Knowledge Graphs & Graph Data Science, More Context, Better Predictions - Neo...Neo4j
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.
This document summarizes a presentation about the graph database Neo4j. The presentation included an agenda that covered graphs and their power, how graphs change data views, and real-time recommendations with graphs. It introduced the presenters and discussed how data relationships unlock value. It described how Neo4j allows modeling data as a graph to unlock this value through relationship-based queries, evolution of applications, and high performance at scale. Examples showed how Neo4j outperforms relational and NoSQL databases when relationships are important. The presentation concluded with examples of how Neo4j customers have benefited.
The document introduces Cypher, the declarative query language for Neo4j. It explains that Cypher uses ASCII art syntax to represent graph patterns and identify patterns in graph data. Nodes represent entities and can have labels and properties, relationships connect nodes and have types and properties. Examples show how to write Cypher queries to create, read, and match patterns in a movie database graph.
These webinar slides are an introduction to Neo4j and Graph Databases. They discuss the primary use cases for Graph Databases and the properties of Neo4j which make those use cases possible. They also cover the high-level steps of modeling, importing, and querying your data using Cypher and touch on RDBMS to Graph.
Knowledge Graphs and Generative AI_GraphSummit Minneapolis Sept 20.pptxNeo4j
This document discusses using knowledge graphs to ground large language models (LLMs) and improve their abilities. It begins with an overview of generative AI and LLMs, noting their opportunities but also challenges like lack of knowledge and inability to verify sources. The document then proposes using a knowledge graph like Neo4j to provide context and ground LLMs, describing how graphs can be enriched with algorithms, embeddings and other data. Finally, it demonstrates how contextual searches and responses can be improved by retrieving relevant information from the knowledge graph to augment LLM responses.
Neo4j provides graph databases and graph algorithms that can be used to ground language models. Grounding LLMs in a knowledge graph stored with Neo4j allows the language models to leverage both explicit relationships in the graph as well as implicit relationships derived from vector embeddings. This improves the ability of LLMs to provide accurate, non-hallucinated responses by basing their answers on real world facts and connections within the knowledge graph.
Technip Energies Italy: Planning is a graph matterNeo4j
Neo4j and Technip Energies Italy executed an Innovation Lab Sprint. The goal of the laboratory has been to frame, design and prototype the use case identified by their colleagues of Planning, Equipment and Construction disciplines, by applying Knowledge Graph technology, as the way to connect the data to gain information and insights as an immediate value, that is:
– capturing engineering deliverable milestone chain by gaining insights into a schedule
– performing reasoning on information, evidence and data
– extracting insights from data
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.
The document discusses graph data science techniques in Neo4j. It provides an overview of graph algorithms categories including pathfinding and search, centrality and importance, community detection, similarity, heuristic link prediction, and node embeddings and machine learning. It also summarizes 60+ graph data science techniques available in Neo4j across these categories and how they can be accessed and deployed. Finally, it discusses graph embeddings and graph native machine learning in Neo4j, covering techniques like Node2Vec, GraphSAGE, and FastRP.
The document is a presentation deck on building a supply chain twin using Neo4j and Google technologies. It discusses how supply chain data can be modeled as a graph and stored in Neo4j to power use cases like identifying product and part shortfalls, evaluating supply chain risk, and enabling scenario planning. The deck outlines an architecture that ingests supply chain data from Google BigQuery into Neo4j, then leverages Neo4j technologies like Graph Data Science, Bloom, and Keymaker to operationalize queries and deliver insights to applications.
EY + Neo4j: Why graph technology makes sense for fraud detection and customer...Neo4j
Graph databases can help insurance companies address challenges like siloed data systems, identity resolution issues, and an inability to gain a full view of customers. They allow for a unified customer 360 view across different business units. Graph databases perform better than SQL for data that is interconnected, requires optimal querying of relationships, and has an evolving data model. Specifically for insurance, graphs can increase cross-sell/upsell opportunities, retention rates, and customer satisfaction while reducing costs and fraud. EY has experience implementing graph solutions for use cases like fraud detection and customer 360 projects.
Optimizing Your Supply Chain with the Neo4j GraphNeo4j
With the world’s supply chain system in crisis, it’s clear that better solutions are needed. Digital twins built on knowledge graph technology allow you to achieve an end-to-end view of the process, supporting real-time monitoring of critical assets.
