Yonik Seeley presents on analytics and graph traversal with Solr. He discusses his background creating Solr and working on Lucene and Apache projects. The document then covers graph databases and how they represent nodes and edges, how graphs can be mapped to a document schema in Solr, and how graph queries and traversal filters can be used to navigate graph relationships in Solr.
Neo4j is a powerful and expressive tool for storing, querying and manipulating data. However modeling data as graphs is quite different from modeling data under a relational database. In this talk, Michael Hunger will cover modeling business domains using graphs and show how they can be persisted and queried in Neo4j. We'll contrast this approach with the relational model, and discuss the impact on complexity, flexibility and performance.
Your Roadmap for An Enterprise Graph StrategyNeo4j
Speaker: Michael Moore, Ph.D., Executive Director, Knowledge Graphs + AI, EY National Advisory
Abstract: Knowledge graphs have enormous potential for delivering superior customer experiences, advanced analytics and efficient data management.
Learn valuable tips from a leading practitioner on how to position, organize and implement your first enterprise graph project.
Dense Retrieval with Apache Solr Neural Search.pdfSease
Neural Search is an industry derivation from the academic field of Neural information Retrieval. More and more frequently, we hear about how Artificial Intelligence (AI) permeates every aspect of our lives and this includes also software engineering and Information Retrieval.
In particular, the advent of Deep Learning introduced the use of deep neural networks to solve complex problems that could not be solved simply by an algorithm. Deep Learning can be used to produce a vector representation of both the query and the documents in a corpus of information. Search, in general, comprises of performing four primary steps:
- generate a representation of the query that describes the information need - generate a representation of the document that captures the information contained in it
- match the query and the document representations from the corpus of information
- assign a score to each matched document in order to establish a meaningful document ranking by relevance in the results.
With the Neural Search module, Apache Solr is introducing support for neural network based techniques that can improve these four aspects of search.
Neo4j is a powerful and expressive tool for storing, querying and manipulating data. However modeling data as graphs is quite different from modeling data under a relational database. In this talk, Michael Hunger will cover modeling business domains using graphs and show how they can be persisted and queried in Neo4j. We'll contrast this approach with the relational model, and discuss the impact on complexity, flexibility and performance.
Your Roadmap for An Enterprise Graph StrategyNeo4j
Speaker: Michael Moore, Ph.D., Executive Director, Knowledge Graphs + AI, EY National Advisory
Abstract: Knowledge graphs have enormous potential for delivering superior customer experiences, advanced analytics and efficient data management.
Learn valuable tips from a leading practitioner on how to position, organize and implement your first enterprise graph project.
Dense Retrieval with Apache Solr Neural Search.pdfSease
Neural Search is an industry derivation from the academic field of Neural information Retrieval. More and more frequently, we hear about how Artificial Intelligence (AI) permeates every aspect of our lives and this includes also software engineering and Information Retrieval.
In particular, the advent of Deep Learning introduced the use of deep neural networks to solve complex problems that could not be solved simply by an algorithm. Deep Learning can be used to produce a vector representation of both the query and the documents in a corpus of information. Search, in general, comprises of performing four primary steps:
- generate a representation of the query that describes the information need - generate a representation of the document that captures the information contained in it
- match the query and the document representations from the corpus of information
- assign a score to each matched document in order to establish a meaningful document ranking by relevance in the results.
With the Neural Search module, Apache Solr is introducing support for neural network based techniques that can improve these four aspects of search.
Data Lakehouse, Data Mesh, and Data Fabric (r2)James Serra
So many buzzwords of late: Data Lakehouse, Data Mesh, and Data Fabric. What do all these terms mean and how do they compare to a modern data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. They all may sound great in theory, but I'll dig into the concerns you need to be aware of before taking the plunge. I’ll also include use cases so you can see what approach will work best for your big data needs. And I'll discuss Microsoft version of the data mesh.
