The document provides an agenda for a Graph Tour presentation which includes introductions, an overview of graphs and Neo4j, use cases in government and finance, digital transformation and the future. Common themes of connectedness in data are discussed. Neo4j is described as an enterprise-grade native graph platform that enables storing, revealing and querying data relationships.
Jeff Morris, Head of Product Marketing at Neo4j, Inc., introduces Graph Tour, which will discuss connectedness as a common theme represented by graphs. The document then discusses how Neo4j is an enterprise-grade native graph platform that allows users to store, reveal, and query data relationships. It provides examples of common use cases for graph technology like fraud detection and knowledge graphs. The document emphasizes that connectedness drives data value and that Neo4j reveals connections in data through its native graph architecture.
Anti-Fraud and eDiscovery using Graph Databases and Graph Visualization - Cor...Neo4j
Investigating fraud often involves identifying suspicious patterns among mountains of uninteresting transactional data. A new partnership between Neo Technologies and Cambridge Intelligence allows fraud investigators and data analysts to uncover these patters far more easily. By combining the power of Neo4j's graph database and the visualization capabilities of KeyLines, a web-based graph visualization engine tightly integrated with Neo4j's data model, these investigators and analysts can visually drill down from aggregate data to the individual suspicious data elements quickly and without requiring significant technical expertise in query languages. This presentation will summarize the Neo Technology and Cambridge Intelligence partnership, discuss the technical integration between the two products, and demonstrate a number of different scenarios of uncovering fraud across multiple domains and data types.
1) The document discusses how graph databases like Neo4j can help governments in areas like law enforcement, security, anti-money laundering, and e-government. It provides examples of how criminal networks, financial transactions, and citizen records can be modeled as graphs.
2) Graphs allow the explicit and implicit connections in data to be readily apparent, which can help with investigations, fraud detection, and improving access to services. This is in contrast to traditional relational databases that don't show relationships as clearly.
3) Adopting a graph database approach could help governments address challenges like siloed information, legacy systems, and lack of efficiency across departments by providing a connected view of data.
Graphs and innovative graph solutions for financial servicesNeo4j
This document discusses how graphs and graph databases can be used for financial services applications. It provides three potential use cases:
1. Measuring risk in corporate lending by understanding the relationships between businesses and how they are connected through partnerships, supply chains, etc.
2. Understanding exposure to commodities or industries by analyzing how businesses and customers are related to different commodities and how changes in commodities may impact them.
3. Predicting customer behavior for investment banking by using graph analysis to better understand customer relationships and trading patterns to anticipate future wants and needs.
Folks, recently I was invited by re-work to be a speaker at the Deep Learning in Finance Summit held in Singapore. First of all, I wanted to thank the folks @ rework for organizing this fantastic event and inviting many talented speakers from the industry and academia. The entire 2 days agenda was a great platform to learn more about the latest happening in this area.
Regarding my presentation- The topic was “ Deep Learning & Fraud Detection in Fintech Lending”. Some of the key points that were covered during this presentation are-
Types of fintech
Key drivers for fraud in fintech lending
Common fraud modus operandi ( MOs) in fintech lending
Why deep learning for fraud detection
Sample deep learning application areas in fraud detection-
Anomaly detection using Autoencoder/ Replicator Neural Network
Social network analysis ( SNA)
Demo of Multi Layer Perceptron ( MLP) deep learning classifier built using Python, Tensorflow and Keras along with vital statistical parameters such as accuracy, logloss, precision, recall, fscore etc.
I am attaching the full presentation here. Do share your thoughts…
Happy reading.
Cheers!
-RP
The presentation will cover common cybercrime trends in Europe and techniques for locating cybercrime suspects. The first part will discuss expected future threats, attacks targeting businesses, and challenges in gathering digital evidence. The second part will demonstrate how to locate suspects using email, Skype, and other services, and how protocols have changed. It will also show how to find locations from TeamViewer IDs or phone numbers. All recommendations are based on real cases involving Serbian and regional companies.
The document discusses using graph databases to help financial institutions combat money laundering. It outlines some of the challenges of using traditional relational databases to track connected data like payment chains and complex transactions. The document then provides an example data model for representing anti-money laundering data as a graph to better identify risks, connections, and suspicious activity. It highlights key entities like account holders, transactions, addresses, devices, and their relationships that are important to model.
Jeff Morris, Head of Product Marketing at Neo4j, Inc., introduces Graph Tour, which will discuss connectedness as a common theme represented by graphs. The document then discusses how Neo4j is an enterprise-grade native graph platform that allows users to store, reveal, and query data relationships. It provides examples of common use cases for graph technology like fraud detection and knowledge graphs. The document emphasizes that connectedness drives data value and that Neo4j reveals connections in data through its native graph architecture.
Anti-Fraud and eDiscovery using Graph Databases and Graph Visualization - Cor...Neo4j
Investigating fraud often involves identifying suspicious patterns among mountains of uninteresting transactional data. A new partnership between Neo Technologies and Cambridge Intelligence allows fraud investigators and data analysts to uncover these patters far more easily. By combining the power of Neo4j's graph database and the visualization capabilities of KeyLines, a web-based graph visualization engine tightly integrated with Neo4j's data model, these investigators and analysts can visually drill down from aggregate data to the individual suspicious data elements quickly and without requiring significant technical expertise in query languages. This presentation will summarize the Neo Technology and Cambridge Intelligence partnership, discuss the technical integration between the two products, and demonstrate a number of different scenarios of uncovering fraud across multiple domains and data types.
