The Customer Journey Is a Graph

The document outlines an agenda for a workshop on building a graph solution using a digital twin data set. It includes sections on logistics, introductions, explaining the use case of a digital twin for a rail network, modeling the graph database solution, building the solution, and a question and answer period. Key aspects covered include an overview of Neo4j's graph database capabilities, modeling the domain entities and relationships, and exploring sample data related to operational points, sections, and points of interest for a rail network digital twin use case.

GPT and Graph Data Science to power your Knowledge Graph

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

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Data Management, Metadata Management, and Data Governance – Working Together

The data disciplines listed in the title must work together. The key to success requires understanding the boundaries and overlaps between the disciplines. Wouldn’t it be great to be able to present the relationships between the disciplines in a simple all-in diagram? At the end of this webinar, you will be able to do just that. This new RWDG webinar with Bob Seiner will outline how Data Management, Metadata Management, and Data Governance can be optimized to work together. Bob will share a diagram that has successfully communicated the relationship between these disciplines to leadership resulting in the disciplines working in harmony and delivering success. Bob will share the following in this webinar: - Categories of disciplines focused on managing data as an asset - A definition of Data Management that embraces numerous data disciplines - The importance of Metadata -Management to all data disciplines - Why data and metadata require formal governance - A graphic that effectively exhibits the relationship between the disciplines

Neo4j – The Fastest Path to Scalable Real-Time Analytics

The document discusses how graph databases like Neo4j can enable real-time analytics at massive scale by leveraging relationships in data. It notes that data is growing exponentially but traditional databases can't efficiently analyze relationships. Neo4j natively stores and queries relationships to allow analytics 1000x faster. The document advocates that graphs will form the foundation of modern data and analytics by enhancing machine learning models and enabling outcomes like building intelligent applications faster, gaining deeper insights, and scaling limitlessly without compromising data.

Optimizing Your Supply Chain with the Neo4j Graph

Companies are increasingly becoming software-driven, requiring new approaches to software architecture and data integration. The "data mesh" architectural pattern decentralizes data management by organizing it around domain experts and treating data as products that can be accessed on-demand. This helps address issues with centralized data warehouses by evolving data modeling with business needs, avoiding bottlenecks, and giving autonomy to domain teams. Key principles of the data mesh include domain ownership of data, treating data as self-service products, and establishing federated governance to coordinate the decentralized system.

Modernizing Integration with Data Virtualization

Watch full webinar here: https://bit.ly/3CMqS0E Today, businesses have more data and data types combined with more complex ecosystems than they have ever had before. Examples include on-premise data marts, data warehouses, data lakes, applications, spreadsheets, IoT data, sensor data, unstructured, etc. combined with cloud data ecosystems like Snowflake, Big Query, Azure Synapse, Amazon S3, Redshift, Databricks, SaaS apps, such as Salesforce, Oracle, Service Now, Workday, and on and on. Data, Analytics, Data Science and Architecture teams are struggling to provide the business users with the right data as quickly and efficiently as possible to quickly enable Analytics, Dashboards, BI, Reports, etc. Unfortunately, many enterprises seek to meet this pressing need by utilizing antiquated and legacy 40+ year-old approaches. There is a better way. Proven by thousands of other companies. As Forrester so astutely reported in their recent Total Economic Impact Study, companies who employed Data Virtualization reported a “65% decrease in data delivery times over ETL” and an “83% reduction in time to new revenue.” Join us for this very educational webinar to learn firsthand from Denodo Technologies and Fusion Alliance how: - Data Virtualization helps your company save time and money by eliminating superfluous ETL pipelines and data replication. - Data Virtualization can become the cornerstone of your modern data approach to deliver data faster and more efficiently than old legacy approaches at enterprise scale. - How quickly and easily, Data Virtualization can scale, even in the most complex environments, to create a universal abstraction semantic model(s) for all of your cloud, on premise, structured, unstructured and hybrid data - Data Mesh and Data Fabric architecture patterns for maximum reuse - Other customers have used, and are using, Data Virtualization to tackle their toughest data integration and data delivery challenges - Fusion Alliance can help you define a data strategy tailored to your organization’s needs and requirements, and how they can help you achieve success and enable your business with self-service capabilities

Denodo: Enabling a Data Mesh Architecture and Data Sharing Culture at Landsba...

This document discusses Landsbankinn's implementation of a logical data warehouse and data mesh architecture using Denodo over five years. It summarizes that: 1) Landsbankinn initially implemented a logical data warehouse with Denodo to create a single point of access, business logic, and control for reporting across heterogeneous data sources. 2) Over the next two years, they expanded this to include additional data consumers and sources without requiring ETL. 3) They then realized a data mesh approach where domains owned and published their data was better, reducing complexity and meetings for mapping changes. 4) The current approach has domains developing and sharing views via a data mesh while the logical data warehouse combines them, improving governance and flexibility

Knowledge Graphs & Graph Data Science, More Context, Better Predictions - Neo...

The document discusses how knowledge graphs and graph data science can provide more context and enable better predictions. It provides examples of using knowledge graphs for interactive browsing of patent and pathway data, cross-species ontology graph queries, identifying relevant COVID-19 genes using graph algorithms, and sub-phenotyping patient populations using graph embeddings. The key message is that knowledge graphs harness relationships to provide deep, dynamic context for analytics and machine learning.

Neo4j Generative AI workshop at GraphSummit London 14 Nov 2023.pdf

Intermediate Cypher.pdf

The document provides an agenda for an intermediate Cypher and data modelling workshop. It will include recapping fundamentals of graph databases and Cypher, exploring how Cypher queries work, covering data modeling fundamentals, learning advanced Cypher techniques, and allowing time for questions. Attendees are instructed to follow along using a free AuraDB instance on Neo4j's console. The workshop will cover graph database concepts like nodes, relationships, labels and properties. It will introduce the Cypher language and clauses like MATCH and RETURN. Writing data with Cypher clauses like CREATE, MERGE, SET, REMOVE and DELETE will also be demonstrated. Best practices for data modeling from use cases will be discussed.