Big Data Architectural Patterns and Best Practices on AWSAmazon Web Services
In this session, we simplify big data processing as a data bus comprising various stages: ingest, store, process, and visualize. John Pignata, AWS Startup Solutions Architect, will discuss how to choose the right technology in each stage based on criteria such as data structure, query latency, cost, request rate, item size, data volume, durability, and so on. He will provide reference architecture, design patterns, and best practices for assembling these technologies to solve your big data problems at the right cost.
Overview of tools available in python for performing data visualization (statistical, geographical, reporting, etc). Prepared for Minsk DataViz Day (October 4, 2017)
Feature Store as a Data Foundation for Machine LearningProvectus
Looking to design and build a centralized, scalable Feature Store for your Data Science & Machine Learning teams to take advantage of? Come and learn from experts of Provectus and Amazon Web Services (AWS) how to!
Feature Store is a key component of the ML stack and data infrastructure, which enables feature engineering and management. By having a Feature Store, organizations can save massive amounts of resources, innovate faster, and drive ML processes at scale. In this webinar, you will learn how to build a Feature Store with a data mesh pattern and see how to achieve consistency between real-time and training features, to improve reproducibility with time-traveling for data.
Agenda
- Modern Data Lakes & Modern ML Infrastructure
- Existing and Emerging Architectural Shifts
- Feature Store: Overview and Reference Architecture
- AWS Perspective on Feature Store
Intended Audience
Technology executives & decision makers, manager-level tech roles, data architects & analysts, data engineers & data scientists, ML practitioners & ML engineers, and developers
Presenters
- Stepan Pushkarev, Chief Technology Officer, Provectus
- Gandhi Raketla, Senior Solutions Architect, AWS
- German Osin, Senior Solutions Architect, Provectus
Feel free to share this presentation with your colleagues and don't hesitate to reach out to us at info@provectus.com if you have any questions!
REQUEST WEBINAR: https://provectus.com/webinar-feature-store-as-data-foundation-for-ml-nov-2020/
GPT and Graph Data Science to power your Knowledge GraphNeo4j
In this workshop at Data Innovation Summit 2023, we demonstrated how you could learn from the network structure of a Knowledge Graph and use OpenAI’s GPT engine to populate and enhance your Knowledge Graph.
Key takeaways:
1. How Knowledge Graphs grow organically
2. How to deploy Graph Algorithms to learn from the topology of a graph
3. Integrate a Knowledge Graph with OpenAI’s GPT
4. Use Graph Node embeddings to feed Machine Learning workflow
Graphs in Retail: Know Your Customers and Make Your Recommendations Engine LearnNeo4j
At Neo4j we believe that “Graphs Are Everywhere”. In this session, we’ll be exploring graphs within the Retail industry. We’ll discuss a range of data that are commonly available within a retail organisation, both online and “brick and mortar". We’ll illustrate some graphs which can be created by linking together different elements of that data and discuss the retail use cases those graphs can enable and transform.
We’ll specifically focus on use cases like Personalised Recommendations (with a live demo), Supply Chain Management, Logistics, and Customer 360. We'll also look at some relevant graph algorithms and talk about opportunities for integration with Artificial Intelligence/Machine Learning technologies, which can be used along with Neo4j to generate new value using retail data.
Walmart, Wobi, and others already deploy Neo4j for use cases like price comparison or real-time contextual and learning recommendation engines. Read about their use cases!
The path to success with Graph Database and Graph Data ScienceNeo4j
What’s new and what’s next? Product innovation moves rapidly at Neo4j – learn how graph technology can provide you with the tools to get much more from your data!
How Graph Algorithms Answer your Business Questions in Banking and BeyondNeo4j
Graph algorithms are powerful tools, and there’s a lot of excitement about their applications for data science. It can sometimes be difficult, however - especially for those of us who aren’t data scientists - to know how they might be applied to a particular data set or a specific business problem. There are graph algorithms for centrality and importance measurement, community detection, similarity comparison, pathfinding, and link prediction. Which ones should you use on your data, and which ones might be most useful in answering your business questions?
In this presentation, we’ll look at a few examples of Neo4j graph algorithms, and see how they can be applied to data and business problems from the banking industry. We’ll discuss what kinds of data are appropriate for different types of algorithms, show how to model and structure data to work with graph algorithms, and run through some real-world scenarios demonstrating the use of graph algorithms on a sample banking data set.
