You’re Solr powered, and needing to customize its capabilities. Apache Solr is flexibly architected, with practically everything pluggable. Under the hood, Solr is driven by the well-known Apache Lucene. Lucene for Solr Developers will guide you through the various ways in which Solr can be extended, customized, and enhanced with a bit of Lucene API know-how. We’ll delve into improving analysis with custom character mapping, tokenizing, and token filtering extensions; show why and how to implement specialized query parsing, and how to add your own search and update request handling.
SolrTM is the popular, blazing fast open Source Enterprise search platform from the Apache LuceneTM project. Its major features include powerful full-text search, hit highlighting, faceted search, near real-time indexing, dynamic clustering, database integration, rich document (e.g., Word, PDF) handling, and geospatial search. Solr is highly reliable, scalable and fault tolerant, providing distributed indexing, replication and load-balanced querying, automated failover and recovery, centralized configuration and more. Solr powers the search and navigation features of many of the world's largest internet sites like (Aol, Yahoo, Buy.com, Cnet, CitySearch, Netflix, Zappos, Stubhub!, digg, eTrade, Disney, Apple, NASA and MTV).
Apache Solr! Enterprise Search Solutions at your Fingertips!Murshed Ahmmad Khan
Get an overview of Apache Solr as an enterprise search server. Get to know the available alternatives and why the Solr is cool! Get Excited! Enterprise Search Solutions are ready to pick.
Apache Solr serves search requests at the enterprises and the largest companies around the world. Built on top of the top-notch Apache Lucene library, Solr makes indexing and searching integration into your applications straightforward. Solr provides faceted navigation, spell checking, highlighting, clustering, grouping, and other search features. Solr also scales query volume with replication and collection size with distributed capabilities. Solr can index rich documents such as PDF, Word, HTML, and other file types.
Come learn how you can get your content into Solr and integrate it into your applications!
This session will introduce and demonstrate several techniques for enhancing the search experience by augmenting documents during indexing. First we'll survey the analysis components available in Solr, and then we'll delve into using Solr's update processing pipeline to modify documents on the way in. The session will build on Erik's "Poor Man's Entity Extraction" blog at http://www.searchhub.org/2013/06/27/poor-mans-entity-extraction-with-solr/
SolrTM is the popular, blazing fast open Source Enterprise search platform from the Apache LuceneTM project. Its major features include powerful full-text search, hit highlighting, faceted search, near real-time indexing, dynamic clustering, database integration, rich document (e.g., Word, PDF) handling, and geospatial search. Solr is highly reliable, scalable and fault tolerant, providing distributed indexing, replication and load-balanced querying, automated failover and recovery, centralized configuration and more. Solr powers the search and navigation features of many of the world's largest internet sites like (Aol, Yahoo, Buy.com, Cnet, CitySearch, Netflix, Zappos, Stubhub!, digg, eTrade, Disney, Apple, NASA and MTV).
Apache Solr! Enterprise Search Solutions at your Fingertips!Murshed Ahmmad Khan
Get an overview of Apache Solr as an enterprise search server. Get to know the available alternatives and why the Solr is cool! Get Excited! Enterprise Search Solutions are ready to pick.
Apache Solr serves search requests at the enterprises and the largest companies around the world. Built on top of the top-notch Apache Lucene library, Solr makes indexing and searching integration into your applications straightforward. Solr provides faceted navigation, spell checking, highlighting, clustering, grouping, and other search features. Solr also scales query volume with replication and collection size with distributed capabilities. Solr can index rich documents such as PDF, Word, HTML, and other file types.
Come learn how you can get your content into Solr and integrate it into your applications!
This session will introduce and demonstrate several techniques for enhancing the search experience by augmenting documents during indexing. First we'll survey the analysis components available in Solr, and then we'll delve into using Solr's update processing pipeline to modify documents on the way in. The session will build on Erik's "Poor Man's Entity Extraction" blog at http://www.searchhub.org/2013/06/27/poor-mans-entity-extraction-with-solr/
Got data? Let's make it searchable! This presentation will demonstrate getting documents into Solr quickly, will provide some tips in adjusting Solr's schema to match your needs better, and finally will discuss how to showcase your data in a flexible search user interface. We'll see how to rapidly leverage faceting, highlighting, spell checking, and debugging. Even after all that, there will be enough time left to outline the next steps in developing your search application and taking it to production.
