GraphConnect 2020: Full Schedule

9:00am

Opening Keynote

Speakers

Emil Eifrem

CEO, Founder, Neo4j

Emil Eifrem sketched what today is known as the property graph model on a flight to Mumbai in 2000. As the CEO and Co-Founder of Neo4j and a co-author of the O’Reilly book Graph Databases, he’s devoted his professional life to building and evangelizing graph databases.Committed to sustainable open source, Emil guides Neo4j along a balanced path between free availability and commercial reliability. He plans to save the world with graphs and own Larry’s yacht by... Read More →

Tuesday April 21, 2020 9:00am - 10:30am
Room 1

11:00am

Neo4j 4.0: A Birds-Eye View

Tuesday April 21, 2020 11:00am - 11:40am
Room 1

Neo4j Tech Talk

Subject Neo4j 4.0

11:00am

GRANDstack is not your Gran's stack: The Evolution Of Modern Web Development

A look at how modern web development has evolved from the LAMP stack to GRANDstack and how developers can take advantage of these modern tools to be extremely productive building data-intensive fullstack applcations. "GRANDstack is a full-stack framework for building applications with GraphQL, React, Apollo, and Neo4j Database. Learn why GraphQL has been quickly gaining adoption and why representing data as a graph is a win when building your API - both for API developers and consumers, and especially if you are working with graph data in the data layer, such as with a graph database like Neo4j.

In this talk we will cover some of the advantages of GraphQL over REST, as well as challenges with adopting GraphQL. We also dive into backend considerations for GraphQL and show how to leverage the power of representing your API data with graph using GraphQL and graph databases on the backend.

After this talk you will:

* Understand the basics of GraphQL

* Be able to query a GraphQL API

* Understand how a GraphQL service is built

* Be exposed to some of the tooling in the GraphQL ecosystem, including database and frontend framework integrations

How to build full-stack applications with GRANDstack

Speakers

Will Lyon

Developer Relations Engineer, Neo4j

William Lyon is a software engineer at Neo4j, the open-source graph database, where he works on building integrations and helping developers build applications with graphs.

Tuesday April 21, 2020 11:00am - 11:40am
Room 2

Practitioner Deep Dive

Audience Developer
Subject GRANd Stack

11:00am

Intro to Neo4j and Cypher

Speakers

Jennifer Reif

Developer Relations Engineer, Neo4j

Jennifer Reif is an avid developer and problem-solver. She holds a Masterâ€™s degree in Computer Management and Information Systems and has worked with large enterprises to organize and make sense of widespread data assets and leverage them for maximum business value. She has... Read More →

Tuesday April 21, 2020 11:00am - 11:40am
Room 5

Practitioner How-To

11:00am

Leveraging Knowledge Graphs for Environmental Challenges

Never has understanding the environment been more crucial. The challenge: environmental data are fragmented across many different systems and formats, or lost in files. Menome Technologies show how we use multi-agent system design and neo4j to get ALL data to create environmental knowledge graphs.

Imagine what we could know about the environment if we had ALL of the data, instead of just some of the data.

Solving environmental challenges requires combining the knowledge of experts, input from community stakeholders with historical and field data. There are significant challenges though with getting all the available data into a useable state.

Data are siloed across many different enterprise and environmental data management systems, many of which use highly specialized applications, use custom data formats, and have been developed with legacy technology.

This problem is exacerbated by the fact that the environmental sector produces much of its insight in large text based environmental reports. The data in these files traditionally consume hours of data scientists valuable time in identifying and hand extracting key data from these reports.

These historical data often must also be combined with large volumes of data derived from field monitoring programs. Monitoring data ranges from IOT style devices for monitoring things such as stream flow or downhole water quality, to streams of video or acoustic data used to identify wildlife.

Many of these data can be collected in places where connectivity is intermittent and doesnâ€™t allow for transmitting large volumes of data, or require large amounts of time from scientists reviewing footage just looking for key frames.

Menome Technologies has developed the Menome Insight platform to address the challenges associated with collecting, integrating and deriving insight from environmental data.

By using a highly containerized, micros-service multi-agent message based architecture, Menome has created a set of Knowledge Agents designed to atomize data from any source into a set of streams that are continuously refined into an environmental Knowledge Graph of all available data sources.

Mike will provide an overview of the Menome approach using examples derived from projects Menome has worked on.

Speakers

Mike Morley

Menome

Mike developed his first knowledge system in 1986 and has been developing software designed to augment the abilities of people, organizations and industries ever since. Following getting a degree in Geological Engineering, Mike has spent the past 25 focusing on disrupting to Environmental... Read More →

Tuesday April 21, 2020 11:00am - 11:40am
Room 4

Technical Case Study

Subject Neo4j Aura

11:45am

The Graph Data Science Journey: From Analytics to AI

When do you use graphs for machine learning, what domains can they be used in, and how do you get started. Real world examples and use cases to show the steps from getting started with a knowledge graph through to graph native learning. Graphs - or information about the relationships, connection, and topology of data points - are transforming machine learning. We'll walk through real world examples of how to get transform your tabular data into a graph and how to get started with graph AI.

This talk will provide an overview of how we to incorporate graph based features into traditional machine learning pipelines, create graph embeddings to better describe your graph topology, and give you a preview of approaches for graph native learning using graph neural networks. We'll talk about relevant, real world case studies in financial crime detection, recommendations, and drug discovery.

This talk is intended to introduce the concept of graph based AI to beginners, as well as help practitioners understand new techniques and applications. Key take aways: how graph data can improve machine learning, when graphs are relevant to data science applications, what graph native learning is and how to get started.

Speakers

Amy Hodler

Director, Graph Analytics & AI Programs, Neo4j

Amy is a network science devotee, AI and Graph Analytics Program Manager at Neo4j, and a co-author of the O'Reilly book, ""Graph Algorithms: Practical Examples in Apache Spark and Neo4j. She promotes the use of graph analytics to reveal structures within real-world networks and... Read More →

Tuesday April 21, 2020 11:45am - 12:25pm
Room 1

Graph Data Science

Subject Graph Data Science

11:45am

Intro to Neo4j Drivers

Tuesday April 21, 2020 11:45am - 12:25pm
Room 5

Practitioner How-To

11:45am

Big Pharma Problems. Big Graphs: Creating the Merck Manufacturing Mesh

Merck not only operates a global pharmaceutical manufacturing network, but also a global network of 400+ data systems and growing. Harking back to the origins of the world wide web, Merck has created a unified data model to link, expose, and contextualize its global network of manufacturing data. The year: 1989. A young(er) Tim Berners-Lee, working at the research-focused CERN, submits a modest proposal to solve to organization's growing knowledge and information management problem. His memo, titled ""Information Management: A Proposal"" focuses on creating a non-hierarchical web to connect the CERN's heterogeneous systems. With management's only comment ""Vague but exciting..."" scrawled across the top of the page, this would serve as the basis for the creation of the World Wide Web.