1) The document discusses how graph databases like Neo4j can help governments in areas like law enforcement, security, anti-money laundering, and e-government. It provides examples of how criminal networks, financial transactions, and citizen records can be modeled as graphs.
2) Graphs allow the explicit and implicit connections in data to be readily apparent, which can help with investigations, fraud detection, and improving access to services. This is in contrast to traditional relational databases that don't show relationships as clearly.
3) Adopting a graph database approach could help governments address challenges like siloed information, legacy systems, and lack of efficiency across departments by providing a connected view of data.
Graphs and innovative graph solutions for financial servicesNeo4j
This document discusses how graphs and graph databases can be used for financial services applications. It provides three potential use cases:
1. Measuring risk in corporate lending by understanding the relationships between businesses and how they are connected through partnerships, supply chains, etc.
2. Understanding exposure to commodities or industries by analyzing how businesses and customers are related to different commodities and how changes in commodities may impact them.
3. Predicting customer behavior for investment banking by using graph analysis to better understand customer relationships and trading patterns to anticipate future wants and needs.
Folks, recently I was invited by re-work to be a speaker at the Deep Learning in Finance Summit held in Singapore. First of all, I wanted to thank the folks @ rework for organizing this fantastic event and inviting many talented speakers from the industry and academia. The entire 2 days agenda was a great platform to learn more about the latest happening in this area.
Regarding my presentation- The topic was “ Deep Learning & Fraud Detection in Fintech Lending”. Some of the key points that were covered during this presentation are-
Types of fintech
Key drivers for fraud in fintech lending
Common fraud modus operandi ( MOs) in fintech lending
Why deep learning for fraud detection
Sample deep learning application areas in fraud detection-
Anomaly detection using Autoencoder/ Replicator Neural Network
Social network analysis ( SNA)
Demo of Multi Layer Perceptron ( MLP) deep learning classifier built using Python, Tensorflow and Keras along with vital statistical parameters such as accuracy, logloss, precision, recall, fscore etc.
I am attaching the full presentation here. Do share your thoughts…
Happy reading.
Cheers!
-RP
The presentation will cover common cybercrime trends in Europe and techniques for locating cybercrime suspects. The first part will discuss expected future threats, attacks targeting businesses, and challenges in gathering digital evidence. The second part will demonstrate how to locate suspects using email, Skype, and other services, and how protocols have changed. It will also show how to find locations from TeamViewer IDs or phone numbers. All recommendations are based on real cases involving Serbian and regional companies.
The document discusses using graph databases to help financial institutions combat money laundering. It outlines some of the challenges of using traditional relational databases to track connected data like payment chains and complex transactions. The document then provides an example data model for representing anti-money laundering data as a graph to better identify risks, connections, and suspicious activity. It highlights key entities like account holders, transactions, addresses, devices, and their relationships that are important to model.
The document discusses two investigative journalism case studies where Neo4j was used to analyze large leaked datasets and reveal connections between people, entities, and accounts. In the first case study, Neo4j helped journalists expose corruption related to the Panama Papers leak. In the second case study, Neo4j helped journalists win a Pulitzer Prize for their investigation of the Paradise Papers leak.
With the introduction of the Neo4j Graph Platform and increased adoption of graph database technology across all industries, now is a better time than ever to get started with graphs.
Join us for this introduction to Neo4j and graph databases. We'll discuss the primary use cases for graph databases and explore the properties of Neo4j that make those use cases possible.
AI, ML and Graph Algorithms: Real Life Use Cases with Neo4jIvan Zoratti
I gave this presentation at DataOps 19 in Barcelona.
You will find information about Neo4j and how to use it with Graph Algorithms for Machine Learning and Artificial Intelligence.
The document summarizes an agenda for a Neo4j GraphTour event in Milan. It includes:
1. Welcome messages from Neo4j team members and an overview of the agenda which will focus on making connections and learning about graph databases.
2. A discussion of the state of graph technologies and their increasing popularity and adoption by enterprises in various industries.
3. An explanation of how graphs are enabling new applications and use cases, and fueling three waves of graph adoption related to relationships, recommendations, and AI.
4. An overview of how Neo4j is enhancing its platform to support analytics, tooling, and graph-enhanced AI and machine learning techniques.
5
Translating the Human Analog to Digital with GraphsNeo4j
Jeff Morris presented on how graphs can translate human analog activities and relationships to the digital world. Some key points:
1) Graphs can represent people, objects, locations, events and their relationships, capturing information like who, what, where, when, why and how. This models human analog data.
2) Modeling data as graphs allows representing complex relationships that are difficult to uncover with traditional methods. This helps with applications like fraud detection.
3) Graphs are well-suited to power applications like recommendations, smart homes, fraud detection and more by combining diverse data sources and identifying new connections.
The document discusses knowledge graphs and provides examples of how Neo4j has been used by customers for knowledge graph and graph database applications. Specifically, it discusses how Neo4j has helped organizations like Itau Unibanco, UBS, Airbnb, Novartis, Columbia University, Telia, Scripps Networks, and Pitney Bowes with fraud detection, master data management, content management, smart home applications, investigative journalism, and other use cases by building knowledge graphs and connecting diverse data sources.