The talk presents the sfSolrPlugin which transparently integrates the Solr search engine into symfony.
The talk explains :
* the features of the solr search engine
* how to integrate the search engine into symfony
* complex search : faceted and geolocalized search
* usage example : http://www.menugourmet.com and http://resolutionfinder.org
The next major release of Solr is right around the corner! Join Solr Committer Cassandra Targett and Lucidworks SVP of Engineering Trey Grainger for a first look into what’s included in the upcoming release.
Overview of Solr 6.2 examples, including features they have and challenges they present. A contrasting demonstration of a minimal viable example. A step-by-step deconstruction of "films" example to show what part of shipped examples are not actually needed.
Apache solr is an enterprise search engine. It facilitates indexing of large number of documents of any size and provides very robust search techniques. This ppt provides brief introduction of it.
code4lib 2011 preconference: What's New in Solr (since 1.4.1)Erik Hatcher
code4lib 2011 preconference, presented by Erik Hatcher of Lucid Imagination.
Abstract: The library world is fired up about Solr. Practically every next-gen catalog is using it (via Blacklight, VuFind, or other technologies). Solr has continued improving in some dramatic ways, including geospatial support, field collapsing/grouping, extended dismax query parsing, pivot/grid/matrix/tree faceting, autosuggest, and more. This session will cover all of these new features, showcasing live examples of them all, including anything new that is implemented prior to the conference.
Got data? Let's make it searchable! This presentation will demonstrate getting documents into Solr quickly, will provide some tips in adjusting Solr's schema to match your needs better, and finally will discuss how to showcase your data in a flexible search user interface. We'll see how to rapidly leverage faceting, highlighting, spell checking, and debugging. Even after all that, there will be enough time left to outline the next steps in developing your search application and taking it to production.
The talk presents the sfSolrPlugin which transparently integrates the Solr search engine into symfony.
The talk explains :
* the features of the solr search engine
* how to integrate the search engine into symfony
* complex search : faceted and geolocalized search
* usage example : http://www.menugourmet.com and http://resolutionfinder.org
The next major release of Solr is right around the corner! Join Solr Committer Cassandra Targett and Lucidworks SVP of Engineering Trey Grainger for a first look into what’s included in the upcoming release.
Overview of Solr 6.2 examples, including features they have and challenges they present. A contrasting demonstration of a minimal viable example. A step-by-step deconstruction of "films" example to show what part of shipped examples are not actually needed.
Apache solr is an enterprise search engine. It facilitates indexing of large number of documents of any size and provides very robust search techniques. This ppt provides brief introduction of it.
code4lib 2011 preconference: What's New in Solr (since 1.4.1)Erik Hatcher
code4lib 2011 preconference, presented by Erik Hatcher of Lucid Imagination.
Abstract: The library world is fired up about Solr. Practically every next-gen catalog is using it (via Blacklight, VuFind, or other technologies). Solr has continued improving in some dramatic ways, including geospatial support, field collapsing/grouping, extended dismax query parsing, pivot/grid/matrix/tree faceting, autosuggest, and more. This session will cover all of these new features, showcasing live examples of them all, including anything new that is implemented prior to the conference.
Solr is the popular, blazing fast open Source Enterprise search platform from the Apache LuceneTM project. Its major features include powerful full-text search, hit highlighting, faceted search, near real-time indexing, dynamic clustering, database integration, rich document (e.g., Word, PDF) handling, and geospatial search. Solr is highly reliable, scalable and fault tolerant, providing distributed indexing, replication and load-balanced querying, automated failover and recovery, centralized configuration and more. Solr powers the search and navigation features of many of the world's largest internet sites like (Aol, Yahoo, Buy.com, Cnet, CitySearch, Netflix, Zappos, Stubhub!, digg, eTrade, Disney, Apple, NASA and MTV).