Knowledge Graphs and Generative AI
Dr. Katie Roberts, Data Science Solutions Architect, Neo4j
It’s no secret that Large Language Models (LLMs) are popular right now, especially in the age of Generative AI. LLMs are powerful models that enable access to data and insights for any user, regardless of their technical background, however, they are not without challenges. Hallucinations, generic responses, bias, and a lack of traceability can give organizations pause when thinking about how to take advantage of this technology. Graphs are well suited to ground LLMs as they allow you to take advantage of relationships within your data that are often overlooked with traditional data storage and data science approaches. Combining Knowledge Graphs and LLMs enables contextual and semantic information retrieval from both structured and unstructured data sources. In this session, you’ll learn how graphs and graph data science can be incorporated into your analytics practice, and how a connected data platform can improve explainability, accuracy, and specificity of applications backed by foundation models.
Elsevier: Empowering Knowledge Discovery in Research with GraphsNeo4j
This document summarizes a presentation about enabling knowledge discovery with graphs. It discusses Elsevier's use of Neo4j's graph database to build structured search applications and power recommendations. Some key points include:
- Elsevier connects over 4 billion relationships in its graph, including references, grants, works, authors, and more to enable queries like finding all papers by an author.
- The graph helps build new product experiences across Elsevier's portfolio like enhanced author profiles and citation counts in search results.
- Graphs and embeddings provide a more precise understanding of author expertise and how their fields of study may have changed over time.
- The graph supports data science and accelerates analytics like evaluating academic impact with page rank
Smarter Fraud Detection With Graph Data ScienceNeo4j
Join us for this 20-minute webinar to hear from Nick Johnson, Product Marketing Manager for Graph Data Science, to learn the basics of Neo4j Graph Data Science and how it can help you to identify fraudulent activities faster.
Volvo Cars - Retrieving Safety Insights using Graphs (GraphSummit Stockholm 2...Neo4j
Volvo Cars has developed a map attributes representation as a graph in Neo4j. By including real time car data, they are able to collect insights to learn on possible accident causes based on road infrastructure.
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.
Join us for this 30-minute webinar to hear from Zach Blumenfeld, Neo4j’s Data Science Specialist, to learn the basics of Graph Neural Networks (GNNs) and how they can help you to improve predictions in your data.
Building a modern data stack to maintain an efficient and safe electrical gridNeo4j
An overview of how our organization transitioned to being data-centric, through the implementation of a Enterprise data backbone, and dedicated tools using Neo4j technology, as the various challenges we faced during the process to make the dream come true.
Introduction to Neo4j for the Emirates & BahrainNeo4j
This document provides an agenda and overview of a Neo4j presentation. It discusses Neo4j as the leading native graph database, its graph data science capabilities, and deployment options like Neo4j Aura and Cloud Managed Services. Success stories are highlighted like Minka using Neo4j Aura to power Colombia's new real-time ACH payments system. The presentation aims to demonstrate Neo4j's technology, use cases, and how it can drive business value through connecting data.
The Data Platform for Today’s Intelligent ApplicationsNeo4j
The document discusses how graph technology and Neo4j's graph data platform are fueling data-driven transformations across industries by unlocking deeper insights from relationships within data. It notes that 75% of Fortune 1000 companies had suppliers impacted by the pandemic showing supply chain problems are really data problems. It then promotes Neo4j as the leader in the growing graph database market and discusses its capabilities and customers across industries like insurance, banking, automotive, retail, and telecommunications.
Get Started with the Most Advanced Edition Yet of Neo4j Graph Data ScienceNeo4j
The document discusses Neo4j's graph data science capabilities. It highlights that Neo4j provides tools for graph algorithms, machine learning pipelines for tasks like node classification and link prediction, and a graph catalog for managing graph projections from the underlying database. The document also notes that Neo4j's capabilities allow users to leverage relationships in connected data to answer business questions.
EY + Neo4j: Why graph technology makes sense for fraud detection and customer...Neo4j
This document discusses how graph technology can help with fraud detection and customer 360 projects in the insurance industry. It notes that insurers today struggle with identity resolution, siloed data, and reactive policies. This leads to an inability to get a full customer view or recommend next best actions. Graph databases provide a unified customer view by linking different data sources and modeling relationships. This enables capabilities like predictive analytics, personalization, and improved fraud identification. The document outlines how to build a customer golden profile with a graph database and provides examples of insights that can be gained. It also discusses proving the value of the graph approach and making graphs a long-term, sustainable solution.
The document discusses how graph databases are well-suited for telecommunications networks due to their ability to model complex relationships. It provides examples of large telecom companies using Neo4j to power applications involving network topology analysis, digital twins, and other use cases. Key benefits highlighted include stronger support for dynamic connectivity modeling, improved path calculation, and more flexible approaches compared to relational databases. The presentation outlines Neo4j's adoption among top telecom firms and leadership in the graph database market.