Keynote: Graphs in Government_Lance Walter, CMONeo4j
This document contains an agenda and presentation slides for a Neo4j Graphs in Government event. The presentation introduces graph databases and Neo4j, discusses how graphs can help solve network-oriented problems, provides examples of graph use cases in various industries, and highlights new features in Neo4j 4.0 like easy management, unlimited scaling, and granular security. Case studies demonstrate how Neo4j has helped organizations like the US Army, MITRE, Adobe, and the German Center for Diabetes Research tackle complex data challenges.
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.
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This document discusses several real-world use cases for graph databases across different industries:
1) It describes how graph databases have been used for master data management by companies like die Bayerische insurance and Classmates social network to create a unified view of customer and organizational data.
2) Graphs have also been applied to network and IT operations management by the Royal Netherlands Meteorological Institute to optimize infrastructure and by Telenor for identity and access management.
3) Fraud detection in industries like banking, insurance, and ecommerce is another common use case, with graphs helping to connect discrete user accounts and transactions to detect rings of fraudulent activity.
EVOLVING PATTERNS IN BIG DATA - NEIL AVERYBig Data Week
The document discusses evolving patterns in big data usage, including enterprise data caching using massive key-value stores, enterprise messaging pipes using Kafka, and NoSQL as a service. It also covers data lakes for centralized raw data storage and Lambda architecture for near real-time and batch processing. Current trends include growing Cassandra usage, Kafka for scalable messaging, and containerization and cloud adoption. Future areas may include graph databases, Spark evolution, and data virtualization.
GraphTalk Helsinki - Introduction to Graphs and Neo4jNeo4j
The document provides an agenda for a Neo4j event. It includes presentations on Neo4j and graph databases from 10:00-12:00 followed by Q&A and networking. It also provides an overview of Neo4j including its adoption, funding, ecosystem, use cases and the Neo4j graph platform.
This document discusses how to build next generation fraud solutions using Neo4j graph database technology. It begins by outlining the challenges of fraud and how traditional relational databases are inadequate for detecting complex fraud patterns. It then describes how graph databases like Neo4j can provide a 360-degree view of connected customer and transaction data to enable real-time fraud detection. Examples of fraud use cases where Neo4j has been successfully applied are also provided, followed by an overview of how to architect a fraud solution leveraging Neo4j's graph capabilities.
GraphTour Keynote, Emil Eifrem, CEO and Founder, Neo4jNeo4j
This document discusses graphs and graph databases. It begins with an agenda about graphs 101, the state of graph databases, and the future of graphs. It then provides examples of how graphs can be used for applications like fraud detection and knowledge graphs. The document discusses how the use of graph databases has grown significantly in recent years and is expected to continue growing. It also provides examples of large companies that use graph databases and discusses how graphs can enhance artificial intelligence by providing connections and context.
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1) Neo4j is a native graph database platform that allows users to store, reveal, and query data relationships in real-time. It is designed specifically for graph databases.
2) Graph databases represent data as nodes and relationships, which provides a more connected view of data compared to relational databases. This connected view of data drives insights and applications in areas like recommendations, fraud detection, and knowledge graphs.
3) Neo4j has over 250 enterprise customers across industries like retail, financial services, and telecom. It is widely used for applications like recommendations, fraud detection, network analysis, and knowledge graphs.
SiriusDecisions Explores the Need for Demand OrchestrationIntegrate
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This SlideShare features content from Kerry Cunningham of Sirius Decisions and Lena Waters of Lookout discussing today’s B2B marketing climate – specifically the growing need for Demand Orchestration as marketing teams become responsible for revenue, not just leads.
Content was originally featured in a live webinar on 4.6.2017. The on-demand webinar can be viewed here: https://discover.integrate.com/webinar_the_move_to_demand_orchestration
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This document provides an agenda and overview of a Neo4j GraphTour event in Santa Monica on September 18, 2019. The agenda includes introductions to graphs, data management trends, case studies showing graph database uses, and a discussion of the future of graphs. It promotes upcoming Neo4j training events on graph data modeling and the GraphConnect conference in 2020. Case studies demonstrate how industries like healthcare, retail, finance, software, and transportation use graph databases for fraud detection, recommendations, network operations, master data management, and other use cases. The document discusses trends in data and analytics technologies including growing adoption of graph databases and their synergies with artificial intelligence.
Introduction to the Neo4j Graph Platform & use casesNeo4j
The document is an agenda for a Neo4j GraphTalks event in Madrid on network and application management. It includes an introduction to graph databases and Neo4j, new approaches for network and application management with graphs, and how to succeed with Neo4j graph database projects. There are also sessions on the impact of graphs and the state of the graph database field.
This document discusses best practices for using Hadoop as an enterprise data hub. It provides an overview of how big data is driving new analytical workloads and the need for deeper customer insights. It discusses challenges with analyzing new sources of structured, unstructured and multi-structured data. It introduces the concept of a Hadoop enterprise data hub and data refinery to simplify access to new insights from big data. Key components of the data hub include a data reservoir to capture raw data from various sources, a data refinery to cleanse and transform the data, and publishing high value insights to data warehouses and other systems.
Atelier - Architecture d’applications de Graphes - GraphSummit ParisNeo4j
Atelier - Architecture d’applications de Graphes
Participez à cet atelier pratique animé par des experts de Neo4j qui vous guideront pour découvrir l’intelligence contextuelle. En utilisant un jeu de données réel, nous construirons étape par étape une solution de graphes ; de la construction du modèle de données de graphes à l’exécution de requêtes et à la visualisation des données. L’approche sera applicable à de multiples cas d’usages et industries.