Portable Lucene Index Format & Applications - Andrzej Bialeckilucenerevolution
See conference video - http://www.lucidimagination.com/devzone/events/conferences/ApacheLuceneEurocon2011
This talk will present a design and implementation of a flexible, version-independent serialization format for Lucene indexes and its applications in index upgrades / downgrades, in distributed document analysis, in distributed indexing, and in integration with external indexing pipelines. This format enables submitting pre-analyzed documents to Lucene/Solr, and transferring parts of indexes between nodes in a distributed setup.
Finite-State Queries in Lucene:
* Background, improvement/evolution of MultiTermQuery API in 2.9 and Flex
* Implementing existing Lucene queries with NFA/DFA for better performance: Wildcard, Regex, Fuzzy
* How you can use this Query programmatically to improve relevance (I'll use an English test collection/English examples)
Quick overview of other Lucene features in development, such as:
* Flexible Indexing
* "More-Flexible" Scoring: challenges/supporting BM25, more vector-space models, field-specific scoring, etc.
* Improvements to analysis
Bonus:
* Lucene / Solr merger explanation and future plans
About the presenter:
Robert Muir is a super-active Lucene developer. He works as a software developer for Abraxas Corporation. Robert received his MS in Computer Science from Johns Hopkins and BS in CS from Radford University. For the last few years Robert has been working on foreign language NLP problems - "I really enjoy working with Lucene, as it's always receptive to better int'l/language support, even though everyone seems to be a performance freak... such a weird combination!"
Presented by Fotolog. Lucene is a powerful, high-performance, full-featured text search engine library that is written entirely in Java and provides a technology suitable for all size applications requiring full-text search in heterogeneous environments.
In this presentation, Frank Mash shows you how you can use Lucene with MySQL to offer powerful searching capabilities to your stakeholders. The presentation will cover installation, usage. optimization of Lucene, and how to interface a Ruby on Rails application with Lucene using a custom Java server. This session is highly recommended for those looking to add full-text cross-platform, database independent search capability to their application.
Apache Solr serves search requests at the enterprises and the largest companies around the world. Built on top of the top-notch Apache Lucene library, Solr makes indexing and searching integration into your applications straightforward.
Solr provides faceted navigation, spell checking, highlighting, clustering, grouping, and other search features. Solr also scales query volume with replication and collection size with distributed capabilities. Solr can index rich documents such as PDF, Word, HTML, and other file types.
Solr Recipes provides quick and easy steps for common use cases with Apache Solr. Bite-sized recipes will be presented for data ingestion, textual analysis, client integration, and each of Solr’s features including faceting, more-like-this, spell checking/suggest, and others.
In this On-Demand Webinar, Erik Hatcher, co-founder of Lucid Imagination, co-author of Lucene in Action, and Lucene/Solr PMC member and committer, presents and discusess key features and innovations of Apache Solr 1.4
Self-learned Relevancy with Apache SolrTrey Grainger
Search engines are known for "relevancy", but the relevancy models that ship out of the box (BM25, classic tf-idf, etc.) are just scratching the surface of what's needed for a truly insightful application.
What if your search engine could automatically tune its own domain-specific relevancy model based on user interactions? What if it could learn the important phrases and topics within your domain, learn the conceptual relationships embedded within your documents, and even use machine-learned ranking to discover the relative importance of different features and then automatically optimize its own ranking algorithms for your domain? What if you could further use SQL queries to explore these relationships within your own BI tools and return results in ranked order to deliver relevance-driven analytics visualizations?
In this presentation, we'll walk through how you can leverage the myriad of capabilities in the Apache Solr ecosystem (such as the Solr Text Tagger, Semantic Knowledge Graph, Spark-Solr, Solr SQL, learning to rank, probabilistic query parsing, and Lucidworks Fusion) to build self-learning, relevance-first search, recommendations, and data analytics applications.
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.
See conference video - http://www.lucidimagination.com/devzone/events/conferences/ApacheLuceneEurocon2011
This talk describes how you can practically apply some of Lucene 4's new features (such as flexible indexing, scoring improvements, column-stride fields) to improve your search application.
The talk will give a brief description of these new features and some example use-cases, to address practical use cases you can try yourself in and around the new features now available in Lucene 4. We'll cover application of functions where you can configure Solr to:
Set up the schema to use Pulsing or Memory codec for a primary key field
Not use a separate spellcheck index, controlling character-level swaps from the query processor
Sorting with a different locale
Per-field similarity configurations, such as using a non-vector-space algorithm
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.