Webinar with Joe Depeau, Neo4j, April 15, 2020
A Google dork query, sometimes just referred to as a dork, is a search string that uses advanced search operators to find information that is not readily available on a website. Google dorking, also known as Google hacking, can return information that is difficult to locate through simple search queries.
A pipeline of reading, parsing, optimizing, and storing a log file to parquet.
This script uses the Python pandas library, utilizing the efficient Apache Parquet format for a big speed up and efficient storage.
Transforming Intelligence Analysis with Knowledge GraphsNeo4j
Transforming Intelligence Analysis with Knowledge Graphs
Vincent H. Bridgeman, Senior Vice President, National Security Services, Redhorse
Pelayo Fernandez, Research Analyst / Project Manager, United States Department of Defense
Intelligence Analysis is fundamentally a network problem. At different levels, the analyst must make sense of networks of related content, networks of related concepts, and ultimately networks of related targets that can only be understood in the context of other (even larger) networks. Examples of network problems in intelligence analysis include terrorism, sanctions evasion, global transnational organized crime, counterintelligence, and cyber security. Redhorse presents an integrated technology solution founded on Neo4j’s native graph database that brings a graphs-centered approach to intelligence analysis. The US Air Force provides an unclassified case study applying graphs to scientific forecasting. This project leverages temporal knowledge graphs, comprised of research article content and metadata, to learn and predict the trajectory of technological advancement, pushing the boundaries of graph-based intelligence analysis.
Introduction to graph databases, Neo4j and Spring Data - English 2015 EditionAleksander Stensby
English edition of my introduction to Neo4j and Spring Data talk - updated in February 2015 for the latest changes in Neo4j and Spring Data. The talk gives an introduction to graph databases, Neo4j and its query language Cypher. In addition, the talk covers Spring Data Neo4j.
Data Lakehouse, Data Mesh, and Data Fabric (r2)James Serra
So many buzzwords of late: Data Lakehouse, Data Mesh, and Data Fabric. What do all these terms mean and how do they compare to a modern data warehouse? In this session I’ll cover all of them in detail and compare the pros and cons of each. They all may sound great in theory, but I'll dig into the concerns you need to be aware of before taking the plunge. I’ll also include use cases so you can see what approach will work best for your big data needs. And I'll discuss Microsoft version of the data mesh.
Big Data Architectural Patterns and Best Practices on AWSAmazon Web Services
In this session, we simplify big data processing as a data bus comprising various stages: ingest, store, process, and visualize. John Pignata, AWS Startup Solutions Architect, will discuss how to choose the right technology in each stage based on criteria such as data structure, query latency, cost, request rate, item size, data volume, durability, and so on. He will provide reference architecture, design patterns, and best practices for assembling these technologies to solve your big data problems at the right cost.
Overview of tools available in python for performing data visualization (statistical, geographical, reporting, etc). Prepared for Minsk DataViz Day (October 4, 2017)
Feature Store as a Data Foundation for Machine LearningProvectus
Looking to design and build a centralized, scalable Feature Store for your Data Science & Machine Learning teams to take advantage of? Come and learn from experts of Provectus and Amazon Web Services (AWS) how to!
Feature Store is a key component of the ML stack and data infrastructure, which enables feature engineering and management. By having a Feature Store, organizations can save massive amounts of resources, innovate faster, and drive ML processes at scale. In this webinar, you will learn how to build a Feature Store with a data mesh pattern and see how to achieve consistency between real-time and training features, to improve reproducibility with time-traveling for data.
Agenda
- Modern Data Lakes & Modern ML Infrastructure
- Existing and Emerging Architectural Shifts
- Feature Store: Overview and Reference Architecture
- AWS Perspective on Feature Store
Intended Audience
Technology executives & decision makers, manager-level tech roles, data architects & analysts, data engineers & data scientists, ML practitioners & ML engineers, and developers
Presenters
- Stepan Pushkarev, Chief Technology Officer, Provectus
- Gandhi Raketla, Senior Solutions Architect, AWS
- German Osin, Senior Solutions Architect, Provectus
Feel free to share this presentation with your colleagues and don't hesitate to reach out to us at info@provectus.com if you have any questions!