Atelier - Innover avec l’IA Générative et les graphes de connaissancesNeo4j
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Allez au-delà du battage médiatique autour de l’IA et découvrez des techniques pratiques pour utiliser l’IA de manière responsable à travers les données de votre organisation. Explorez comment utiliser les graphes de connaissances pour augmenter la précision, la transparence et la capacité d’explication dans les systèmes d’IA générative. Vous partirez avec une expérience pratique combinant les relations entre les données et les LLM pour apporter du contexte spécifique à votre domaine et améliorer votre raisonnement.
Amenez votre ordinateur portable et nous vous guiderons sur la mise en place de votre propre pile d’IA générative, en vous fournissant des exemples pratiques et codés pour démarrer en quelques minutes.
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The document discusses two investigative journalism case studies where Neo4j was used to analyze large leaked datasets and reveal connections between people, entities, and accounts. In the first case study, Neo4j helped journalists expose corruption related to the Panama Papers leak. In the second case study, Neo4j helped journalists win a Pulitzer Prize for their investigation of the Paradise Papers leak.
With the introduction of the Neo4j Graph Platform and increased adoption of graph database technology across all industries, now is a better time than ever to get started with graphs.
Join us for this introduction to Neo4j and graph databases. We'll discuss the primary use cases for graph databases and explore the properties of Neo4j that make those use cases possible.
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I gave this presentation at DataOps 19 in Barcelona.
You will find information about Neo4j and how to use it with Graph Algorithms for Machine Learning and Artificial Intelligence.
The document summarizes an agenda for a Neo4j GraphTour event in Milan. It includes:
1. Welcome messages from Neo4j team members and an overview of the agenda which will focus on making connections and learning about graph databases.
2. A discussion of the state of graph technologies and their increasing popularity and adoption by enterprises in various industries.
3. An explanation of how graphs are enabling new applications and use cases, and fueling three waves of graph adoption related to relationships, recommendations, and AI.
4. An overview of how Neo4j is enhancing its platform to support analytics, tooling, and graph-enhanced AI and machine learning techniques.
5
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Jeff Morris presented on how graphs can translate human analog activities and relationships to the digital world. Some key points:
1) Graphs can represent people, objects, locations, events and their relationships, capturing information like who, what, where, when, why and how. This models human analog data.
2) Modeling data as graphs allows representing complex relationships that are difficult to uncover with traditional methods. This helps with applications like fraud detection.
3) Graphs are well-suited to power applications like recommendations, smart homes, fraud detection and more by combining diverse data sources and identifying new connections.
The document discusses knowledge graphs and provides examples of how Neo4j has been used by customers for knowledge graph and graph database applications. Specifically, it discusses how Neo4j has helped organizations like Itau Unibanco, UBS, Airbnb, Novartis, Columbia University, Telia, Scripps Networks, and Pitney Bowes with fraud detection, master data management, content management, smart home applications, investigative journalism, and other use cases by building knowledge graphs and connecting diverse data sources.
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This document contains an agenda and presentation slides for a Neo4j Graphs in Government event. The presentation introduces graph databases and Neo4j, discusses how graphs can help solve network-oriented problems, provides examples of graph use cases in various industries, and highlights new features in Neo4j 4.0 like easy management, unlimited scaling, and granular security. Case studies demonstrate how Neo4j has helped organizations like the US Army, MITRE, Adobe, and the German Center for Diabetes Research tackle complex data challenges.
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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.
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This document discusses several real-world use cases for graph databases across different industries:
1) It describes how graph databases have been used for master data management by companies like die Bayerische insurance and Classmates social network to create a unified view of customer and organizational data.
2) Graphs have also been applied to network and IT operations management by the Royal Netherlands Meteorological Institute to optimize infrastructure and by Telenor for identity and access management.
3) Fraud detection in industries like banking, insurance, and ecommerce is another common use case, with graphs helping to connect discrete user accounts and transactions to detect rings of fraudulent activity.
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This document discusses how to build next generation fraud solutions using Neo4j graph database technology. It begins by outlining the challenges of fraud and how traditional relational databases are inadequate for detecting complex fraud patterns. It then describes how graph databases like Neo4j can provide a 360-degree view of connected customer and transaction data to enable real-time fraud detection. Examples of fraud use cases where Neo4j has been successfully applied are also provided, followed by an overview of how to architect a fraud solution leveraging Neo4j's graph capabilities.
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This document discusses graphs and graph databases. It begins with an agenda about graphs 101, the state of graph databases, and the future of graphs. It then provides examples of how graphs can be used for applications like fraud detection and knowledge graphs. The document discusses how the use of graph databases has grown significantly in recent years and is expected to continue growing. It also provides examples of large companies that use graph databases and discusses how graphs can enhance artificial intelligence by providing connections and context.
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1) Neo4j is a native graph database platform that allows users to store, reveal, and query data relationships in real-time. It is designed specifically for graph databases.
2) Graph databases represent data as nodes and relationships, which provides a more connected view of data compared to relational databases. This connected view of data drives insights and applications in areas like recommendations, fraud detection, and knowledge graphs.