Solr search engine with multiple table relationJay Bharat
Here you can learn how to use solr search engine and implement in your application like in PHP/MYSQL.
I am introducing how to handle multiple table data handling in SOLR.
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.
Iterator - a powerful but underappreciated design patternNitin Bhide
Iterator design pattern is described in GoF ‘Design Patterns’ book. It is used at many places (e.g. Sql Cursor is a ‘iterator’), C++ standard template library uses iterators heavily. .Net Linq interfaces are based IEnumerable (i.e. iterator). However, I don’t see projects creating/using ‘custom’ iterator classes. Many problems can be solved ‘elegantly’ by use of customized iterators. This talk is about ‘power of iterators’ and how custom iterators can solve common problems and help create modular/reusable code components.
Key Discussion Points
Typical examples of iterators in common use.
Kind of problems that can be ‘elegantly’ solved with iterators
When to use custom iterators?
How write custom iterators in C++/C#
From webinar I did on TechGig
http://www.techgig.com/expert-speak/Iterator-a-powerful-but-underappreciated-pattern-449
Solr 4.0 dramatically improves scalability, performance, and flexibility. An overhauled Lucene underneath sports near real-time (NRT) capabilities allowing indexed documents to be rapidly visible and searchable. Lucene’s improvements also include pluggable scoring, much faster fuzzy and wildcard querying, and vastly improved memory usage. These Lucene improvements automatically make Solr much better, and Solr magnifies these advances with “SolrCloud.” SolrCloud enables highly available and fault tolerant clusters for large scale distributed indexing and searching. There are many other changes that will be surveyed as well. This talk will cover these improvements in detail, comparing and contrasting to previous versions of Solr.
Solr now smoothly integrates with Lucene-level payloads.
Payloads provide optional per-term metadata, numeric or otherwise. Payloads help solve challenging use cases such as per-store product pricing and per-term confidence/weighting.
This session will present the payload feature from the Lucene layer up to the Solr integration, including per-store pricing, per-term weighting, and more.
Think *inside* the box. Inside the *search* box, that is.
The "best"* search results incorporate many more factors than (just) textual matching and relevancy. Search experience owners manage query context rules, signals automatically feed back machine learned factors, users implicit and explicit behaviors filter and weight future interactions. Synergy emerges with several cooperating (just) searches.
This talk will showcase and detail several (just) search examples including rules, typeahead/suggest, signals, and location awareness, bringing them all together into a cohesive search experience.
Lucene powers the search capabilities of practically all library discovery platforms, by way of Solr, etc. The Lucene project evolves rapidly, and it's a full-time job to keep up with the ever improving features and scalability. This talk will distill and showcase the most relevant(!) advancements to date.
Using Apache Lucene and Solr search technologies, information and knowledge have become vastly more searchable, findable, and accessible. Because scholars and researchers are some of the most demanding users of search systems, the problems encountered by the implementers are complex. For example, many of the applications built on these technologies also thrive on intentionally designed-in serendipitous discovery capabilities, bringing to light previously unknown, yet related and potentially interesting, content.
Libraries and other public knowledge-sharing environments, such as Wikipedia, generally embrace "open source" and community improving contributions as core principles, making a lovely synergy with the power, features, and community-driven ecosystem provided by Lucene and Solr.
This talk will introduce you to several Solr powered library-related systems, detail how they work, and leave you with lessons learned that can be applied to your applications.
"Solr Update" at code4lib '13 - ChicagoErik Hatcher
Solr is continually improving. Solr 4 was recently released, bringing dramatic changes in the underlying Lucene library and Solr-level features. It's tough for us all to keep up with the various versions and capabilities.
This talk will blaze through the highlights of new features and improvements in Solr 4 (and up). Topics will include: SolrCloud, direct spell checking, surround query parser, and many other features. We will focus on the features library coders really need to know about.
In this talk, Solr's built-in query parsers will be detailed included when and how to use them. Solr has nested query parsing capability, allowing for multiple query parsers to be used to generate a single query. The nested query parsing feature will be described and demonstrated. In many domains, e-commerce in particular, parsing queries often means interpreting which entities (e.g. products, categories, vehicles) the user likely means; this talk will conclude with techniques to achieve richer query interpretation.