Graphs for Finance - AML with Neo4j Graph Data Science Neo4j
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.
Neo4j is a native graph database that allows organizations to leverage connections in data to create value in real-time. Unlike traditional databases, Neo4j connects data as it stores it, enabling lightning-fast retrieval of relationships. With over 200 customers including Walmart, UBS, and adidas, Neo4j is the number one database for connected data by providing a highly scalable and flexible platform to power use cases like recommendations, fraud detection, and supply chain management through relationship queries and analytics.
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.
Beyond Big Data: Leverage Large-Scale ConnectionsNeo4j
Today’s CIOs and CTOs don’t just need to manage larger volumes of data – they need to generate insight from their existing data. In this case, the relationships between data points matter more than the individual points themselves. In order to leverage data relationships, organizations need a database technology that stores relationship information as a first-class entity. That technology is a graph database.
Attend this webinar to hear about:
1. Why graph technologies are essential for the future of increasingly connected data
2. How enterprises such Walmart, eBay, and UBS are using Neo4j’s native-graph platform for a diverse set of use cases, including security & fraud detection, real-time recommendation engines, master data and many more
3. And how Neo4j on IBM POWER8 can scale your massive graph data with real-time graph processing that’s entirely in-memory.
GraphTalk Hamburg - Einführung in Graphdatenbanken und Neo4jNeo4j
The document outlines an agenda for a GraphTalks event in Hamburg hosted by Neo4j, including introductions to graph databases and Neo4j, new approaches for network and application management using graphs, and how to make graph database projects with Neo4j successful. Several use cases for graph databases are presented, such as fraud detection and real-time recommendations, as well as case studies of companies using Neo4j including eBay and Walmart.
The document discusses graph data science techniques in Neo4j. It provides an overview of graph algorithms categories including pathfinding and search, centrality and importance, community detection, similarity, heuristic link prediction, and node embeddings and machine learning. It also summarizes 60+ graph data science techniques available in Neo4j across these categories and how they can be accessed and deployed. Finally, it discusses graph embeddings and graph native machine learning in Neo4j, covering techniques like Node2Vec, GraphSAGE, and FastRP.
The document is a presentation deck on building a supply chain twin using Neo4j and Google technologies. It discusses how supply chain data can be modeled as a graph and stored in Neo4j to power use cases like identifying product and part shortfalls, evaluating supply chain risk, and enabling scenario planning. The deck outlines an architecture that ingests supply chain data from Google BigQuery into Neo4j, then leverages Neo4j technologies like Graph Data Science, Bloom, and Keymaker to operationalize queries and deliver insights to applications.
EY + Neo4j: Why graph technology makes sense for fraud detection and customer...Neo4j
Graph databases can help insurance companies address challenges like siloed data systems, identity resolution issues, and an inability to gain a full view of customers. They allow for a unified customer 360 view across different business units. Graph databases perform better than SQL for data that is interconnected, requires optimal querying of relationships, and has an evolving data model. Specifically for insurance, graphs can increase cross-sell/upsell opportunities, retention rates, and customer satisfaction while reducing costs and fraud. EY has experience implementing graph solutions for use cases like fraud detection and customer 360 projects.
Optimizing Your Supply Chain with the Neo4j GraphNeo4j
With the world’s supply chain system in crisis, it’s clear that better solutions are needed. Digital twins built on knowledge graph technology allow you to achieve an end-to-end view of the process, supporting real-time monitoring of critical assets.
Knowledge Graphs and Generative AI
Dr. Katie Roberts, Data Science Solutions Architect, Neo4j
It’s no secret that Large Language Models (LLMs) are popular right now, especially in the age of Generative AI. LLMs are powerful models that enable access to data and insights for any user, regardless of their technical background, however, they are not without challenges. Hallucinations, generic responses, bias, and a lack of traceability can give organizations pause when thinking about how to take advantage of this technology. Graphs are well suited to ground LLMs as they allow you to take advantage of relationships within your data that are often overlooked with traditional data storage and data science approaches. Combining Knowledge Graphs and LLMs enables contextual and semantic information retrieval from both structured and unstructured data sources. In this session, you’ll learn how graphs and graph data science can be incorporated into your analytics practice, and how a connected data platform can improve explainability, accuracy, and specificity of applications backed by foundation models.
Elsevier: Empowering Knowledge Discovery in Research with GraphsNeo4j
This document summarizes a presentation about enabling knowledge discovery with graphs. It discusses Elsevier's use of Neo4j's graph database to build structured search applications and power recommendations. Some key points include:
- Elsevier connects over 4 billion relationships in its graph, including references, grants, works, authors, and more to enable queries like finding all papers by an author.