Fast-forward 30 years. A group of engineers and scientist, working at the research-focused pharmaceutical company Merck, grows tired of typing the phrase ""LEFT OUTER JOIN"" in order to link and contextualize the company's many data sources. Consequently, they set out to create a unified manufacturing information model. With attempts at relational data models failing, the team turns graph databases to better capture the complex nature of their heterogeneous, inconsistent schemas-based, and non-hierarchical data systems. With nothing written across any memos (because it's 2019 and we use email now), their efforts serve as the origins of the Merck Manufacturing Mesh. This mesh brings together a wide array of domains including MES, LIMS, and ERP data to create a more intuitive data model free of the need for primary keys. Leveraging the efficiency of index free adjacency, the Mesh will help engineers and scientist quickly answer questions regarding batch genealogy, material impact assessments, end-to-end product lead times, and much more."

Speakers

Marcus Adams

Associate Director, Digital Proactive Process Analytics, Merck & Co.

Marcus Adams earned his BEng and MS in Chemical Engineering from the University of Delaware and Villanova University, respectively. His more than decade of experience at Merck spans the bio-pharmaceutical spectrum and includes experience in pre-clinical PK/PD modeling, product commercialization... Read More →

Tuesday April 21, 2020 11:45am - 12:25pm
Room 2

Technical Case Study

11:45am

From Impossible to Done: Transforming WestJet's Flight Schedule Data Publishing

We replaced a year-long manual (pen, paper, spreadsheet and email) process with three hours of compute in our flight schedule graph model. The result is an incomparable increase in efficiency, accuracy, and work capacity. The Neo4j-based solution changed the game entirely.

At WestJet Airlines, we prioritize Guest experience. On our website, the first step of our booking process is optimized by representing only reachable destinations from the selected origin. Without such an optimization, our guests would have only a one in four chance of choosing an origin-destination combination that is purchasable.

Identifying routable pairs of origins and destinations was simple to do when our flight network was small. But with a growing network of bases, seasonal destinations, additional code share partners, and ever-shifting commercial flight schedules, maintaining this data becomes a difficult problem. Historically, dedicated employees identified obvious connection changes in a tedious process involving pen and paper. Connection changes were communicated through a change-set spreadsheet via email to be applied to master XML files.

In this talk we'll describe how we used Neo4j to model our flight network from the commercial schedule and requisite minimum connection time rules. The result is a weekly process that ingests a stream of SSIM format schedule data to build a graph model from which we harvest our required data artifacts.

Speakers

Dave Pirie

WestJet

Mark Miller

Software Engineer, Westjet

Tuesday April 21, 2020 11:45am - 12:25pm
Room 3

Technical Case Study

11:45am

How we're using GRANDstack for Data Utopia in MEP design

The construction industry is in the midst of radial change. A significant driving force is in developing sustainable designs which meet the needs of the building occupants.

This talk will showcase how graph data can provide rich insights and a common source of data throughout the design process."

Speakers

WIll Reynolds

Software Engineer, Hoare Lea

I've been developing software for the construction industry for around 15 years. Starting from tools and utilities for AutoCAD and Autodesk Revit, and now a firm graph data enthusiast set on revolutionising the buildings services industry with the awesome power the graph!

Tuesday April 21, 2020 11:45am - 12:25pm
Room 4

Technical Case Study

Subject GRANd Stack

1:30pm

Anomalies, inconsistencies and fraudulent behaviour: Data mining with Neo4j and GRANDstack

The Neo4j ecosystem made graph data mainstream. Developers and business users alike can now easily structure their data in a way that matches the real-world.

This real-world view is especially useful for detecting fraud: who is involved, where and when does it take place, what assets do they hold, and - most importantly - how are each of these details connected.

Wikipedia’s community-based approach to editing makes it prone to many types of fraud, including artificial bot activities, brigading, digital vandalism and collusive behaviors. By examining controversial and topical Wikipedia articles together with their rich edit history, Christian demonstrates how using GRANDstack tooling the smart way can enable the detection of suspect patterns of behavior.

GRANDstack gives developers the premium tools they need to build consistent, extensible applications for trend and pattern analysis. Christian guides you through the steps needed to implement an end-to-end anomaly detection platform, explaining why each technology in the modern GRANDstack approach is the right choice. He’ll include:

streaming techniques for ingesting large volume data
core graph modeling in Neo4j
GraphQL querying to reveal complex interaction behaviors
visualizing and actioning suspicious networks in a React front-end

Christian has extensive Neo4j Graph platform experience. He’s worked with Fortune 500 companies building graph analytics applications and bringing them to production successfully.

Speakers

Christian Miles

Technical Lead, Cambridge Intelligence

Since completing his Masters in Maths & Computer Science at Bristol University in the UK, Christian has specialized in graph analytics software for global enterprise deployments. In his roles at BAE Systems and the Wynyard Group, Christian’s focus has been applying graph network... Read More →

Tuesday April 21, 2020 1:30pm - 1:45pm
Room 6

Practitioner Deep Dive, Lightning Talk

Subject GRANd Stack

1:30pm

Identity Graph at Scale - Transforming Billions of Page views to Unique Identity Profiles in Publishing

Leveraging Neo4j, Meredith has been able to create an Enterprise wide Identity Graph with 12.3 Billion Nodes and 18.9 Billion Relationships that represents its Digital presence over the last 18 Months across 30+ brands. Neo4js Graph Algorithms enable unique Digital Profiles for Premium Ad Sales. Digital Marketing and Custom Audience Advertising is a focus of any Media Publisher. Meredith Corporation is leading the field of first party data retargeting using Neo4j for an in-house Identity Graph containing every digital cookie seen across 30+ brands and applications, including People, Entertainment Weekly,Real Simple, Eating Well, and All Recipes. With 100 million unduplicated consumers, the Meredith Database is the largest U.S. consumer database of any media company, and includes 7 in 10 women and 8 in 10 homeowners. The Identity Graph solution has leveraged 10's of Billions page views across multiple data streams to create a graph comprising of 12.2 Billion Cookies and 18.9 Billion Relationships between them to identify recurring individuals across domains and devices to improve Custom Audience Advertising and Profiling.

Speakers

Ben Squire

Data Scientist, Meredith Corporation

Benjamin Squire is a Senior Data Scientist at Meredith Corporation. He has developed several successful POC's in Machine Learning, Custom Audience Advertising, and Identity + Profiling. His interest is in using new technologies and automation to bring the best content to the right... Read More →

Tuesday April 21, 2020 1:30pm - 2:10pm
Room 1

Graph Data Science

Subject Graph Data Science

1:30pm

Neo4j 4.0: Horizontal Scaling with Sharding & Federation

Tuesday April 21, 2020 1:30pm - 2:10pm
Room 3

Neo4j Tech Talk

Subject Neo4j 4.0

1:30pm

Databases on Kubernetes Using a Custom Operator: Day 1, Day 2, and Beyond

We started the journey of building a managed cloud version of the graph database Neo4j. A bit later we started developing an operator to manage multiple database clusters in k8s.

Handling persistence and Neo4j's own distributed consensus algorithm within k8s gave us a challenge. In this session we want to share the lessons we learned writing this operator and using it in production.

We will start with how to get started using the k8s controller tooling to create an operator to manage a CRD. We go beyond the ""day 1"" tasks of creating and deleting databases and discuss how we meet ""day 2"" concerns such as:

- Unit testing our operator using k8s fakes.

- Continuously deploying an operator into a GKE cluster.

- Automatic rolling updates of Neo4j databases with zero downtime and fault tolerance.