Got data? Let's make it searchable! This interactive presentation will demonstrate getting documents into Solr quickly, provide some tips in adjusting Solr's schema to match your needs better, and finally showcase your data in a flexible search user interface. We'll see how to rapidly leverage faceting, highlighting, spell checking, and debugging. Even after all that, there will be enough time left to outline the next steps in developing your search application and taking it to production.
Solr Flair: Search User Interfaces Powered by Apache Solr (ApacheCon US 2009,...Erik Hatcher
Solr powers library, government, and enterprise search systems in thousands of applications. This talk showcases various technologies and techniques used to build effective user search, browse, and find interfaces on top of Solr.
Solr Flair: Search User Interfaces Powered by Apache SolrErik Hatcher
Solr powers library, government, and enterprise search systems in thousands of applications. This talk will showcase the various technologies and techniques used to build effective user search, browse, and find interfaces on top of Solr. Several of the full featured open-source library Solr front-ends will be shown, including Blacklight and VuFind. We’ll also demonstrate several front-end frameworks including:
• SolrJS - a JavaScript widget library
• Solr Flare - a Ruby on Rails plugin featuring Simile Timeline integration, Ajax suggest, and more
• Solritas - a built-in lightweight UI templating framework
Additionally, we’ll take a look under the covers of http://search.lucidimagination.com and see what makes it shine.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
2. Abstract
You’re Solr powered, and needing to customize its
capabilities. Apache Solr is flexibly architected, with
practically everything pluggable. Under the hood, Solr is
driven by the well-known Apache Lucene. Lucene for
Solr Developers will guide you through the various ways
in which Solr can be extended, customized, and enhanced
with a bit of Lucene API know-how. We’ll delve into
improving analysis with custom character mapping,
tokenizing, and token filtering extensions; show why and
how to implement specialized query parsing, and how to
add your own search and update request handling.
2
3. About me...
• Co-author, “Lucene in Action”
• Commiter, Lucene and Solr
• Lucene PMC and ASF member
• Member of Technical Staff / co-founder,
Lucid Imagination
3
4. ... works
search platform
www.lucidimagination.com
4
5. What is Lucene?
• An open source search library (not an application)
• 100% Java
• Continuously improved and tuned over more than
10 years
• Compact, portable index representation
• Programmable text analyzers, spell checking and
highlighting
• Not a crawler or a text extraction tool
5
6. Inverted Index
• Lucene stores input data in what is known as an
inverted index
• In an inverted index each indexed term points to a
list of documents that contain the term
• Similar to the index provided at the end of a book
• In this case "inverted" simply means the list of terms
point to documents
• It is much faster to find a term in an index, than to
scan all the documents
6
8. Segments and Merging
• A Lucene index is a collection of one or more sub-indexes
called segments
• Each segment is a fully independent index
• A multi-way merge algorithm is used to periodically merge
segments
• New segments are created when an IndexWriter flushes new
documents and pending deletes to disk
• Trying for a balance between large-scale performance vs. small-
scale updates
• Optimization merges all segments into one
8
10. Segments
• When a document is deleted it still exists
in an index segment until that segment is
merged
• At certain trigger points, these Documents
are flushed to the Directory
• Can be forced by calling commit
• Segments are periodically merged
10
15. Lucene Scoring
• Lucene uses a similarity scoring formula to rank results by measuring the
similarity between a query and the documents that match the query. The
factors that form the scoring formula are:
• Term Frequency: tf (t in d). How often the term occurs in the document.
• Inverse Document Frequency: idf (t). A measure of how rare the term is in
the whole collection. One over the number of times the term appears in
the collection.
• Terms that are rare throughout the entire collection score higher.
15
16. Coord and Norms
• Coord: The coordination factor, coord (q, d).
Boosts documents that match more of the
search terms than other documents.
• If 4 of 4 terms match coord = 4/4
• If 3 of 4 terms match coord = 3/4
• Length Normalization - Adjust the score based
on length of fields in the document.
• shorter fields that match get a boost
16
17. Scoring Factors (cont)
• Boost: (t.field in d). A way to boost a field
or a whole document above others.