- The graph helps build new product experiences across Elsevier's portfolio like enhanced author profiles and citation counts in search results.
- Graphs and embeddings provide a more precise understanding of author expertise and how their fields of study may have changed over time.
- The graph supports data science and accelerates analytics like evaluating academic impact with page rank
Smarter Fraud Detection With Graph Data ScienceNeo4j
Join us for this 20-minute webinar to hear from Nick Johnson, Product Marketing Manager for Graph Data Science, to learn the basics of Neo4j Graph Data Science and how it can help you to identify fraudulent activities faster.
Volvo Cars - Retrieving Safety Insights using Graphs (GraphSummit Stockholm 2...Neo4j
Volvo Cars has developed a map attributes representation as a graph in Neo4j. By including real time car data, they are able to collect insights to learn on possible accident causes based on road infrastructure.
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.
Join us for this 30-minute webinar to hear from Zach Blumenfeld, Neo4j’s Data Science Specialist, to learn the basics of Graph Neural Networks (GNNs) and how they can help you to improve predictions in your data.
Building a modern data stack to maintain an efficient and safe electrical gridNeo4j
An overview of how our organization transitioned to being data-centric, through the implementation of a Enterprise data backbone, and dedicated tools using Neo4j technology, as the various challenges we faced during the process to make the dream come true.
Introduction to Neo4j for the Emirates & BahrainNeo4j
This document provides an agenda and overview of a Neo4j presentation. It discusses Neo4j as the leading native graph database, its graph data science capabilities, and deployment options like Neo4j Aura and Cloud Managed Services. Success stories are highlighted like Minka using Neo4j Aura to power Colombia's new real-time ACH payments system. The presentation aims to demonstrate Neo4j's technology, use cases, and how it can drive business value through connecting data.
The Data Platform for Today’s Intelligent ApplicationsNeo4j
The document discusses how graph technology and Neo4j's graph data platform are fueling data-driven transformations across industries by unlocking deeper insights from relationships within data. It notes that 75% of Fortune 1000 companies had suppliers impacted by the pandemic showing supply chain problems are really data problems. It then promotes Neo4j as the leader in the growing graph database market and discusses its capabilities and customers across industries like insurance, banking, automotive, retail, and telecommunications.
Get Started with the Most Advanced Edition Yet of Neo4j Graph Data ScienceNeo4j
The document discusses Neo4j's graph data science capabilities. It highlights that Neo4j provides tools for graph algorithms, machine learning pipelines for tasks like node classification and link prediction, and a graph catalog for managing graph projections from the underlying database. The document also notes that Neo4j's capabilities allow users to leverage relationships in connected data to answer business questions.
EY + Neo4j: Why graph technology makes sense for fraud detection and customer...Neo4j
This document discusses how graph technology can help with fraud detection and customer 360 projects in the insurance industry. It notes that insurers today struggle with identity resolution, siloed data, and reactive policies. This leads to an inability to get a full customer view or recommend next best actions. Graph databases provide a unified customer view by linking different data sources and modeling relationships. This enables capabilities like predictive analytics, personalization, and improved fraud identification. The document outlines how to build a customer golden profile with a graph database and provides examples of insights that can be gained. It also discusses proving the value of the graph approach and making graphs a long-term, sustainable solution.
The document discusses how graph databases are well-suited for telecommunications networks due to their ability to model complex relationships. It provides examples of large telecom companies using Neo4j to power applications involving network topology analysis, digital twins, and other use cases. Key benefits highlighted include stronger support for dynamic connectivity modeling, improved path calculation, and more flexible approaches compared to relational databases. The presentation outlines Neo4j's adoption among top telecom firms and leadership in the graph database market.
Graphs for Finance - AML with Neo4j Graph Data Science Neo4j
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.
Neo4j is a native graph database that allows organizations to leverage connections in data to create value in real-time. Unlike traditional databases, Neo4j connects data as it stores it, enabling lightning-fast retrieval of relationships. With over 200 customers including Walmart, UBS, and adidas, Neo4j is the number one database for connected data by providing a highly scalable and flexible platform to power use cases like recommendations, fraud detection, and supply chain management through relationship queries and analytics.
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.
Beyond Big Data: Leverage Large-Scale ConnectionsNeo4j
Today’s CIOs and CTOs don’t just need to manage larger volumes of data – they need to generate insight from their existing data. In this case, the relationships between data points matter more than the individual points themselves. In order to leverage data relationships, organizations need a database technology that stores relationship information as a first-class entity. That technology is a graph database.