- Database administration (backup, restore, password resets etc.) via an operator.

Speakers

Jacob Davis-Hansson

Engineer, Cloud, Neo4j

I do engineering at Neo. Over the years I've had my hands on most parts of the code base; everywhere from kernel code, procedures, cypher and Bolt to HA Paxos code and RAFT. Right now I write Kubernetes operators for Neo4j Cloud.

Tuesday April 21, 2020 1:30pm - 2:10pm
Room 4

Practitioner Deep Dive

1:30pm

Tune Up Your Cypher: Tips and Tricks for Optimizing Query Performance

Tuesday April 21, 2020 1:30pm - 2:10pm
Room 2

Practitioner Deep Dive

1:30pm

Intro to Data Import

Tuesday April 21, 2020 1:30pm - 2:10pm
Room 5

Practitioner How-To

1:50pm

Simplify Your Backend! From RestAPI to GRANDstack: The Case of TypeScript and Flutter

You’ve heard of all the great things about GRANDstack? Excellent! Convinced you want to try it but you already have a tech stack?
We have a story of our own migration from REST API to GraphQL with GRANDstack.
Twindly is a mobile app for organizing your beauty products and giving automated recommendations for sustainable beauty products. Our API server was implemented with Nest.js, a Node.js framework with TypeScript support. Nest.js supports GraphQL with a wrapped Apollo server underneath thus allowing us to switch from REST API to GraphQL by using neo4j-graphql.js. We use Neo4J Aura as our main database.
We will show how we ended up with less code on our backend and how our mobile app, developed with Flutter, can connect to it.

Speakers

I. Abiyasa Suhardi

Co-Founder, Twindly

I. Abiyasa Suhardi is a full-stack software engineer who loves to experiment and play around with various tech stacks. Currently an expert in JavaScript and web technologies (amongst other things), working at eBay as a Frontend engineer focusing on NodeJS, UI components development... Read More →

Astrid Mochtarram

CEO, Twindly

Astrid Mochtarram is a geek, an engineer, a designer, a writer and an entrepreneur.A little crazy and working on too many projects at times, she is most passionately working for her tech startup twindly, which focuses on beauty and sustainability.She is Indonesian and currently calling... Read More →

Tuesday April 21, 2020 1:50pm - 2:05pm
Room 6

Practitioner Deep Dive, Lightning Talk

Subject GRANd Stack

2:15pm

Cypher is Everywhere: Even up in your GraphQL - with the help of the GRANDstack

Have you ever wondered if there was an easy way to make your Neo4j data accessible for web applications? With the GRANDstack not only are you completely harnessing the power of Neo4j and GraphQL, you’re able to make the most of the data with custom fields and types with the @Cypher directive and the being able to create custom queries and mutations using both Cypher and APOC procedures. I’ll show how using the full capabilities of Cypher can make your application and data clean and accessible.

Speakers

Michael Porter

Founder, Muddy Boots Code

I’m a former Marine, world traveler and self taught coder. One day while working in the oil fields of West Texas I picked up “How To Automate the Boring Things With Python,” did exactly that and have never stopped coding since. I started my own business, live and work remotely... Read More →

Tuesday April 21, 2020 2:15pm - 2:30pm
Room 6

Practitioner Deep Dive, Lightning Talk

Subject GRANd Stack

2:15pm

New: Neo4j's Graph Data Science Library

Speakers

Alicia Frame

Senior Data Scientist, Neo4j

Alicia Frame is the lead data scientist at Neo4j. She works as part of the product management team to determine the product roadmap for Neo4j's graph algorithms library, and the strategy to grow Neo4j into a dominant analytics platform. In her role, she works closely with early adopters... Read More →

Tuesday April 21, 2020 2:15pm - 2:55pm
Room 2

Graph Data Science

Subject Graph Data Science

2:15pm

Neo4j 4.0: Building Granular Security into your Graph

Tuesday April 21, 2020 2:15pm - 2:55pm
Room 3

Neo4j Tech Talk

Subject Neo4j 4.0

2:15pm

Contrasting Neo4j vs RDBMS, a Performance Deep Dive

If you came to Neo4j from the RDBMS world, you've likely noticed that conventions around queries is dramatically different between them. Not just in syntax, but in structure. For example, a SQL query with 7 JOINs in it will likely terrify a DBA, but connecting that many different entity types is commonplace in Cypher.During this talk, we'll explore how Neo4j and PostgreSQL store data on disk and send it over the wire, and use that insight to analyze the performance profiles of both databases in various query scenarios.
This presentation contains pretty advanced topics, as we'll be examining logistics of data at rest and in motion, but I will endeavor to present these topics in a way that will be easier to understand for beginner-level data engineers and intermediate-level solution engineers.The RDBMS implementation we will be discussing is PostgreSQL. It's not THE most popular RDBMS but it's the one I have the most intimate knowledge of, it's common enough that the audience is likely to have worked with it, and other implementations such as MySQL are close enough in their performance profiles (since SQL is prescriptive enough to coerce implementations into similar structures) that most RDBMS details mentioned in the talk are usable as at least a foundation for evaluating performance.

Speakers

Jamie Gaskins

Jamie Gaskins is a software architect at Snapdocs, Inc. where he uses his distributed-systems expertise to bring the real estate industry into the 21st century. Jamie is a Neo4j Certified Professional and the creator and maintainer of the Crystal language Neo4j driver.

Tuesday April 21, 2020 2:15pm - 2:55pm
Room 4

Practitioner Deep Dive

2:15pm

Level up Neo4j with APOC and Advanced Cypher

Speakers

Michael Hunger

Head of Developer Relations, Neo4j

Michael Hunger has been passionate about software development for a very long time. For the last few years he has been working on the open source Neo4j graph database filling many roles.As caretaker of the Neo4j community and ecosystem he especially loves to work with graph-related projects, users... Read More →

Tuesday April 21, 2020 2:15pm - 2:55pm
Room 5

Practitioner How-To

2:15pm

Using a Knowledge Graph to support Fluid Experiences & Deliveries

Our ultimate goal at Royal Caribbean International is to make your time on vacation awesome. Connecting cruise data into a powerful new Knowledge Graph provides opportunities to transform the guest and crew experience to the next level.

Let's embark on a voyage to enhance guest and crew experiences through the use of a Knowledge Graph. This sailing includes many disparate data sources, a marine infrastructure and a deep blue sea of graph data modeling principles. On this journey we also navigate the deployment of a knowledge graph powered service layer and ultimately integration with the mobile app in the hands of our Royal Caribbean guests. Now let's dive into the Knowledge Graph and how it all came together.

Tuesday April 21, 2020 2:15pm - 2:55pm
Room 1

Technical Case Study

2:35pm

Using Graph Technology to Map Fandom

For years fan analytics have been focused on metrics over time - Streams today, new followers this month, likes on a post… But not every fan who follows or likes a post is created equal. Using graph technology we are helping artists identify, communicate with, and ultimately monetize their superfans. Graph technology has been instrumental for us in measuring the relationship between artist and fan. Did the fan use the song in their YouTube video that has 100K views? Did the fan post from the show and get 5 friends to come? Did they tweet the artist’s Spotify link and get 100 retweets? These are all metrics that we are able to sift through using graph algorithms to determine who are those top 20% of fans that are driving the majority of an artist’s revenue.