3) Neo4j has over 250 enterprise customers across industries like retail, financial services, and telecom. It is widely used for applications like recommendations, fraud detection, network analysis, and knowledge graphs.
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This SlideShare features content from Kerry Cunningham of Sirius Decisions and Lena Waters of Lookout discussing today’s B2B marketing climate – specifically the growing need for Demand Orchestration as marketing teams become responsible for revenue, not just leads.
Content was originally featured in a live webinar on 4.6.2017. The on-demand webinar can be viewed here: https://discover.integrate.com/webinar_the_move_to_demand_orchestration
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GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
GraphAware - Transforming policing with graph-based intelligence analysisNeo4j
Petr Matuska, Sales & Sales Engineering Lead, GraphAware
Western Australia Police Force’s adoption of Neo4j and the GraphAware Hume graph analytics platform marks a significant advancement in data-driven policing. Facing the challenges of growing volumes of valuable data scattered in disconnected silos, the organisation successfully implemented Neo4j database and Hume, consolidating data from various sources into a dynamic knowledge graph. The result was a connected view of intelligence, making it easier for analysts to solve crime faster. The partnership between Neo4j and GraphAware in this project demonstrates the transformative impact of graph technology on law enforcement’s ability to leverage growing volumes of valuable data to prevent crime and protect communities.
GraphSummit Stockholm - Neo4j - Knowledge Graphs and Product UpdatesNeo4j
David Pond, Lead Product Manager, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
Shirley Bacso, Data Architect, Ingka Digital
“Linked Metadata by Design” represents the integration of the outcomes from human collaboration, starting from the design phase of data product development. This knowledge is captured in the Data Knowledge Graph. It not only enables data products to be robust and compliant but also well-understood and effectively utilized.
Your enemies use GenAI too - staying ahead of fraud with Neo4jNeo4j
Delivered by Michael Down at Gartner Data & Analytics Summit London 2024 - Your enemies use GenAI too: Staying ahead of fraud with Neo4j.
Fraudsters exploit the latest technologies like generative AI to stay undetected. Static applications can’t adapt quickly enough. Learn why you should build flexible fraud detection apps on Neo4j’s native graph database combined with advanced data science algorithms. Uncover complex fraud patterns in real-time and shut down schemes before they cause damage.
BT & Neo4j _ How Knowledge Graphs help BT deliver Digital Transformation.pptxNeo4j
Delivered by Sreenath Gopalakrishna, Director of Software Engineering at BT, and Dr Jim Webber, Chief Scientist at Neo4j, at Gartner Data & Analytics Summit London 2024 this presentation examines how knowledge graphs and GenAI combine in real-world solutions.
BT Group has used the Neo4j Graph Database to enable impressive digital transformation programs over the last 6 years. By re-imagining their operational support systems to adopt self-serve and data lead principles they have substantially reduced the number of applications and complexity of their operations. The result has been a substantial reduction in risk and costs while improving time to value, innovation, and process automation. Future innovation plans include the exploration of uses of EKG + Generative AI.
Workshop: Enabling GenAI Breakthroughs with Knowledge Graphs - GraphSummit MilanNeo4j
Look beyond the hype and unlock practical techniques to responsibly activate intelligence across your organization’s data with GenAI. Explore how to use knowledge graphs to increase accuracy, transparency, and explainability within generative AI systems. You’ll depart with hands-on experience combining relationships and LLMs for increased domain-specific context and enhanced reasoning.
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
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!
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
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.
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
Infrastructure Challenges in Scaling RAG with Custom AI modelsZilliz
Building Retrieval-Augmented Generation (RAG) systems with open-source and custom AI models is a complex task. This talk explores the challenges in productionizing RAG systems, including retrieval performance, response synthesis, and evaluation. We’ll discuss how to leverage open-source models like text embeddings, language models, and custom fine-tuned models to enhance RAG performance. Additionally, we’ll cover how BentoML can help orchestrate and scale these AI components efficiently, ensuring seamless deployment and management of RAG systems in the cloud.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
“An Outlook of the Ongoing and Future Relationship between Blockchain Technologies and Process-aware Information Systems.” Invited talk at the joint workshop on Blockchain for Information Systems (BC4IS) and Blockchain for Trusted Data Sharing (B4TDS), co-located with with the 36th International Conference on Advanced Information Systems Engineering (CAiSE), 3 June 2024, Limassol, Cyprus.
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!SOFTTECHHUB
As the digital landscape continually evolves, operating systems play a critical role in shaping user experiences and productivity. The launch of Nitrux Linux 3.5.0 marks a significant milestone, offering a robust alternative to traditional systems such as Windows 11. This article delves into the essence of Nitrux Linux 3.5.0, exploring its unique features, advantages, and how it stands as a compelling choice for both casual users and tech enthusiasts.