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Building a Logical Data Fabric using Data Virtualization (ASEAN)

Watch full webinar here: https://bit.ly/3FF1ubd In the recent Building the Unified Data Warehouse and Data Lake report by leading industry analysts TDWI, we have discovered 64% of organizations stated the objective for a unified Data Warehouse and Data Lakes is to get more business value and 84% of organizations polled felt that a unified approach to Data Warehouses and Data Lakes was either extremely or moderately important. In this session, you will learn how your organization can apply a logical data fabric and the associated technologies of machine learning, artificial intelligence, and data virtualization can reduce time to value. Hence, increasing the overall business value of your data assets. KEY TAKEAWAYS: - How a Logical Data Fabric is the right approach to assist organizations to unify their data. - The advanced features of a Logical Data Fabric that assist with the democratization of data, providing an agile and governed approach to business analytics and data science. - How a Logical Data Fabric with Data Virtualization enhances your legacy data integration landscape to simplify data access and encourage self-service.

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Neo4j in Production: A look at Neo4j in the Real World

This document summarizes a presentation about graph databases and Neo4j. It includes case studies of companies like Walmart and Adidas using Neo4j for real-time recommendations. It also discusses how graph databases are better suited than relational databases for recommendation systems because they can easily model relationships between users, products, and transactions. A demo is shown of using Cypher queries to build a recommendation engine in Neo4j by loading product, customer, and order data. The document concludes by providing resources for moving forward with Neo4j.

Graph Database 101- What, Why and How?.pdf

The document discusses graph databases and Neo4j. It explains that graph databases store data and relationships, and traversing relationships is fast, making them well-suited for applications where connections are important. Neo4j is introduced as the leading graph database, using a labeled property graph model. A variety of use cases are described such as recommendations, fraud detection, and master data management.

Graphs for Finance - AML with Neo4j Graph Data Science

This document discusses using graph data science and graph algorithms to detect fraud. It explains that graph data science uses relationships in data to power predictions. It provides examples of how graph algorithms like Louvain clustering, PageRank, connected components, and Jaccard similarity can be used to identify communities that frequently interact, measure influence, identify accounts sharing identifiers, and measure account similarity to detect fraud in applications like banking and financial services. The document also discusses using graph embeddings and feature engineering with graph networks to improve machine learning models for fraud detection by basing predictions on influential entities and their relationships.

Modeling Cybersecurity with Neo4j, Based on Real-Life Data Insights

Graph databases can help address challenges in cybersecurity by leveraging connections within datasets. Gal Bello's presentation provided an overview of using graph modeling for cybersecurity. It discussed how graph databases can assist companies in securing data by using relationships. The presentation also provided examples of modeling fraud rings and law enforcement data as graphs to improve efficiency and reveal patterns. Real-world use cases demonstrated how organizations are applying graph databases to challenges in cybersecurity.

Data Modeling with Neo4j

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.

The Knowledge Graph Explosion

The document discusses knowledge graphs and their value for organizations. It notes that two-thirds of Neo4j customers have implemented knowledge graphs and that 88% of CXOs believe knowledge graphs will significantly improve business outcomes. Knowledge graphs are described as interconnected datasets enriched with meaning to enable complex decision-making. Examples of how knowledge graphs have helped companies with recommendations, fraud detection, and track and trace are provided.

Workshop Introduction to Neo4j

This document provides an overview of an introduction to Neo4j workshop. The workshop covers what graphs are and why they are useful, identifying good graph scenarios, the anatomy of a property graph database and introduction to Cypher, and hands-on exercises using the movie graph on Neo4j Sandbox or AuraDB Free. It also previews using the Stackoverflow graph and discusses continuing one's graph learning journey through Neo4j's online training and resources.

The perfect couple: Uniting Large Language Models and Knowledge Graphs for En...

Large Language models are amazing but are also black-box models that often fail to capture and accurately represent factual knowledge. Knowledge graphs, by contrast, are structural knowledge models that explicitly represent knowledge and, indeed, allow us to detect implicit relationships. In this talk we will demonstrate how LLMs can be improved by Knowledge Graphs, and how LLM’s can augment Knowledge Graphs. A perfect couple!

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Neo4j 4.1 introduces new features for security including role-based access control, schema-based security, and granular security for write operations. It also includes improvements to causal clustering, performance, and developer tools. This document reviews the history of releases from Neo4j 3.0 through 4.1 and highlights some of the main new capabilities in security, performance, and operations.

Emil Eifrém - The Data Platform for Today’s Intelligent Applications

The document summarizes Neo4j, a graph database company. It discusses Neo4j's history starting in 2000, growth over time including becoming the leader in the graph database category. It highlights Neo4j's large enterprise customer base, funding, and key metrics like over 800 employees. The document also discusses how connected data use cases are growing and examples of how Neo4j is used. It outlines Neo4j's platform and ecosystem of partners before concluding with a prediction about graph analytics replacing traditional models.

Applying Network Analytics in KYC

This document discusses how Rabobank, a Dutch bank, is applying network analytics to enhance its know your customer (KYC) and anti-money laundering (AML) processes. It describes building a graph model with 250 million nodes and 1 billion relations from customer data. Network features like risk triangles and communities are generated and used to identify and rank potentially risky customer cases for AML experts to review. Initial results were promising and a follow-up project was started to further develop ethical network analytics for KYC/AML monitoring.

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