Speakers

Sajan Sanghvi

CTO, Laylo

Saj currently serves as the CTO of Laylo, a platform aimed to be Salesforce for Artists. The platform allows artists to identify, communicate with, and ultimately monetize their superfans. Saj also serves as a band member of 'No Suits', totalling over 10M streams online, and represented... Read More →

Tuesday April 21, 2020 2:35pm - 2:50pm
Room 6

Technical Case Study, Lightning Talk

3:30pm

The Graph Database Workshop That Changed An Industry

In 2019, a group of eager engineers gathered in a room to learn about Neo4j, Graphql and Apollo. Little did they know, this workshop not only changed their lives, but shook an entire industry. This is the true story of how graph connected people around the world and catalyzed real change.

Speakers

Polley Wong

CEO, VIUSPACE

Polley Wong is a serial entrepreneur. She’s the CEO of design technology and research company VIUSPACE (pronounces as View-space) and co-founded the Interior and Architecture design firm We Create Group with Interior Designer Briana Earl. She’s a strong advocate for gender equality... Read More →

Tuesday April 21, 2020 3:30pm - 3:45pm
Room 6

Technical Case Study, Lightning Talk

3:30pm

Empowering the Business with Graph Analytics

Lockheed Martin Aeronautics has integrated graph technology into its technology landscape empowering end users to build and visually explore their data models more effectively than traditional methods. During our graph implementation journey, we developed a self-service operating and support model to enable our users. Equipped with Kettle, Neo4j and Linkurious, business users have been able to develop their own graph models to answer business questions, leveraging developer-consultants for complex solutions. This has proven to be successful in creating a graph community within Lockheed Martin Aeronautics.

Speakers

Robert Tung

Staff Software Engineer, Lockheed Martin Aeronautics

Shawn Akberali

Senior Software Engineer, Lockheed Martin Aeronautics

Caroline Nelson

Senior Data Analyst, Lockheed Martin Aeronautics

Tuesday April 21, 2020 3:30pm - 4:10pm
Room 1

Graph Data Science

3:30pm

Neo4j 4.0: Leveraging Multi-Database

Tuesday April 21, 2020 3:30pm - 4:10pm
Room 3

Neo4j Tech Talk

Subject Neo4j 4.0

3:30pm

Intro to Algos

Speakers

Mark Needham

Developer Relations Engineer, Neo4j

Mark Needham is a graph advocate and developer relations engineer at Neo4j.As a developer relations engineer, Mark helps users embrace graph data and Neo4j, building sophisticated solutions to challenging data problems. Mark previously worked in engineering on the clustering team... Read More →

Tuesday April 21, 2020 3:30pm - 4:10pm
Room 5

Practitioner How-To

3:30pm

99.9999% (seriously, that many 9's) uptime at Adobe: How we got there with Neo4j

Did you ever think you can setup your casual cluster to be self-healing and auto recoverable in the cloud? Would you like to know that your backups will restore without error and that your data is consistent every day? Come to our talk to learn more about running a stress free causal Neo4j cluster.

 We will discuss:

Restore testing / data consistency check
- Confirming the backup was a success by executing a successful restore
- Executing a consistency check on the restore
Automated backups
- Installed and scheduled to run on each node
- Uses etcd cluster is as a locking mechanism to ensure only running on one node at a time in the cluster
Autoscaling groups
- Set to always have at least one server running
- Extra ASG configured to help facilitate rolling upgrades
CoreOS
- Cloud Ignition
  - systemd units
  - Used to get secure keys and environment variables from S3 bucket.
  - Setup scripts
Docker implementation
- Use AWS ECR to store custom Neo4j docker image
Ansible
- Config management tool we used to configure all infrastructure.
ELB endpoints
- Uses native Neo4j calls to properly forward requests to a Leader or Follower
ENI for persistent IP
- Known private IPs for the cluster allows the use of a pre-ordained config file.
How to select the right instance types
- Hardware considerations
- Memory is at least 2x database size - allows for growth - as well as some more for the OS.
Gotchas
- RAFT leader election issue with ephemeral ports
  - Add the correct port range to Security Group to allow for RAFT protocol
- ENI CoreOS routing
  - Routing table rules needed for the configured private IPs
- Unique constraints
  - What happens when you don’t add a unique constraint when adding a new Node Type that has an Id

Speakers

Manuel Toledo

Software Engineer, Adobe

Gabe Tucker

Software Engineer, Adobe

I have been working with data technologies in operations, administration and engineering for over 15 years in multiple technologies and industries. I consistently advocates of the importance of accuracy and integrity of data.

Tuesday April 21, 2020 3:30pm - 4:10pm
Room 2

Technical Case Study

3:30pm

From Zero to Knowledge Graph at BCBS

It started as a straightforward request:

"How can we view a timeline of each interaction we have with our members?"

Along the way, this straightforward ask grew into the â€œMember Intervention Hub: an analytics engine to enable our business teams to identify key members and intervention opportunities to improve health and reduce costs.

In between, our small team had a crash course in Neo4j, CypherQL, and the right and wrong ways to build a knowledge graph for health insurance.

This talk covers some of the key things we learned while building our Member Intervention Hub:

1. Getting business / executive buy-in on a graph solution

2. Iterating on a data model

3. How far you can get with Community Edition

Speakers

James Colvin

BCBS

It started as a straightforward request:“How can we view a timeline of each interaction we have with our members?”Along the way, this straightforward ask grew into the “Member Intervention Hub”: an analytics engine to enable our business teams to identify key members and intervention... Read More →

Tuesday April 21, 2020 3:30pm - 4:10pm
Room 4

Technical Case Study

3:50pm

Writing Neo4j procedures in Kotlin

At the Port of Rotterdam we love to use cool technologies, like Neo4J and Kotlin. When we started to hit the limits of (our understanding of) Cypher we decided to run our algorithm directly on Neo4j using a user-defined procedure. But while all our software is written in Kotlin, the documentation only mentioned Java and on Google we only found some small examples. Our algorithm had to run in production within 2 months.
Within 15 minutes we’ll share our experiences, tips & tricks on writing performing and stable user-defined procedures in Kotlin.

Speakers

Riccardo Lippolis

Software Engineer, Port of Rotterdam

An inquiring and experienced Java/Kotlin Software Engineer with a passion for solving complex problems. He works for JDriven (currently at the Port of Rotterdam), where he shares his passion and drive with other enthusiasts. He has spoken at several international conferences, including... Read More →

Jorrit van der Ven

Port of Rotterdam

Jorrit is a Kotlin/Java developer working at JDriven in the Netherlands. He loves to learn about new technologies and to share his knowledge with others. In his spare time he likes to make his house a bit smarter using wires, chips and a soldering iron. He has spoken at several international... Read More →

Tuesday April 21, 2020 3:50pm - 4:05pm
Room 6

Practitioner Deep Dive, Lightning Talk

4:15pm

Open Source Knowledge Graph of News

Journalism is in crisis. Newspaper revenues have been falling while fake news is on the rise. To alleviate these problems, we propose a knowledge graph of entities and relationships from publicly available news sources, using an open dataset of over 23,000 Vox articles as a proof-of-concept. We believe that this work will significantly enable both journalists and consumers of news to keep track of large volumes of information in a non-siloed manner. News organizations will have access to a comprehensive content catalog that allows for collaboration between and within other organizations, as well as producing investigative journalism pieces at a lower cost. The public will benefit from having a platform that is widely available, easily searchable, comprehensive over time and space, and curated by trustworthy sources. Because we also believe that our initiative is of great benefit to the public, we are both open sourcing our efforts and actively encouraging others to collaborate with us and each other to bring this project forward.