• Query Norm: (q). Normalization value
for a query, given the sum of the squared
weights of each of the query terms.
• You will often hear the Lucene scoring
simply referred to as
TF·IDF.
17
18. Explanation
• Lucene has a feature called Explanation
• Solr uses the debugQuery parameter to
retrieve scoring explanations
0.2987913 = (MATCH) fieldWeight(text:lucen in 688), product of:
1.4142135 = tf(termFreq(text:lucen)=2)
9.014501 = idf(docFreq=3, maxDocs=12098)
0.0234375 = fieldNorm(field=text, doc=688)
18
21. Customizing - Don't do it!
• Unless you need to.
• In other words... ensure you've given the built-in
capabilities a try, asked on the e-mail list, and
spelunked into at least Solr's code a bit to make
some sense of the situation.
• But we're here to roll up our sleeves, because we
need to...
21
22. But first...
• Look at Lucene and/or Solr source code as
appropriate
• Carefully read javadocs and wiki pages - lots of tips
there
• And, hey, search for what you're trying to do...
• Google, of course
• But try out LucidFind and other Lucene ecosystem
specific search systems -
http://www.lucidimagination.com/search/
22
24. Factories
• FooFactory (most) everywhere.
Sometimes there's BarPlugin style
• for sake of discussion... let's just skip the
"factory" part
• In Solr, Factories and Plugins are used by
configuration loading to parameterize and
construct
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25. "Installing" plugins
• Compile .java to .class, JAR it up
• Put JAR files in either:
• <solr-home>/lib
• a shared lib when using multicore
• anywhere, and register location in
solrconfig.xml
• Hook in plugins as appropriate
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31. CharFilter
• extend BaseCharFilter
• enables pre-tokenization filtering/morphing
of incoming field value
• only affects tokenization, not stored value
• Built-in CharFilters: HTMLStripCharFilter,
PatternReplaceCharFilter, and
MappingCharFilter
31
32. Tokenizer
• common to extend CharTokenizer
• implement -
• protected abstract boolean isTokenChar(int c);
• optionally override -
• protected int normalize(int c)
• extend Tokenizer directly for finer control
• Popular built-in Tokenizers include: WhitespaceTokenizer,
StandardTokenizer, PatternTokenizer, KeywordTokenizer,
ICUTokenizer
32
33. TokenFilter
• a TokenStream whose input is another
TokenStream
• Popular TokenFilters include:
LowerCaseFilter, CommonGramsFilter,
SnowballFilter, StopFilter,
WordDelimiterFilter
33
34. Lucene's analysis APIs
• tricky business, what with Attributes
(Source/Factory's), State, characters, code
points,Version, etc...
• Test!!!
• BaseTokenStreamTestCase
• Look at Lucene and Solr's test cases
34
39. Built-in QParsers
from QParserPlugin.java
/** internal use - name to class mappings of builtin parsers */
public static final Object[] standardPlugins = {
LuceneQParserPlugin.NAME, LuceneQParserPlugin.class,
OldLuceneQParserPlugin.NAME, OldLuceneQParserPlugin.class,
FunctionQParserPlugin.NAME, FunctionQParserPlugin.class,
PrefixQParserPlugin.NAME, PrefixQParserPlugin.class,
BoostQParserPlugin.NAME, BoostQParserPlugin.class,
DisMaxQParserPlugin.NAME, DisMaxQParserPlugin.class,
ExtendedDismaxQParserPlugin.NAME, ExtendedDismaxQParserPlugin.class,
FieldQParserPlugin.NAME, FieldQParserPlugin.class,
RawQParserPlugin.NAME, RawQParserPlugin.class,
TermQParserPlugin.NAME, TermQParserPlugin.class,
NestedQParserPlugin.NAME, NestedQParserPlugin.class,
FunctionRangeQParserPlugin.NAME, FunctionRangeQParserPlugin.class,
SpatialFilterQParserPlugin.NAME, SpatialFilterQParserPlugin.class,
SpatialBoxQParserPlugin.NAME, SpatialBoxQParserPlugin.class,
JoinQParserPlugin.NAME, JoinQParserPlugin.class,
};
39
40. Local Parameters
• {!qparser_name param=value}expression
• or
• {!qparser_name param=value v=expression}
• Can substitute $references from request
parameters
40
44. Built-in Update
Processors
• RunUpdateProcessor
• Actually performs the operations, such as
adding the documents to the index
• LogUpdateProcessor
• Logs each operation
• SignatureUpdateProcessor
• duplicate detection and optionally rejection
44
46. Update Processor
Chain
• UpdateProcessor's sequence into a chain
• Each processor can abort the entire update
or hand processing to next processor in
the chain
• Chains, of update processor factories, are
specified in solrconfig.xml
• Update requests can specify an
update.processor parameter
46
47. Default update
processor chain
From SolrCore.java
// construct the default chain
UpdateRequestProcessorFactory[] factories =
new UpdateRequestProcessorFactory[]{
new RunUpdateProcessorFactory(),
new LogUpdateProcessorFactory()
};
Note: these steps have been swapped on trunk recently
47
48. Example Update
Processor
• What are the best facets to show for a particular
query? Wouldn't it be nice to see the distribution of
document "attributes" represented across a result
set?