Attend this webinar to hear about:
1. Why graph technologies are essential for the future of increasingly connected data
2. How enterprises such Walmart, eBay, and UBS are using Neo4j’s native-graph platform for a diverse set of use cases, including security & fraud detection, real-time recommendation engines, master data and many more
3. And how Neo4j on IBM POWER8 can scale your massive graph data with real-time graph processing that’s entirely in-memory.
GraphTalk Hamburg - Einführung in Graphdatenbanken und Neo4jNeo4j
The document outlines an agenda for a GraphTalks event in Hamburg hosted by Neo4j, including introductions to graph databases and Neo4j, new approaches for network and application management using graphs, and how to make graph database projects with Neo4j successful. Several use cases for graph databases are presented, such as fraud detection and real-time recommendations, as well as case studies of companies using Neo4j including eBay and Walmart.
CTO Radshow Hamburg17 - Keynote - The CxO responsibilities in Big Data and AI...Santiago Cabrera-Naranjo
When talking about how the future of Big Data will look like, this conversation often turns straight to Artificial Intelligence and Deep Learning. However, today data science is all too often a process where new insights and models get developed as a one-time effort or deployed to production on an ad-hoc basis i.e. they commonly require regular babysitting for monitoring and updating.
According to Gartner, the number of useless Data Lakes will be of 90% in 2018. Furthermore, only 15% of Big Data Products are mature enough to be deployed into Production - Who is responsible to make Big Data successful and Business relevant within an enterprise?
Neo4j is a graph database designed for connected data and relationships. It is better suited than relational databases for data that is highly connected with complex relationships. Neo4j has many customers using it for applications that require real-time recommendations, routing, and master data management by leveraging the connected relationships in their data. Examples of customers highlighted include Walmart, eBay, Cisco, and a large logistics carrier.
This document provides an overview of GraphDB and Neo4j. It discusses why graphs are useful for modeling connected data and common use cases. It also summarizes Neo4j's transactional graph database capabilities, performance advantages, and deployment options. Key topics covered include causal clustering, query planning, and driver and tooling support for developers.
Neo4j GraphTalks - Introduction to GraphDatabases and Neo4jNeo4j
This document provides an overview of graph databases and Neo4j. It discusses how graph databases are well-suited for dynamic systems where relationships are important. Neo4j is introduced as a native graph database that is highly scalable and allows organizations to leverage connections in data. Examples are given of companies using Neo4j for applications like recommendations, fraud detection, and network management. Neo4j is presented as an enterprise-ready solution with features like ACID transactions, security, and support for popular languages.
Network and IT Ops Series: Build Production Solutions Neo4j
Jeff Morris, Director, Neo4j:Are you building a breakthrough product or extending an existing one? Do you need introduce new capabilities based on insights from data relationships? If so, you should consider embedding a graph database.
For software providers building products to assure quality network operations or security, using an embedded graph database may open new customer opportunities. Watch this webinar to learn how you can easily differentiate your applications and take your solutions to market faster with a native graph database like Neo4j.
To disrupt and innovate, you need access to data. All of your data. The challenge for many organisations is that the data they need is locked away in a variety of silos. And there's perhaps no bigger silo than one of the most a widely deployed business application: SAP. Bringing together all your data for analytics and machine learning unlocks new insights and business value. Together, Cloudera and Datavard hold the key to breaking SAP data out of its silo, providing access to unlimited and untapped opportunities that currently lay hidden.
How to add security in dataops and devopsUlf Mattsson
The emerging DataOps is not Just DevOps for Data. According to Gartner, DataOps is a collaborative data management practice focused on improving the communication, integration and automation of data flows between data managers and consumers across an organization.
The goal of DataOps is to create predictable delivery and change management of data, data models and related artifacts. DataOps uses technology to automate data delivery with the appropriate levels of security, quality and metadata to improve the use and value of data in a dynamic environment.
This session will discuss how to add Security in DataOps and DevOps.
ADV Slides: How to Improve Your Analytic Data Architecture MaturityDATAVERSITY
Many organizations are immature when it comes to data use. The answer lies in delivering a greater level of insight from data, straight to the point of need. Enter: machine learning.
In this webinar, William will look at categories of organizational response to the challenge across strategy, architecture, modeling, processes, and ethics. Machine learning maturity levels tend to move in harmony across these categories. As a general principle of maturity models, you can’t skip levels in any category, nor can you advance in one category well beyond the others.
Vis-à-vis ML, attaining and retaining momentum up the model is paramount for success. You will ascend the model through concerted efforts delivering business wins utilizing progressive elements of the model, and thereby increasing your machine learning maturity. The model will evolve. No plateaus are comfortable for long.
