} ${recording_type}s
${genre} ${musician_type}s
${performer} ${recording_type}s
Rolling Stones Albums
New Wave Songs
Classical Pianists
If we create Pivot Template Patterns like this:
${original_performer} ${recording_type}s covered by ${performer} (plus context)
Beatles Songs covered by Joe Cocker
We get suggestions like this:
${name}
Stuck Inside of Mobile With The Memphis Blues Again
24. Suggester Builder - validate and contextualize
• Validate - make sure that the query works
• Contextualize - use facets to acquire “aboutness” stuff
Tests the query against the content collection
“Stuck Inside of Mobile With The Memphis Blues Again”
composition_type_ss: [
"Song"
]
composer_ss: [
"Bob Dylan"
]
genre_ss: [
"Blues Rock"
"Folk Rock"
]
25. Use Cases - User Context sensitive typeahead
User Permissions: Security Trimming of Suggestions
Faceting on ACL lists of content collection - copy set of ACL values for
suggestion result set to suggester collection
=> Don’t suggest queries that return 0 results for a given user
User Behavior: Dynamic On-The-Fly Predictive Analytics
Cache context facets returned by Suggester - use as boost queries for
subsequent queries in a user session
=> System learns “what” user is looking for
26. Data Quality - Text - Metadata
Data design and curation - solve garbage in - garbage out at the
source.
More fields with more precise values - combine for
expressiveness
The Ole Structured vs Unstructured bugga-boo
Use Machine Learning / Knowledge Base Classification to add
metadata
28. (more)'Structured'Document'
Collec1on'
Query'Autofiltering'
Query'
Solr'/'Lucene''
Result'Set'
Query Autofiltering can be used as a
“normalization” layer for classification
Document)Classifica0on)Stages)
(Manual,ML,Ontology,Hybrid))
Document)Classifica0on)Stages)
(Manual,ML,Ontology,Hybrid))
Document)Classifica0on)Stages)
(Manual,ML,Ontology,Hybrid))
Metadata)Enrichment)
(more))Structured)Document)
Collec0on)–)The)Model!)
=> Can Think of the Solr/Lucene Index itself as the “Model”