Speakers

Hanhan J. Li

I’m Hanhan, the co-founder of PressDB, an exploratory writing engine that empowers people to research and write better and faster. I graduated with a dual masters’ degree in Journalism and Statistics from Columbia University in 2018.I have been fascinated about the graph world... Read More →

Chris Rusnak

Chris Rusnak has 8 years of work experience as a data scientist in information services and consulting industries. He has obtained a M.S. degree in Data Science from the Data Science Institute at Columbia University in 2017 and a B.A. degree in Math and Biological Sciences as sum... Read More →

Tuesday April 21, 2020 4:15pm - 4:30pm
Room 6

Technical Case Study, Lightning Talk

4:15pm

Graph Analytics in Healthcare Healthfirst's Journey from Idea to Production

At Healthfirst, we're on a journey to use graph to improve the health of our members. We began with a small pilot and scaled up to a KG, allowing us to analyze connections between members, claims, and providers: weâ€™re using Neo4j for fraud detection, reducing costs, and improving care patterns.

We'll start the talk with an overview of Healthfirst and our business model. We'll discuss the business problems outlined to us by stakeholders, and how we helped them frame their questions in a way that could be answered with graph analytics.

We'll review our very first use case (fraud detection), which started with a Neo4j trial off the side of our desks, the results of which were powerful and gained us the support to scale up to a Neo4j server and several licenses, expand the fraud detection use case, and launch a new use case for out of network utilization analysis.

We will focus the talk how we collaborated w/ business partners to execute the use cases and how the results are being used to improve quality of care for our members, but will also touch on the graph schema, data sources/ ingestion patterns, and graph algorithms used for the analyses."

Tuesday April 21, 2020 4:15pm - 4:55pm
Room 3

Graph Data Science

4:15pm

Neo4j Innovation Labs

Speakers

Stefan Wendin

Global Head of Business Design & Strategic Programs, Neo4j

Tuesday April 21, 2020 4:15pm - 4:55pm
Room 2

Neo4j Tech Talk

4:15pm

The Practical GRANd Stack

The GRAND stack is a new and exciting stack available to users of Neo4j; consisting of GraphQL, React, Apollo, and the Neo4j Database. Along with these technologies, there is an evolving ecosystem of tools and libraries at your disposal that can transform your workflow and truly get you in the "Fullstack Graph" mindset. One of these libraries in particular neo4j-graphql.js allows you to write a traditional GraphQL schema and have Cypher based query and mutation resolvers generated for you, making GraphQL server development a breeze. It also allows you to write custom Cypher inline in your schema definition to make incredibly powerful computed fields. Using these tools, in this talk, we'll get a brand new full-stack application from `mkdir new-app` to deployable in 45 minutes. Including user authentication, custom resolvers, and a single graph used in our Neo4j instance on down to our React app.

Speakers

Erik Rahm

Polyglot web developer., Amoeba

Tuesday April 21, 2020 4:15pm - 4:55pm
Room 5

Practitioner How-To

Subject GRANd Stack

4:15pm

Graph-based AIOPs at eBay

In the 25-year-journey of eBay developing and managing large scale software, data and system architecture. It has always been critical to ensuring quality, reliability, and security among a host of other key expected fundamentals of the business products.

Our AI OPs roadmap aims to address the following key challenges:

* "Blindness": limited observability on architectural knowledge or issues

* "Ignorance": Lack of measurability for service architecture, or technical debts

* "Primitiveness": Missing diagnostic, engineering and run-time automation

Graph techniques and algorithms are a critical part of our roadmap - build and evolve sustainable eBay service architecture by providing automated architectural visibility, assessment, and governance of our service ecosystem.

In this talk, we will be sharing our blueprints, thoughts, and existing progress (e.g., [Realtime Graph-based Root Cause Analysis for Cloud-Native Distributed Data Platform](http://www.vldb.org/pvldb/vol12/p1942-wang.pdf), graph-based dependency systems). Our goal here is to share the motivation, concept, design, and values of modeling complicated and evolving infrastructure with key knowledge (which generated from various distributed sources, e.g. ML models) as a graph.

Speakers

Hanzhang Wang

Applied Researcher, eBay

Hanzhang is an applied researcher at eBay. After earning his Ph.D. from the University of Michigan, he is leading eBay's intelligent infrastructure research - to build and evolve sustainable eBay microservice architecture by providing best of breed automated architectural visibility... Read More →

Tuesday April 21, 2020 4:15pm - 4:55pm
Room 1

Technical Case Study

9:30am

Opening Keynote

Wednesday April 22, 2020 9:30am - 10:30am
Room 1

11:00am

Building a People Graph with R

The McKinsey People Analytics team has combined R and Neo4j to build a People Graph to helps us leverage the networks of people, skills, and expertise in our Firm. To this end, we’ve built an R package that allows efficient querying and server management for Neo4J using bolt, R, and cypher-shell.

We will present a quick overview of both the theoretical and technical sides of our use case. Theory: by combining various HR data sources in a graph db, we are able to analyze communities of people, identify pockets of expertise, and understand social networks in the Firm. This has already made impact in our talent management strategy. Technical: We’ve developed an R package (neo4jshell) that allows us to integrate Neo4j into our broader existing workstreams, such as Oracle dbs and R. We’d like to encourage other R developers to check out this package for their own use cases.

Speakers

Rachel Ramsay

McKinsey, Senior Data Scientist

Wednesday April 22, 2020 11:00am - 11:15am
Room 6

Technical Case Study, Lightning Talk

11:00am

Improving Patient Outcomes with Graph Algorithms

AstraZeneca recognized that no patient journey through the US healthcare system is the same but they wanted to find places where they could improve the outcomes for patients. The story started with an interest in disrupting the traditional approach of qualitative market research and ended with building a path to leverage graph algorithms to help the right patients at the right time to improve their outcomes.

Wednesday April 22, 2020 11:00am - 11:40am
Room 1

Graph Data Science

11:00am

Neo4j worst practices, welcome to the dark side RELOADED

5 years ago I gave a talk "Neo4j worst practices - welcome to the dark side" at GraphConnect 2015. This "RELOADED" sequel touches common gotchas a graphista will discover sooner or later during her journey throught the graph space. "Graphdatabases and esp Neo4j are a super exciting technology enabling us to solve certain problem orders of magnitudes faster and cheaper compared to other persistence technologies. As with every new technology there is a learning phase in the beginning. Typically the learning curve with Neo4j is rather steep, productive code can be written after just a couple of days. Nevertheless you will fall in one or the other trap sooner or later. May it be data modelling, testing, configuration or tuning - all of them keep some surprises for the graph database rookie.