• Learned this trick from the Smithsonian, who were
doing it manually - add an indexed field containing the
field names of the interesting other fields on the
document.
• Facet on that field "of field names" initially, then
request facets on the top values returned.
48
50. FieldsUsedUpdateProcessorFactory
public class FieldsUsedUpdateProcessorFactory extends UpdateRequestProcessorFactory {
private String fieldsUsedFieldName;
private Pattern fieldNamePattern;
public UpdateRequestProcessor getInstance(SolrQueryRequest req, SolrQueryResponse rsp,
UpdateRequestProcessor next) {
return new FieldsUsedUpdateProcessor(req, rsp, this, next);
}
// ... next slide ...
}
50
51. FieldsUsedUpdateProcessorFactory
@Override
public void init(NamedList args) {
if (args == null) return;
SolrParams params = SolrParams.toSolrParams(args);
fieldsUsedFieldName = params.get("fieldsUsedFieldName");
if (fieldsUsedFieldName == null) {
throw new SolrException
(SolrException.ErrorCode.SERVER_ERROR,
"fieldsUsedFieldName must be specified");
}
// TODO check that fieldsUsedFieldName is a valid field name and multiValued
String fieldNameRegex = params.get("fieldNameRegex");
if (fieldNameRegex == null) {
throw new SolrException
(SolrException.ErrorCode.SERVER_ERROR,
"fieldNameRegex must be specified");
}
fieldNamePattern = Pattern.compile(fieldNameRegex);
super.init(args);
}
51
52. class FieldsUsedUpdateProcessor extends UpdateRequestProcessor {
public FieldsUsedUpdateProcessor(SolrQueryRequest req,
SolrQueryResponse rsp,
FieldsUsedUpdateProcessorFactory factory,
UpdateRequestProcessor next) {
super(next);
}
@Override
public void processAdd(AddUpdateCommand cmd) throws IOException {
SolrInputDocument doc = cmd.getSolrInputDocument();
Collection<String> incomingFieldNames = doc.getFieldNames();
Iterator<String> iterator = incomingFieldNames.iterator();
ArrayList<String> usedFields = new ArrayList<String>();
while (iterator.hasNext()) {
String f = iterator.next();
if (fieldNamePattern.matcher(f).matches()) {
usedFields.add(f);
}
}
doc.addField(fieldsUsedFieldName, usedFields.toArray());
super.processAdd(cmd);
}
}
52
55. Example - auto facet
select
• It sure would be nice if you could have Solr automatically
select field(s) for faceting based dynamically off the
profile of the results. For example, you're indexing
disparate types of products, all with varying attributes
(color, size - like for apparel, memory_size - for
electronics, subject - for books, etc), and a user searches
for "ipod" where most products match products with
color and memory_size attributes... let's automatically
facet on those fields.
• https://issues.apache.org/jira/browse/SOLR-2641
55
56. AutoFacetSelection
Component
• Too much code for a slide, let's take a look in
an IDE...
• Basically -
• process() gets autofacet.field and autofacet.n
request params, facets on field, takes top N
values, sets those as facet.field's
• Gotcha - need to call rb.setNeedDocSet
(true) in prepare() as faceting needs it
56