With ML maturity markers, sequencing, and tactics, this webinar provides a plan for how to build analytic Data Architecture maturity in your organization.
How Data-Driven Approaches are Changing Your Data Management Strategies
Introducing data-driven strategies into your business model alters the way your organization manages and provides information to your customers, partners and employees. Gone are the days of “waterfall” implementation strategies from relational data to applications within a data center. Now, data-driven business models require agile implementation of applications based on information from all across an organization–on-premises, cloud, and mobile–and includes information from outside corporate walls from partners, third-party vendors, and customers. Data management strategies need to be ready to meet these challenges or your new and disruptive business models will fail at the most critical time: when your customers want to access it.
Neo4j GraphDay Seattle- Sept19- Connected data imperativeNeo4j
The document outlines an agenda for a Neo4j Graph Day event including sessions on connected data, graphs and artificial intelligence, a lunch break, Neo4j training, and a reception. Key topics include Neo4j in production environments, its role in boosting artificial intelligence, and training opportunities.
Don't Let Your Shoppers Drop; 5 Rules for Today’s eCommerceDataStax
The definition of eCommerce has totally changed, expanding from a purely retail perspective to mean "the place where your customers meet you online." Whether you offer mortgage services or catering recommendations, you must think of your online transaction application as an eCommerce site.
EDB Postgres is more than just a DBMS. It is a platform that liberates enterprises from expensive DBMS to enable digital business. EDB enterprise customers drive new and modernization IT initiatives using EDB Postgres to minimize risk and maximize savings.
What if IT could free their core database management systems from traditional limitations and become a differentiator instead of a bottleneck to the business?
Learn how the open source based EDB Postgres Platform helps enterprises confidently deliver on the promise of digital business. This presentation covers -
* The updated platform,
* Value for the enterprise,
* Technical differentiators, and
* Customer success stories showing EDB Postgres in action.
This presentation is intended for IT leaders, both management and technical. If you want to lead your organization to accelerate the transformation to digital business, this webinar is for you.
Building the Cognitive Era : Big Data StrategiesKevin Sigliano
This document discusses big data and its applications. It begins with an overview of the growth of data and defines big data. Examples are given of how companies like Walmart, the CIA, and Puig use big data. The challenges of big data including volume, veracity, velocity and variety are described. Common applications of big data like customer insights, marketing, and risk detection are mentioned. The document outlines a roadmap for implementing a big data strategy and discusses technologies and terms. Success cases in fast moving consumer goods are presented. Finally, the benefits of big data for survival, strategic decisions, and cost reductions are noted.
Connecta Event: Big Query och dataanalys med Google Cloud PlatformConnectaDigital
Avancerad dataanalys och ”big data” har under de senaste åren klättrat på trendlistorna och är nu ett av de mest prioriterade områdena i utvecklingen av nya tjänster och produkter för ledarföretag i det digitala landskapet.
Informationen som byggs upp i systemen när kundmötena digitaliseras har visat sig vara guld värt. Här finns allt vi behöver veta för att göra våra affärer mer effektiva.
Sedan sommaren 2013 har Connecta tillsammans med Google ett etablerat samarbete för att hjälpa våra kunder med övergången till moln-tjänster för bland annat avancerad dataanalys. För att göra oss själva redo att hjälpa våra kunder har vi under ett antal år utvecklat såväl kunskaper som skaffat oss erfarenheter kring Googles olika moln-produkter, som exempelvis ”Big Query”.
Big Query är ett molnbaserat analysverktyg och en del av Google Cloud Platform. Big Query gör det möjligt att ställa snabba frågor mot enorma dataset på bara någon sekund. Big Query och Google Cloud Platform erbjuder färdiga lösningar för att sätta upp och underhålla en infrastruktur som med enkla medel gör allt detta möjligt.
På Connecta Digital Consultings tredje event för våren introducerade vi våra kunder och partners i koncepten dataanalys och Big Query.
Under eventet berördes följande punkter:
- Big Data och Business Intelligence (BI)
- “The Google Big Data tools” – framgångsfaktorer och hur man kommer igång
- Google Cloud Platform och hur man genomför en framgångsrik molnsatsning
Vi presenterade case och berättade om viktiga lärdomar vi dragit i samarbetet med Google och våra kunder.
DAMA Webinar: Turn Grand Designs into a Reality with Data VirtualizationDenodo
Watch full webinar here: https://buff.ly/2HMdbUp
What started to evolve as the most agile and real-time enterprise data fabric, data virtualization is proving to go beyond its initial promise and is becoming one of the most important enterprise big data fabrics.