REQUEST WEBINAR: https://provectus.com/webinar-feature-store-as-data-foundation-for-ml-nov-2020/
GPT and Graph Data Science to power your Knowledge GraphNeo4j
In this workshop at Data Innovation Summit 2023, we demonstrated how you could learn from the network structure of a Knowledge Graph and use OpenAI’s GPT engine to populate and enhance your Knowledge Graph.
Key takeaways:
1. How Knowledge Graphs grow organically
2. How to deploy Graph Algorithms to learn from the topology of a graph
3. Integrate a Knowledge Graph with OpenAI’s GPT
4. Use Graph Node embeddings to feed Machine Learning workflow
Graphs in Retail: Know Your Customers and Make Your Recommendations Engine LearnNeo4j
At Neo4j we believe that “Graphs Are Everywhere”. In this session, we’ll be exploring graphs within the Retail industry. We’ll discuss a range of data that are commonly available within a retail organisation, both online and “brick and mortar". We’ll illustrate some graphs which can be created by linking together different elements of that data and discuss the retail use cases those graphs can enable and transform.
We’ll specifically focus on use cases like Personalised Recommendations (with a live demo), Supply Chain Management, Logistics, and Customer 360. We'll also look at some relevant graph algorithms and talk about opportunities for integration with Artificial Intelligence/Machine Learning technologies, which can be used along with Neo4j to generate new value using retail data.
Walmart, Wobi, and others already deploy Neo4j for use cases like price comparison or real-time contextual and learning recommendation engines. Read about their use cases!
The path to success with Graph Database and Graph Data ScienceNeo4j
What’s new and what’s next? Product innovation moves rapidly at Neo4j – learn how graph technology can provide you with the tools to get much more from your data!
How Graph Algorithms Answer your Business Questions in Banking and BeyondNeo4j
Graph algorithms are powerful tools, and there’s a lot of excitement about their applications for data science. It can sometimes be difficult, however - especially for those of us who aren’t data scientists - to know how they might be applied to a particular data set or a specific business problem. There are graph algorithms for centrality and importance measurement, community detection, similarity comparison, pathfinding, and link prediction. Which ones should you use on your data, and which ones might be most useful in answering your business questions?
In this presentation, we’ll look at a few examples of Neo4j graph algorithms, and see how they can be applied to data and business problems from the banking industry. We’ll discuss what kinds of data are appropriate for different types of algorithms, show how to model and structure data to work with graph algorithms, and run through some real-world scenarios demonstrating the use of graph algorithms on a sample banking data set.
Webinar with Joe Depeau, Neo4j, April 15, 2020
A Google dork query, sometimes just referred to as a dork, is a search string that uses advanced search operators to find information that is not readily available on a website. Google dorking, also known as Google hacking, can return information that is difficult to locate through simple search queries.
A pipeline of reading, parsing, optimizing, and storing a log file to parquet.
This script uses the Python pandas library, utilizing the efficient Apache Parquet format for a big speed up and efficient storage.
Transforming Intelligence Analysis with Knowledge GraphsNeo4j
Transforming Intelligence Analysis with Knowledge Graphs
Vincent H. Bridgeman, Senior Vice President, National Security Services, Redhorse
Pelayo Fernandez, Research Analyst / Project Manager, United States Department of Defense
Intelligence Analysis is fundamentally a network problem. At different levels, the analyst must make sense of networks of related content, networks of related concepts, and ultimately networks of related targets that can only be understood in the context of other (even larger) networks. Examples of network problems in intelligence analysis include terrorism, sanctions evasion, global transnational organized crime, counterintelligence, and cyber security. Redhorse presents an integrated technology solution founded on Neo4j’s native graph database that brings a graphs-centered approach to intelligence analysis. The US Air Force provides an unclassified case study applying graphs to scientific forecasting. This project leverages temporal knowledge graphs, comprised of research article content and metadata, to learn and predict the trajectory of technological advancement, pushing the boundaries of graph-based intelligence analysis.