5. Connectedness Represented in Graphs
C
C
A AA
U
S S SS S
USER_ACCESS
CONTROLLED_BY
SUBSCRIBED _BY
User
Customers
Accounts
Subscriptions
VP
Staff Staff StaffStaff
DirectorStaffDirector
Manager Manager Manager Manager
Fiber
Link
Fiber
Link
Fiber
Link
Ocean
Cable
Switch Switch
Router Router
Service
Organizational
Hierarchy
Product
Subscriptions
Network
Operations
Social
Networks
6. Who We Are: The Graph Platform
Neo4j is an enterprise-grade native graph platform that enables you to:
• Store, reveal and query data relationships
• Traverse and analyze any levels of depth in real-time
• Add context and connect new data on the fly
• Performance
• ACID Transactions
• Agility
• Graph Algorithms
6
Designed, built and tested natively
for graphs from the start for:
• Developer Productivity
• Hardware Efficiency
• Global Scale
• Graph Adoption
7. The Rise of Connections in Data
Networks of People Business Processes Knowledge Networks
E.g., Risk management, Supply
chain, Payments
E.g., Employees, Customers,
Suppliers, Partners,
Influencers
E.g., Enterprise content,
Domain specific content,
eCommerce content
Data connections are increasing as rapidly as data volumes
13. CAR
name: “Dan”
born: May 29, 1970
twitter: “@dan”
name: “Ann”
born: Dec 5, 1975
since:
Jan 10, 2011
brand: “Volvo”
model: “V70”
Neo4j Invented the Labeled Property Graph Model
Nodes
• Can have Labels to classify nodes
• Labels have native indexes
Relationships
• Relate nodes by type and direction
Properties
• Attributes of Nodes & Relationships
• Stored as Name/Value pairs
• Can have indexes and composite
indexes
MARRIED TO
LIVES WITH
PERSON PERSON
13
15. 10M+
Downloads
3M+ from Neo4j Distribution
7M+ from Docker
Events
400+
Approximate Number of
Neo4j Events per Year
50k+
Meetups
Number of Meetup
Members Globally
Largest pool of graph technologists
50k+
Trained/certified Neo4j
professionals
Trained Developers
16. How Neo4j Fits — Common Architecture Patterns
From Disparate Silos
To Cross-Silo Connections
From Tabular Data
To Connected Data
From Data Lake Analytics
to Real-Time Operations
19. Connecting Roles & Projects around Enterprise Data Hub
Data Scientists
Real-time
Graph traversal
Applications
Developers
& Prod Mgrs
Analysts and
Business Users
Chief Officers of …
Compliance, Data, Digital,
Information, Innovation,
Marketing, Operations, Risk &
Security…
Big Data IT &
Architecture
ID, Auth & Security
Network & IT Ops
Metadata
Management
360⁰
Marketing
Customer 360
Real-time
Cybersecurity
Account navigation
• Multiple paths through
organization
• Graphs have strong
appetite for data to add
nodes & increase density
of relationships
• Value of graph increase
according to Metcalfe’s
Law (V=n2)
• Customer applications
iterate every 3 months
23. “Lessons Learned Database”
A half-century of collective NASA engineering
knowledge
“How do we make sure the command module
doesn’t tip over and sink?”
24. Let’s Hear a Few Stories
— David Meza, Chief Knowledge
Architect at NASA
“Neo4j saved well over two
years of work and one million
dollars of taxpayer funds.”
Impact
26. Business Problem
• Find relationships between people, accounts,
shell companies and offshore accounts
• Journalists are non-technical
• Biggest “Snowden-Style” document leak ever;
11.5 million documents, 2.6TB of data
Solution and Benefits
• Pulitzer Prize winning investigation resulted in
robust coverage of fraud and corruption
• PM of Iceland & Pakistan resigned, exposed
Putin, Prime Ministers, gangsters, celebrities
(Messi)
• Led to assassination of journalist in Malta
Background
• International Consortium of Investigative
Journalists (ICIJ), small team of data journalists
• International investigative team specializing in
cross-border crime, corruption and accountability
of power
• Works regularly with leaks and large datasets
ICIJ Panama Papers INVESTIGATIVE JOURNALISM
Fraud Detection / Knowledge Graph26
30. Business Problem
• Find relationships between people, corporations,
accounts, shell companies and offshore accounts
• Journalists are non-technical
• 2017 Leak from Appleby tax sheltering law firm
matched 13.4 million account records with public
business registrations data from across Caribbean
Solution and Benefits
• Exposed tax sheltering practices of Apple, Nike
• Revealed hidden connections among politicians
and nations, like Wilbur Ross & Putin’s son in law
• Triggered government tax evasion investigations in
US, UK, Europe, India, Australia, Bermuda, Canada
and Cayman Islands within 2 days.
Background
• International Consortium of Investigative
Journalists (ICIJ), Pulitzer Prize winning journalists
• Fourth blockbuster investigation using Neo4j to
reveal connections in text-based, and account-
based data leaked from offshore law firms and
government records about the “1% Elite”
• Appends Neo4j-based, “Offshore Leaks Database”
ICIJ Paradise Papers INVESTIGATIVE JOURNALISM
Fraud Detection / Knowledge Graph30
33. Endpoint-Centric
Analysis of users and
their end-points
1.
Navigation Centric
Analysis of
navigation behavior
and suspect patterns
2.
Account-Centric
Analysis of anomaly
behavior by channel
3.
PC:s
Mobile Phones
IP-addresses
User ID:s
Comparing Transaction
Identity Vetting
Traditional Fraud Detection Methods
DISCRETE ANALYSIS
34. Endpoint-Centric
Analysis of users and
their end-points
1.
Navigation Centric
Analysis of
navigation behavior
and suspect patterns
2.
Account-Centric
Analysis of anomaly
behavior by channel
3.