Attend this session to learn:
• What data virtualization really is,
• How it differs from other enterprise data integration technologies
• Real-world examples of data virtualization in action from companies such as Logitech, Autodesk and Festo.
GraphTalk Berlin - Einführung in GraphdatenbankenNeo4j
The document describes an agenda for Neo4j GraphTalks in October 2015 in Germany. The agenda includes:
- Breakfast and networking from 09:00-09:30
- Introduction to graph databases and Neo4j from 09:30-10:00 by Bruno Ungermann from Neo4j
- Kantwert's experience using Neo4j for its first decision network in Germany from 10:00-10:30 by Tilo Walter
- e-Spirit's experience integrating Neo4j into its content management system from 10:30-11:00 by Christoph Feddersen
Data is both our most valuable asset and our biggest ongoing challenge. As data grows in volume, variety and complexity, across applications, clouds and siloed systems, traditional ways of working with data no longer work.
Unlike traditional databases, which arrange data in rows, columns and tables, Neo4j has a flexible structure defined by stored relationships between data records.
We'll discuss the primary use cases for graph databases
Explore the properties of Neo4j that make those use cases possible
Look into the visualisation of graphs
Introduce how to write queries.
Webinar, 23 July 2020
Graphs & GraphRAG - Essential Ingredients for GenAINeo4j
Knowledge graphs are emerging as useful and often necessary for bringing Enterprise GenAI projects from PoC into production. They make GenAI more dependable, transparent and secure across a wide variety of use cases. They are also helpful in GenAI application development: providing a human-navigable view of relevant knowledge that can be queried and visualised.
This talk will share up-to-date learnings from the evolving field of knowledge graphs; why more & more organisations are using knowledge graphs to achieve GenAI successes; and practical definitions, tools, and tips for getting started.
Discover how Neo4j-based GraphRAG and Generative AI empower organisations to deliver hyper-personalised customer experiences. Explore how graph-based knowledge empowers deep context understanding, AI-driven insights, and tailored recommendations to transform customer journeys.
Learn actionable strategies for leveraging Neo4j and Generative AI to revolutionise customer engagement and build lasting relationships.
GraphTalk New Zealand - The Art of The Possible.pptxNeo4j
Discover firsthand how organisations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimising supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
In this presentation, ANZ will be sharing their journey towards AI-enabled data management at scale. The session will explore how they are modernising their data architecture to support advanced analytics and decision-making. By leveraging a knowledge graph approach, they are enhancing data integration, governance, and discovery, breaking down silos to create a unified view across diverse data sources. This enables AI applications to access and contextualise information efficiently, and drive smarter, data-driven outcomes for the bank. They will also share lessons they are learning and key steps for successfully implementing a scalable, AI-ready data framework.
Google Cloud Presentation GraphSummit Melbourne 2024: Building Generative AI ...Neo4j
GenerativeAI is taking the world by storm while traditional ML maturity and successes continue to accelerate across AuNZ . Learn how Google is working with Neo4J to build a ML foundation for trusted, sustainable, and innovative use cases.
Telstra Presentation GraphSummit Melbourne: Optimising Business Outcomes with...Neo4j
This session will highlight how knowledge graphs can significantly enhance business outcomes by supporting the Data Mesh approach. We’ll discuss how knowledge graphs empower organisations to create and manage data products more effectively, enabling a more agile and adaptive data strategy. By leveraging knowledge graphs, businesses can better organise and connect their data assets, driving innovation and maximising the value derived from their data, ultimately leading to more informed decision-making and improved business performance.
Building Smarter GenAI Apps with Knowledge Graphs
While GenAI offers great potential, it faces challenges with hallucination and limited domain knowledge. Graph-powered retrieval augmented generation (GraphRAG) helps overcome these challenges by integrating vector search with knowledge graphs and data science techniques. This approach improves context, enhances semantic understanding, enables personalisation, and facilitates real-time updates.
In this workshop, you’ll explore detailed code examples to kickstart your journey with GenAI and graphs. You’ll leave with practical skills you can immediately apply to your own projects.
How Siemens bolstered supply chain resilience with graph-powered AI insights ...Neo4j
In this captivating session, Siemens will reveal how Neo4j’s powerful graph database technology uncovers hidden data relationships, helping businesses reach new heights in IT excellence. Just as organizations often face unseen barriers, your business may be missing critical insights buried in your data. Discover how Siemens leverages Neo4j to enhance supply chain resilience, boost sustainability, and unlock the potential of AI-driven insights. This session will demonstrate how to navigate complexity, optimize decision-making, and stay ahead in a constantly evolving market.