Introduction to graph databases, Neo4j and Spring Data - English 2015 EditionAleksander Stensby
English edition of my introduction to Neo4j and Spring Data talk - updated in February 2015 for the latest changes in Neo4j and Spring Data. The talk gives an introduction to graph databases, Neo4j and its query language Cypher. In addition, the talk covers Spring Data Neo4j.
"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.
Elixir and Crystal are both descendants of the Ruby programming language, applying Ruby syntax and ideas to extremely different functional and OOP foundations. This talk compares all three languages and suggests appropriate cases for applying them.
Graph Sample and Hold: A Framework for Big Graph AnalyticsNesreen K. Ahmed
Sampling is a standard approach in big-graph analytics; the goal is to efficiently estimate the graph properties by consulting a sample of the whole population. A perfect sample is assumed to mirror every property of the whole population. Unfortunately, such a perfect sample is hard to collect in complex populations such as graphs(e.g. web graphs, social networks), where an underlying network connects the units of the population. Therefore, a good sample will be representative in the sense that graph properties of interest can be estimated with a known degree of accuracy.While previous work focused particularly on sampling schemes to estimate certain graph properties (e.g. triangle count), much less is known for the case when we need to estimate various graph properties with the same sampling scheme. In this paper, we pro-pose a generic stream sampling framework for big-graph analytics,called Graph Sample and Hold (gSH), which samples from massive graphs sequentially in a single pass, one edge at a time, while maintaining a small state in memory. We use a Horvitz-Thompson construction in conjunction with a scheme that samples arriving edges without adjacencies to previously sampled edges with probability p and holds edges with adjacencies with probability q. Our sample and hold framework facilitates the accurate estimation of subgraph patterns by enabling the dependence of the sampling process to vary based on previous history. Within our framework, we show how to produce statistically unbiased estimators for various graph properties from the sample. Given that the graph analytic swill run on a sample instead of the whole population, the runtime complexity is kept under control. Moreover, given that the estimators are unbiased, the approximation error is also kept under control.
You've seen the basic 2-stage example Spark Programs, and now you're ready to move on to something larger. I'll go over lessons I've learned for writing efficient Spark programs, from design patterns to debugging tips.
The slides are largely just talking points for a live presentation, but hopefully you can still make sense of them for offline viewing as well.
Apache TinkerPop is an open source graph computing framework which uses Gremlin, a domain-specific language for graphs mutation and traversal. IBM Graph offers an Apache TinkerPop3 compatible API as a service. This service can be used for building recommendation engines, analyzing social networks, fraud detection and more. During this session, we will cover: - What’s a Graph and why use it. - Challenges faced and lessons learned while building and operating a service based on TinkerPop3 stack
Introduction to graph databases in term of neo4jAbdullah Hamidi
The records in a graph database are called Nodes .
Nodes are connected through typed, directed Relationships.
Each single Node and Relationship can have named attributes referred to as Properties.
A Label is a name that organizes nodes into groups.
The flexibility of the graph model has allowed us to add new nodes and new relationships.
Relationships in a graph naturally form paths. Querying—or traversing—the graph involves following paths.
Similar to Analytics and Graph Traversal with Solr - Yonik Seeley, Cloudera (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
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: 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
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
GridMate - End to end testing is a critical piece to ensure quality and avoid...ThomasParaiso2
End to end testing is a critical piece to ensure quality and avoid regressions. In this session, we share our journey building an E2E testing pipeline for GridMate components (LWC and Aura) using Cypress, JSForce, FakerJS…
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
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/
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
SAP Sapphire 2024 - ASUG301 building better apps with SAP Fiori.pdfPeter Spielvogel
Building better applications for business users with SAP Fiori.
• What is SAP Fiori and why it matters to you
• How a better user experience drives measurable business benefits
• How to get started with SAP Fiori today
• How SAP Fiori elements accelerates application development
• How SAP Build Code includes SAP Fiori tools and other generative artificial intelligence capabilities
• How SAP Fiori paves the way for using AI in SAP apps
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
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