PC:s
Mobile Phones
IP-addresses
User ID:s
Comparing Transaction
Identity Vetting
Traditional Fraud Detection Methods
DISCRETE ANALYSIS Unable to detect
• Fraud rings
• Fake IP addresses
• Hijacked devices
• Synthetic Identities
• Stolen Identities
• And more…
Weaknesses
35. Entity Linking
Analysis of relationships
to detect organized
crime and collusion
5.
Endpoint-Centric
Analysis of users and
their end-points
1.
Navigation Centric
Analysis of
navigation behavior
and suspect patterns
2.
Account-Centric
Analysis of anomaly
behavior by channel
3.
PC:s
Mobile Phones
IP-addresses
User ID:s
Comparing Transaction
Identity Vetting
Augment Methods by Examining Connected Data
DISCRETE ANALYSIS CONNECTED ANALYSIS
Cross Channel
Analysis of anomaly
behavior correlated
across channels
4.
36. ACCOUNT
HOLDER 2
ACCOUNT
HOLDER 1
ACCOUNT
HOLDER 3
CREDIT
CARD
BANK
ACCOUNT
BANK
ACCOUNT
BANK
ACCOUNT
PHONE
NUMBER
UNSECURED
LOAN
SSN 2
UNSECURED
LOAN
Modeling Fraud Transactions as an Organized Ring
At first glance, each
account holder
looks normal.
Each has multiple
accounts…
39. Internal Risk Models Span Investment Data Silos
Graph technology unites discrete silos into a unified data source
that enables banks to trace compliance data lineage
40. Tracing Risk Lineage
FRTB requires banks to
calculate investment risk at the
trading-desk level…
…requiring them to evaluate
the interrelatedness of every
investment on their books.
Graph technology is
the right solution
41. The Role of Ontologies in Financial Risk Management
43. Business Problem
• Needed new asset management backbone to
handle scheduling, ads, sales and pushing
linear streams to satellites
• Novell LDAP content hierarchy not flexible
enough to store graph-based business content
Solution and Benefits
• Neo4j selected for performance and domain fit
• Flexible, native storage of content hierarchy
• Graph includes metadata used by all systems:
TV series-->Episodes-->Blocks with Tags-->
Linked Content, tagged with legal rights, actors,
dubbing et al
Background
• Nashville-based developer of lifestyle-
oriented content for TV, digital, mobile and
publishing
• Web properties generate tens of millions of
unique visitors per month
Scripps Networks MEDIA AND ENTERTAINMENT
Master Data Management43
44. Background
• SF-based C2C rental platform
• Dataportal democratizes data access for
growing number of employees while improving
discoverability and trust
• Data strewn everywhere—in silos, in segmented
departments, nothing was universally accessible
Business Problem
• Data-driven culture hampered by variety and
dependability of data, tribal knowledge and
word-of-mouth distribution
• Needed visibility into information usage, context,
lineage and popularity across company of 3,000+
Solution and Benefits
• Offers search with context & metadata, user &
team-centric pages for origin & lineage
• Nodes are resources: data tables, dashboards,
reports, users, teams, business outcomes, etc.
• Relationships reflect consumption, production,
association, etc.
• Neo4j, Elasticsearch, Python
Airbnb Dataportal TRAVEL TECHNOLOGY
Knowledge Graph, Metadata Management44
CE users since 2017
46. "Neo4j continues to dominate
the graph database market.”
October, 2017
“Customers choose Neo4j
to drive innovation.”
February, 2018
“In fact, the rapid rise of Neo4j and
other graph technologies may
signal that data connectedness is
indeed a separate paradigm from
the model consolidation happening
across the rest of the NoSQL
landscape.”
March, 2018
Graph is a Unique Paradigm
47. 47
Harnessing Connections Drives Business Value
Enhanced Decision
Making
Hyper
Personalization
Massive Data
Integration
Data Driven Discovery
& Innovation
Product Recommendations
Personalized Health Care
Media and Advertising
Fraud Prevention
Network Analysis
Law Enforcement
Drug Discovery
Intelligence and Crime Detection
Product & Process Innovation
360 view of customer
Compliance
Optimize Operations
Connected Data at the Center
AI & Machine
Learning
Price optimization
Product Recommendations
Resource allocation
Digital Transformation Megatrends
48. Background
• Personal shopping assistant
• Converses with buyer via text, picture and voice
to provide real-time recommendations
• Combines AI and natural language understanding
(NLU) in Neo4j Knowledge Graph
• First of many apps in eBay's AI Platform
Business Problem
• Improve personal context in online shopping
• Transform buyer-provided context into ideal
purchase recommendations over social platforms
• "Feels like talking to a friend"
Solution and Benefits
• 3 developers, 8M nodes, 20M relationships
• Needed high-performance traversals to respond
to live customer requests
• Easy to train new algorithms and grow model
• Generating revenue since launch
eBay ShopBot ONLINE RETAIL
Knowledge Graph powers Real-Time Recommendations48
EE Customer since 2016 Q3
57. Customer Graph
Product Graph
Supply Graph
Real-time product
recommendations
Real-time supply
chain management
Real-time risk mitigation
Region
Street
Customer
Address
Phone
Customer
Email
Email
Address
Phone
Product
Product
Category
Product
Category
Store
Street
Store
The Graph Behind Online Stores
58. Business Problem
• Optimize walmart.com user experience
• Connect complex buyer and product data to
gain super-fast insight into customer needs and
product trends
• RDBMS couldn’t handle complex queries
Solution and Benefits
• Replaced complex batch process real-time online
recommendations
• Built simple, real-time recommendation system
with low-latency queries
• Serve better and faster recommendations by
combining historical and session data
Background
• Founded in 1962 and based in Arkansas
• 11,000+ stores in 27 countries with
walmart.com online store
• 2M+ employees and $470 billion in annual
revenues
Walmart RETAIL
Real-Time Recommendations58
64. Neo4j — Changing the World
ICIJ used Neo4j to uncover the world’s largest
journalistic leak to date, The Panama Papers,
exposing criminals, corruption and extensive
tax evasion.