Knowledge Graphs for AI-Ready Data and Enterprise Deployment - Gartner IT Sym...Neo4j
Knowledge graphs are emerging as useful and often necessary for bringing Enterprise GenAI projects from PoC into production. They make GenAI more dependable, transparent and secure across a wide variety of use cases. They are also helpful in GenAI application development: providing a human-navigable view of relevant knowledge that can be queried and visualised. This talk will share up-to-date learnings from the evolving field of knowledge graphs; why more & more organisations are using knowledge graphs to achieve GenAI successes; and practical definitions, tools, and tips for getting started.
Proactive Vulnerability Detection in Source Code Using Graph Neural Networks:...Ranjan Baisak
As software complexity grows, traditional static analysis tools struggle to detect vulnerabilities with both precision and context—often triggering high false positive rates and developer fatigue. This article explores how Graph Neural Networks (GNNs), when applied to source code representations like Abstract Syntax Trees (ASTs), Control Flow Graphs (CFGs), and Data Flow Graphs (DFGs), can revolutionize vulnerability detection. We break down how GNNs model code semantics more effectively than flat token sequences, and how techniques like attention mechanisms, hybrid graph construction, and feedback loops significantly reduce false positives. With insights from real-world datasets and recent research, this guide shows how to build more reliable, proactive, and interpretable vulnerability detection systems using GNNs.
AgentExchange is Salesforce’s latest innovation, expanding upon the foundation of AppExchange by offering a centralized marketplace for AI-powered digital labor. Designed for Agentblazers, developers, and Salesforce admins, this platform enables the rapid development and deployment of AI agents across industries.
Email: [email protected]
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TestMigrationsInPy: A Dataset of Test Migrations from Unittest to Pytest (MSR...Andre Hora
Unittest and pytest are the most popular testing frameworks in Python. Overall, pytest provides some advantages, including simpler assertion, reuse of fixtures, and interoperability. Due to such benefits, multiple projects in the Python ecosystem have migrated from unittest to pytest. To facilitate the migration, pytest can also run unittest tests, thus, the migration can happen gradually over time. However, the migration can be timeconsuming and take a long time to conclude. In this context, projects would benefit from automated solutions to support the migration process. In this paper, we propose TestMigrationsInPy, a dataset of test migrations from unittest to pytest. TestMigrationsInPy contains 923 real-world migrations performed by developers. Future research proposing novel solutions to migrate frameworks in Python can rely on TestMigrationsInPy as a ground truth. Moreover, as TestMigrationsInPy includes information about the migration type (e.g., changes in assertions or fixtures), our dataset enables novel solutions to be verified effectively, for instance, from simpler assertion migrations to more complex fixture migrations. TestMigrationsInPy is publicly available at: https://github.com/altinoalvesjunior/TestMigrationsInPy.
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Not So Common Memory Leaks in Java WebinarTier1 app
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14. ... Concept Design & Analysis Development Production Process Budget Marketing Product Mgt Customer Feedback ...
Design
CRM
ERP
Budget
Electrical Models
Document Mgt
CAD
PLM
Manufacturing
Product Management
Design
Marketing
Product DNA, Product 360
Metadata
R&D
Testdata
User Requirements
15. Need to capture complexity
Flexibility / Agility
Performance
16. Need to capture complexity
Flexibility / Agility
Performance
17. The US space agency uses Neo4j for their
“Lessons Learned” database to connect
information to improve search ability
effectiveness in space mission.
Knowledge Graph
Finding critical information through a Knowledge Graph
LLIS required you to punch in a keyword which would then produce an
endless, randomly arranged list of links to documents, every one of which
needed to be checked one by one—a process so tedious that NASA
engineers hardly ever consulted the system.
21. Need to capture complexity
Flexibility / Agility
Performance
22. Need to capture complexity
Flexibility / Agility
Performance
23. 23
• Record “Cyber Monday” sales
• About 35M daily transactions
• Each transaction is 3-22 hops
• Queries executed in 4ms or less
• Replaced IBM Websphere commerce
• 300M pricing operations per day
• 10x transaction throughput on half the
hardware compared to Oracle
• Replaced Oracle database
• Large postal service with over 500k
employees
• Neo4j routes 7M+ packages daily at peak,
with peaks of 5,000+ routing operations per
second.
Delivering real-time performance at scale
Real-time promotion
recommendations
Marriott’s Real-time
Pricing Engine
Handling Package
Routing in Real-Time
24. 24
Common Use Case Categories
Network & IT Operations
Application Management
Meta Data ManagementReal-Time
Recommendations
Identity & Access Mgt.
Security
Knowledge
Management
Fraud Detection, AML
Compliance, GDPR
25. Use cases on https://neo4j.com/use-cases/