This talk will show ways to make sure to fall in all of these traps. The presentation of anti patterns is supposed to be informative but also humorous - maybe sometimes slightly sarcastic. This session will be rounded up by some andecdotes for customer projects and the open source community of Neo4j which I'm in close touch with for the last 8 years as field engineer with Neo Technology.

See https://neo4j.com/blog/dark-side-neo4j-worst-practices/ for a write up and recording of the original episode of this talk."

Speakers

Stefan Armbruster

Field Engineer, Neo4j

Stefan has worked for 8+ years for Neo4j as field engineer and helps customers to bring and keep their graph db project on track. After finishing a diploma in physics he has spent ~15 years as a freelance consultant. Aside from coding in the java ecosystem he is a passionate Linux... Read More →

Wednesday April 22, 2020 11:00am - 11:40am
Room 2

Practitioner Deep Dive

11:00am

Neo4j in the Cloud

Speakers

David Allen

Partner Solution Architect, Neo4j

M. David Allen is a technologist who loves to learn and to figure out how to do things that haven't been done before. At Neo4j, he is a Partner Solution architect working with Neo4j’s strategic partners, in particular cloud computing platforms and Hadoop/Spark partners. Prior to... Read More →

Wednesday April 22, 2020 11:00am - 11:40am
Room 5

Practitioner How-To

11:20am

Enhancing conversational AI with a contextual graph model

A contextual graph model feeds AI suggestions to refine a retail catalog display in a person to person conversation. The graph indexes a conversational sequence on genres, related sub-genres and attributes to yield suggestions that are meaningful and relevant through the conversation flow.

Speakers

Kofi Dadzie

Co-Founder, Executive Vice-Chair, Rancard

Kofi is Co-Founder and Executive Vice Chair of Rancard. He has led the company in its evolution to conversational discovery with AI & social recommendations, following success in mobile content distribution technology with developers and brands including Google, BBC, VOA, MTV, ESPN... Read More →

Wednesday April 22, 2020 11:20am - 11:35am
Room 6

Technical Case Study, Lightning Talk

11:45am

It Depends (and why it's the most frequent answer to modelling questions)

The answer to most general purpose graph modelling questions is “it depends”. This talk demonstrates the pitfalls of modelling without knowing use cases- it shows how two sets of people can produce two different models for the same set of data elements, and how use cases should guide the model.

Speakers

Luanne Misquitta

VP Engineering, GraphAware

Luanne Misquitta is VP of Engineering at GraphAware, and has been working with Neo4j for 10+ years. She was a core committer to Neo4j OGM and SDN 4, has spoken at GraphConnect in both Europe and the US.

Wednesday April 22, 2020 11:45am - 12:00pm
Room 6

Practitioner Deep Dive, Lightning Talk

11:45am

Graph data science in practice: building models with Neo4j and Python

Wednesday April 22, 2020 11:45am - 12:25pm
Room 4

Graph Data Science

11:45am

Graph Visulazation: Which Tool is Right for You?

Speakers

Anurag Tandon

Director Product Management, End User Applications, Neo4j

Anurag’s mission is to help Neo4j customers become successful with our portfolio of end-user products.Prior to Neo4j, Anurag spent almost two decades in big data analytics and business intelligence, while in product and customer-facing roles at Zoomdata and MicroStrategy. He is keenly p... Read More →

Wednesday April 22, 2020 11:45am - 12:25pm
Room 2

Neo4j Tech Talk

11:45am

Neo4j Aura: Accelerating Development with Neo4j-Database-as-a-Service

Wednesday April 22, 2020 11:45am - 12:25pm
Room 3

Neo4j Tech Talk

Subject Neo4j Aura

11:45am

Bruce Wayne to Batman: Migrating a Non-Reactive Spring App to Reactive

Adapting an application may feel more like fighting a villain and less like a costume change. In this session, see the process live to migrate a Spring Data Neo4j application to reactive with the new Spring Data Neo4j Reactive capabilities. See how the migration works and avoid the pitfalls! "Taking a working application to the next level by adopting a newer process may often feel more like fighting a villain and less like a quick costume change. Many developers have to migrate applications on their own with little documentation or assistance through the process.

In this session, see the process live as the presenter walks through migrating a Spring Data Neo4j application to a reactive application with the new Spring Data Neo4j Reactive capabilities. We will see how the migration works, as well as understand pitfalls and how to navigate them along the way.

Come to this session to go from a billionaire application to a superhero application through the conversion!

Speakers

Jennifer Reif

Developer Relations Engineer, Neo4j

Wednesday April 22, 2020 11:45am - 12:25pm
Room 1

Practitioner Deep Dive

Audience Developer

12:05pm

RDBMS to Neo4j: Tips & Tricks

Excited about Neo4j? The first step is migrating your data from an RDBMS like Postgres 🐘 or MySQL 🐬 to Neo4j. This talk covers tips and tricks surrounding performance optimizations, indexes, data transformations, and other first principles when migrating your data.

Speakers

Mike Blum

Software Engineer, Logicgate

Software engineer at LogicGate in Chicago tasked with our Neo4j infrastructure. We use graphs to build a platform for customers creating custom compliance and audit workflows.

Wednesday April 22, 2020 12:05pm - 12:20pm
Room 6

Practitioner Deep Dive, Lightning Talk

1:30pm

Interval Trees for Genomic Feature Retrieval in Neo4j

When you grow corn, yield is paramount. How much a corn seed will yield is encoded in its genome. If you visualized that genome in ASCII characters, you would see a seemingly random string of 25 million A, C, G, and T characters. Somewhere in that string you could find the interesting portions that governed how much corn that seed will yield, as well as others that provide useful properties to the corn plant making it resilient to different pressures.

At Bayer Crop Science, we track these interesting portions of the Corn genome as numeric intervals: start and stop indices within the string of 25 million characters. Our goal is to find relationships between intervals of interest to determine how to breed plants to produce the greatest yield and be resilient for different conditions around the world.

Our team, the developers of an internal genomics software stack within Bayer Crop Science, have been challenged to provide an API to our internal customers for efficiently finding these related intervals. In exploring solutions we came upon the interval tree data structure and implemented a means for storing and querying interval trees in Neo4j.

In this session we will discuss and demonstrate our approach as well as a set of Neo4j stored procedures created by Bayer Crop Science to effectively manage, retrieve and search interval tree data structures in large scale.

Speakers

Jason Clark

Lead Data Engineer, Bayer

Jason Clark is a Lead Data Engineer in Bayer's Crop Science business unit with a depth of experience in delivering fit-for-purpose data and software solutions for genetics and genomics datasets using software design principles. As a founding member of Crop Science's Product360 data... Read More →

Wednesday April 22, 2020 1:30pm - 2:10pm
Room 4

Graph Data Science

1:30pm

Building Geospatial Algorithms and Apps using Neo4j

Graphs and Geospatial are natural partners, but Neo4j has native support for only the simplest spatial type, the Point. Is this a problem? What if you want to write apps that perform more complex spatial searches using spatial algorithms on polygons and multi-polygons? This talk will show you how. Graphs and Geospatial are natural partners. And yet, Neo4j has native support for only the simplest spatial type, the Point. Is this a problem? What if you want to perform more complex spatial searches, spatial modelling or spatial algorithms using complex types like polygons and multi-polygons? At graphconnect 2018 we showed you how to write a web app that demonstrated route finding using A-Star and spatial search using a point-in-polygon algorithm. This talk will take that further, showing you how to write your own spatial algorithms for more complex analyses and how to integrate them into a web-app through user-defined functions accessible with Cypher queries. To demonstrate this we will use a new library we've been working on to prototype complex spatial algorithms and complex spatial datatypes within Neo4j.