The US space agency uses Neo4j for their
“Lessons Learned” database to connect
information to improve search ability
effectiveness in space mission.
eBay uses Neo4j to enable machine
learning through knowledge graphs
powering “conversational commerce”.
Knowledge Graph for AIFraud Detection Knowledge Graph for humans
65. Neo4j - The Graph Company
720+
7/10
12/25
8/10
53K+
100+
270+
450+
Adoption
Top Retail Firms
Top Financial Firms
Top Software Vendors
Customers Partners
• Creator of the Neo4j Graph Platform
• ~200 employees
• HQ in Silicon Valley, other offices include
London, Munich, Paris and Malmö (Sweden)
• $80M in funding from Fidelity, Sunstone,
Conor, Creandum, and Greenbridge Capital
• Over 10M+ downloads
• 270+ enterprise subscription customers with
over half with >$1B in revenue
Ecosystem
Startups in program
Enterprise customers
Partners
Meet up members
Events per year
Industry’s Largest Dedicated Investment in Graphs
66. 66
• 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.
Handling Large Graph Work Loads for Enterprises
Real-time promotion
recommendations
Marriott’s Real-time
Pricing Engine
Handling Package
Routing in Real-Time
67. Modelling the Supply Chain
to Find Counterfeit Products
Connected Data analysis reveals hidden
relationships, which can aid in
identifying counterfeiter supply chains.
69. Background
• One of the world’s largest logistics carriers
• Projected to outgrow capacity of old system
• New parcel routing system
Single source of truth for entire network
B2C and B2B parcel tracking
Real-time routing: up to 7M parcels per day
Business Problem
• Needed 365x24x7 availability
• Peak loads of 3000+ parcels per second
• Complex and diverse software stack
• Need predictable performance, linear scalability
• Daily changes to logistics network: route from
any point to any point
Solution and Benefits
• Ideal domain fit: a logistics network is a graph
• Extreme availability, performance via clustering
• Greatly simplified routing queries vs. relational
• Flexible data model reflect real-world data
variance much better than relational
• Whiteboard-friendly model easy to understand
Accenture LOGISTICS IMPLEMENTATION AT DHL
69 Real-Time Routing Recommendations
70. Business Problem
• Enable delivery in London within 90 minutes
• Manage network of routes, carriers and couriers
• Calculate delivery options and times in real time
across all possible routes
• Scale to enable a variety of services, including
same-day and consumer-to-consumer shipping
Solution and Benefits
• Calculates all possible routes in real time
• Thousands of times faster than MySQL solution
• Queries require up to 100 times less code,
improving time-to-market and code quality
• Adding new functionality that was
previously impossible
Background
• eBay acquired London-based Shutl bring same-
day delivery to London to counter Amazon
Prime and to expand its global retail presence
• Founded in 2009, Shutl was the UK leader in
same-day delivery with 70% of the market
eBay Now RETAIL DELIVERY and SUPPLY CHAIN ROUTING
Real-Time Routing70
72. 72
Graph Platform: Connects to Many Roles in Enterprise
DEVELOPERS
ADMINS
Graph
Analytics
Graph
Transactions
DATA
ANALYSTS
DATA
SCIENTISTS
APPLICATIONS
Drivers & APIs
Data Integration
BIG DATA IT
Analytics
Tooling
BUSINESS USERS
Discovery & Visualization
Development &
Administration
75. Cypher: Powerful and Expressive Query Language
MATCH (:Person { name:“Dan”} ) -[:MARRIED_TO]-> (spouse)
MARRIED_TO
Dan Ann
NODE RELATIONSHIP TYPE
LABEL PROPERTY VARIABLE
76. Why Cypher is Better
Ease of use drives adoption & popularity
• Demonstrable maturity and proven success
• Huge ecosystem and support network
• Visibly represents relationships & paths
• Declarative language is easy to learn
Cypher is Open, Easy and Everywhere
Cypher on Apache
Spark (CAPS)
Cypher ToolingBI Tools
Integration
Tools Cypher on …
Additional Sources
Apache Hadoop
Accelerating Market Adoption
• openCypher participation is growing
• Reference model for ISO, other research projects
• SQL compatible and complementary
• Released for under friendly Apache license
Evolving and Expanding Rapidly
Incorporating new ideas for Cypher such as:
• Return results as graphs OR tables of data (composability)
• compose subqueries and chain-linking query algorithms
• build graph expressions
• define new graph object types like walks, runs and paths
77. Finds the optimal path
or evaluates route
availability and quality
• Single Source Short Path
• All-Nodes SSP
• Parallel paths
Evaluates how a
group is clustered or
partitioned
• Label Propagation
• Union Find
• Strongly Connected
Components
• Louvain
• Triangle-Count
Determines the
importance of distinct
nodes in the network
• PageRank
• Betweeness
• Closeness
• Degree
Data Science Algorithms