Speakers

Craig Taverner

Team Lead, Cypher, Neo4j

Craig is the team lead for Neo4j Cypher and product lead for Spatial. He has been using Neo4j since 2009, first as a customer building mobile telecommunications analysis tools, and as a community member creating the 'Neo4j Spatial' GIS modelling library. Then in 2014, he joined the... Read More →

Wednesday April 22, 2020 1:30pm - 2:10pm
Room 1

Practitioner Deep Dive

1:30pm

Applying Graphs for a Global Media Inventory at Discovery Networks

Have you ever wondered how a giant media enterprise manages a vast library of content? Here's a hint: It's involves a large graph. Come listen and understand how a graph can be leveraged to track the complex relationships across millions of pieces of video and related intellectual property items.

Speakers

Brant Boehmann

Senior Software Engineer and Technical Lead, Discovery Network

Brant Boehmann is a Java/JVM and Graph enthusiast. Brant is the co-founder and co-organizer of KnoxJava, a Java Users' Group in Knoxville, TN. Brant currently serves as a Senior Software Engineer and Technical Lead at Discovery where he designs and implements solutions for the ingestion... Read More →

Wednesday April 22, 2020 1:30pm - 2:10pm
Room 2

Technical Case Study

1:30pm

Bank of Montreal: Predictive Risk Graph for Financial Institutions

Commercial banks hand out loans based on risk assessment at a point in time. When a company's risk degrades, banks must act quickly to limit their exposure.
We built a graph that combines external news, and internal data to alert analysts when a customer's rating might degrade bc of negative news.

With thousands of commercial banking clients, it is difficult for risk analysts to know what the bank's exposure is to certain customers. Often they are unaware that an entity is a customer or has a commercial relationship with one.

The graph we built combines relational internal bank data to show how a loan is structured, unstructured documentation to identify connected entities, and external media (twitter, reuters, etc) with sentiment analysis.

Whenever we detect a news article with negative sentiment and identify a path to one of our customers we issue an alert to the risk team who can then take appropriate action. In this talk I'll highlight the key technologies we used to make this possible: NLP, Neo4j plugins, and how we built an end-to-end application using neo4j as the engine.

Wednesday April 22, 2020 1:30pm - 2:10pm
Room 3

Technical Case Study

2:15pm

Graphing Geodata

You love graphs and would like to switch to a graph database like Neo4j but you are hesitating because your business rely on geodata and you want to make sure you will be able to handle those cases in Neo4j? This talk is for you! We will cover some available tools and migration examples.

Speakers

Estelle Scifo

Wednesday April 22, 2020 2:15pm - 2:30pm
Room 6

Practitioner Deep Dive, Lightning Talk

2:15pm

Worst (And Best) Practices for Implementing Graph Data Science

Speakers

Sören Reichardt

Graph Analytics Engineer, Neo4j

Martin Junghanns

Graph Analytics Engineer, Neo4j

Wednesday April 22, 2020 2:15pm - 2:55pm
Room 2

Graph Data Science

Subject Graph Data Science

2:15pm

Bloom 1.2: Mastering Graph Visualization

Wednesday April 22, 2020 2:15pm - 2:55pm
Room 5

Practitioner Deep Dive

2:15pm

Ending the Licit Opioid Crisis with Neo4j and Artificial Intelligence

Leveraging Neo4j, machine learning, and our Analytics Driven Targeting methodology our team was able to identify pharmacies and prescribers diverting opioids and other controlled substances. Ultimately this has led to restrictions against numerous medical professionals in the U.S.

Our team has supported actions against numerous medical professionals in the U.S. Using Neo4j, machine learning, our Analytics Driven Targeting methodology, and bespoke diversion-specific risk factors we analyzed millions of prescription records and identified numerous prescribers, patients, and pharmacies diverting opioids and other controlled substances. Neo4j proved to be critical in our analysis because the relationships between entities proved to be some of our most valuable features in our predictive models. Additionally, Neo4j's graph engine, algorithms, and built-in scalability enabled us to analyze the massive amount of data rapidly.

Wednesday April 22, 2020 2:15pm - 2:55pm
Room 3

Technical Case Study

Subject Graph Data Science

2:15pm

Relevant Search with Graphs

"Text based similarity scoring has had his time, nowadays all that matters is context. And what is best suited to make sense of context ? A Graph of course !
Learn how to use graphs to improve your search engine and improve your users experience.This session will go through :

- Graph-Based search boosting

- Synonyms are graphs too

- Graph Algorithms useful for search

All will be demoed on the Neo4j community forum data.

Speakers

Christophe Willemsen

CTO, GraphAware

Christophe Willemsen is CTO at GraphAware, the world's #1 Neo4j consultancy. He is a Neo4j expert, consultant and trainer, having implemented graphs in various industries all over the globe. He focus now on the technical development of Hume, GraphAware's Graph-Powered Insights Engine... Read More →

Wednesday April 22, 2020 2:15pm - 2:55pm
Room 4

Technical Case Study

2:15pm

The shortest path to digitizing worldwide multi-modal transport planning at the Port of Rotterdam

Learn how Europe's largest and most innovative port, the Port of Rotterdam, is leveraging neo4j graph technology to run its multi-modal schedule route optimization engine. The core driver of our application Navigate. Our door-to-door route optimization application for container shippers.

We believe that transparency is key in the optimization of the logistics industry and in the reduction of CO2 emissions due to suboptimal routing. Our application Navigate therefore provides container-shippers and -forwarders a clear and neutral comparison of door-to-door options to ship their container from A to B. Combining schedule information from deepsea, shortsea, train, barge and truck operators. Enabling them to optimize their route on duration, arrival time, emissions and number of transfers.

Due to the combination of an urging problem, proven technology, business experts and development team, the engine was able to go from a proof of concept in our Innovation lab, to a minimal viable product, and into production within months. Starting up fast with cypher queries in Neo4j desktop and scaling up by using graph procedures in our cloud environment. Additionally the engine also serves as an enabler for machine learning models generating estimated times of arrival for vessels coming to our and other ports.

The talk is covering our process from an initial idea to bringing it into production as a SaaS solution, now used by ports on multiple continents. Our experienced benefits of having full control from data retrieval to application, as well as technical insights such as custom graph procedures using Kotlin.

Speakers

Riccardo Lippolis

Software Engineer, Port of Rotterdam

Jorrit van der Ven

Port of Rotterdam

Kevin Kruijthoff

Product Lead, Port of Rotterdam

A data scientist turned Product lead at Port of Rotterdam, responsible for the team realizing the Pathfinder engine as well as multiple applications. Enthusiastic about exploring the potential beneficial use of data analysis, modeling and simulation, and applying them in complex problems... Read More →

Wednesday April 22, 2020 2:15pm - 2:55pm
Room 1

Technical Case Study

2:35pm

Exploring NASA Open data

How’s NASA Open Data datasets related? Which datasets are related and which aren’t? How easy is it to search for specific data in NASA? Let’s go through the work of putting all this information in a Neo4j Database to get the most of one of the biggest open data sources ever.

Speakers

Bea Hernandez

Wednesday April 22, 2020 2:35pm - 2:50pm
Room 6

Technical Case Study, Lightning Talk

3:30pm

Molecules are Graphs! Lowering the Costs of Drug Discovery with Neo4j

Molecules are graphs! When you change part of this graph, swap one part out for another, add something in here, remove a little there you change how that molecule behaves and interacts with your body. This talk models alterations of molecular graphs as a network and applies it to drug discovery!

**Molecules are graphs!** The nodes are atoms and the edges are bonds. What happens when we take these graphs and their properties and put them in a graph database?

**Drug discovery is expensive**. The cost of producing a new drug is estimated to cost $2.6 Bn with a significant chunk of that cost coming from research and development of the drug molecule. Reducing down the cost of developing new therapeutics is key to helping patients. If researchers can make smarter decisions earlier in R&D the timelines and cost of bringing new drugs to patients will be reduced.

**Matched molecular pair** analysis (MMPA) is a method used in chemoinformatics that compares the properties of two molecules that differ only by a single chemical transformation (or graph alteration). An example of this would be if you were looking at two molecules that only differed by the substitution of a hydrogen atom for a fluorine atom. These two molecules whilst almost identical could have vastly different chemical properties. These **pairs** of compounds are known as **matched molecular pairs** (MMP), and any change in the properties of these molecules can be modelled on an edge linking them together.

This talk will explore how combining biological assay data with these chemical transformations in a **matched molecular pair knowledge graph (MMPKG)** using Neo4J allows for powerful exploration of chemical data. Through the use of Neo4j browser, cypher, and graph algorithms library new insights can be gathered to help answer the question on most medicinal chemists lips... _""which molecule should I make next?""_ and exactly how the MMPKG can be used and applied to real drug discovery problems to help drive this decision making process."

Speakers

Matthew Sellwood

Product Manager, IQVia

Matthew is currently a Product Manager at IQVIA, and has worked across the life sciences and healthcare industry. He earned his Masters of Chemistry and his PhD at the University of Sheffield in the UK. In his thesis work he researched the discovery of novel therapeutics for ALS (Lou... Read More →

Wednesday April 22, 2020 3:30pm - 4:10pm
Room 4

Graph Data Science

Subject Graph Data Science

3:30pm

Which Comes First, The Data Model or the Algorithm?

The intelligence community has applied link analysis to everything from modeling call records to financial transactions. But what happens when you apply the same techniques to the technical artifacts of cyberattacks? How do you avoid overthinking your data model when modeling such complex data?

Cybersecurity may be the ideal domain for graph analysis as the relationships between technical attributes are often more critical than the discrete values. For example, an attribute's maliciousness often depends on the surrounding context. This can include the presence or absence of other attributes, the behaviors that those attributes exhibited, and the similarity of that behavior with other attack vectors. Graphs and contextual link analysis are very effective mechanisms for identifying potentially malicious activity.

However, before performing any type of analysis, you need to create the data model! While many graph data model examples are reasonably straightforward, the modeling of cybersecurity data can become quite complex. You would ideally model the attributes of any real world artifact (e.g., an email or file), the occasions in which those attributes were seen together, the behavior that those attributes exhibited when they were observed, and the source of your knowledge about those relationships. But how much knowledge do you really need to encode in the graph? When should you rely on path traversals rather than leveraging more advanced graph algorithms? Do you need to create a hyper graph in order to capture the source of relationships? What are the performance implications? How do you expire data from the graph? And finally, how do you make some decisions and actually build something?

Speakers

Liz Maida

CEO, UpLevel Security (McAfee)

Liz Maida is the Founder and CEO of Uplevel Security (recently acquired by McAfee). She was previously a Senior Director at Akamai Technologies and served in multiple executive roles focused on technology strategy and new product development. She played a lead role in Akamai’s initial... Read More →

Wednesday April 22, 2020 3:30pm - 4:10pm
Room 2

Graph Data Science

Subject Graph Data Science

3:30pm

Building a Polyglot Streaming Architecture with Neo4j, Apache Kafka, and Elastic

Integrating Apache Kafka with other systems in a reliable and scalable way is often a key part of an event streaming platform. We will show how to build a Polyglot Streaming architecture with Neo4j and Elasticsearch (a Search Engine) by using the Apache Kafka and leveraging the Neo4j Streams Project

Integrating Apache Kafka with other systems in a reliable and scalable way is often a key part of an event streaming platform. Fortunately, Apache Kafka includes the Connect API that enables streaming integration both in and out of Kafka. Like any technology, understanding its architecture and deployment patterns is key to successful use, as is knowing where to go looking when things aren't working. In this context, we will introduce the Neo4j Streams project that enables Kafka Streams on Neo4j and we'll build a Streaming Poligot architecture composed by Neo4j and Elasticsearch (a Search Engine) by using the Kafka Connect platform.

Wednesday April 22, 2020 3:30pm - 4:10pm
Room 1

Practitioner Deep Dive

3:30pm

Neo4j 4.0: A Developer's Guide

Wednesday April 22, 2020 3:30pm - 4:10pm
Room 5

Practitioner How-To

3:30pm

First Line of Defense: How the Danish Business Registry fights fraud with Machine Learning and Knowledge Graphs

Regulation of fraudulent businesses is necessary in order to mitigate the negative impact on society. However it would be best if these actors are never given the means to commit fraud. The Danish Business Authority uses ML and knowledge graph to prevent registration of fraudulent businesses.

Speakers

Marius Hartmann

Team Lead, ML Lab, Danish Business Authority

Marius Hartmann is the team leader of the ML Lab at the Danish Business Authority and responsible for designing their ML data platform. Based on open source components, the synergy between machine learning and graph analysis enables near real time interception of fraudulant behaviour... Read More →

Wednesday April 22, 2020 3:30pm - 4:10pm
Room 3

Technical Case Study

4:15pm

Graph Analytics in Anti-Money Laundering at Manulife

Money launderers use complicated schemes to wash dirty money, and financial institutes need to fight back with advanced techniques. Using graph analytics, it becomes feasible to connect dots in very complicated schemes that traditional methods cannot handle. Nowadays, money laundering involves leveraging different types of financial instruments with more complicated schemes. Preventing money laundering and terrorist financing has become high priority for financial institutions.

Traditional methods of monitoring for AML typically involve static, rule-based alerts built from previous experience. The biggest challenge faced by traditional approach is that money laundering schemes continuously changing in a way that is difficult to detect. With the power of graph analytics, it becomes feasible to connect dots in complicated schemes that traditional methods cannot handle. In this talk, we will share our experience using advanced rules traversing hidden patterns in the data, creating graph-based features for machine learning and finding similar patterns using graph embeddings.

Speakers

Lin Gao

Data Scientist, Manulife

Wednesday April 22, 2020 4:15pm - 4:55pm
Room 3

Graph Data Science

Subject Graph Data Science

4:15pm

Journey Towards a Standard Property Graph Query Language

Wednesday April 22, 2020 4:15pm - 4:55pm
Room 4

Neo4j Tech Talk