NODES 2020 Agenda

Tuesday, October 20, 2020 • 8:00 - 17:00 EDT [ 12:00 - 21:00 GMT ]

Back to NODES Home

Our day will start with a Keynote Session
"The Next Decade of Graphs"
by Neo4j Founder and CEO Emil Eifrem
from 8AM-9AM ET

Emil Eifrem
Use Cases

9:05 - 9:50

Unlock Cyber Security with Knowledge Graphs

Graphs, Nodes, Relationships and Security - Graphs make it easy to explore hidden relationships. In this talk, I will showcase a threat modeling tool I have created with GrandStack. The tools aims to help model complex software security threats to usable graph applications.

Uday Korlimarla

Uday Korlimarla

Founder, inspektre

I live in Melbourne, Australia, I love React, Javascript, Graphql and Neo4j. With my love for application security, I am on full-sail ahead with my startup inspektre (inspektre.io)

Use Cases

9:55 - 10:15

Scalable, Reactive Product Development Framework for the Automotive Industry

We developed a Business Plan Gaming tool for the automotive development process that e.g. incorporates: a) scalable task durations, b) succeeding tasks that start once their predecessors have progressed to a certain maturity, c) optional paths Our knowledge graph adapts and scales meaningfully but also significantly based on our customers’ inputs.

Elena Kohlwey

Elena Kohlwey

Specialist Digital Engineering, RLE INTERNATIONAL

With completed studies in European Business and in Mathematics, Elena Kohlwey strives to make intelligent applications that yield Business benefits. Graph-based applications with Neo4j are a very exciting and promising field which Elena has enjoyed working in for the last one and a half years at RLE INTERNATIONAL. As a global player, RLE is aiming at playing a predominant role in the digital future of the automotive industry. Graph-based applications are just the beginning of our journey.

Uwe Kloss

Uwe Kloss

Head of Digital Engineering, RLE INTERNATIONAL

Uwe Kloss has had various engineering and management positions in the last 30 years in the automotive industry. Uwe has a key focus in the last 15 years on improving the product development process in the process, methods and tools domain, balancing the edge between tools and people.

Use Cases

10:20 - 10:40

Using Neo4j for Contact Tracing in the Philippines

Using QR code-based data, we helped a large multinational company track the progression of the COVID infection in the Philippines. Neo4j is particularly suited for this task as we can map interaction among people, places and time periods.

Additionally, we used Louvain Community and Neo4j Bloom to create visualizations that provide insights with which the company could use to stop the chain of transmission.

Wilson Chua

Wilson Chua

Managing Director and Founder, Future Gen International Pte Ltd

Wilson Chua has a master's degree in ITPM and certifications in Neo4j, Tableau and RapidMiner.

Use Cases

10:45 - 11:30

Using Cypher to Extend a Graph-Based Documentation

At first, TheBrain (thebrain.com) can be thought of as a digital document management tool, that allows for a graph structure organization of documents, urls and notes. No more wondering "which directory does it make more sense to put this doc in". You can put it in as many categories you like ! Because it's based on a graph structure, we'll see that it also helps elaborate your thoughts and understanding of a subject.

Automatic processing of the resulting database is limited though. In this talk I will show how I'm importing TheBrain DB into Neo4j, to automatically extract selected lists or tables and take full advantage of the graph structure, to meet different purposes.

Along the way I will expose some principles of my conception of information design, and a few thoughts about why I think digital tools are a huge epistemological breakthrough, because they make graph representation easy.

Véronique Gendner

Véronique Gendner

Information Design | Data Processing

I was born in France. Learned English because I had no idea of what job I wanted to do, and thought it would be useful anyway. Also learned Chinese, because it was fun to make sense of those mysterious characters.

I then learned Computational Linguistics as a way to connect the previous two, to the computers I'd been playing with since aged 10. (Too) many years spent trying to write a Ph.D., when actually still playing with computers, doing a lot of data processing. 10 years exploring the corporate world: more data processing and starting to build my way of doing Information Design. Then 5 years back in the research field, building web interfaces for data processing and information display (Constances.fr).

Now super excited to start (Sept 2020) a one year mission at the French National Institute of Geographical Information (IGN), to work on geolocalized data, with ontologies and labeled property graphs (Choucas project, TheBrain, Evernote, Outlook, TickTick, Inoreader, Prezi and methodologies.

Use Cases

11:35 - 12:20

Neo4j Security in Action

This session will be dedicated to security features available in Neo4j such as Role Based Access Control (RBAC), intra-cluster encryption and logging. We will also cover some gotchas and lessons learned that we discovered when deploying Enterprise Neo4j clusters.

Christophe Willemsen

Christophe Willemsen

Chief Technical Officer, GraphAware

Christophe is CTO at GraphAware, the World's #1 Neo4j solutions and consultancy. Christophe implemented Enterprise Neo4j based solutions in different industries across the globe. He now focuses on the research and development of GraphAware expertise in various strategic Neo4j related technologies, ensuring GraphAware remains the worldwide go-to Graph company.

Use Cases

12:25 - 12:45

Best Practices and the Fundamentals of Innovation When Prototyping Using Graph Databases and Neo4j

In this talk we will cover best practices when innovating and building successful prototypes and applications with graph databases.

We’ll talk about what we refer to as the “The 4 Pillars of Innovation” which includes:

  • Data capture (ingesting the most relevant data for the use case)
  • Data modeling and storage
  • Processing and analytics (queries and algorithms)
  • End user applications and insights (tangible end-results)

Finally, we’ll showcase an example of what this looks like in practice using a supply chain example, however, these innovation principles would apply for any industry or use case.

Alessandro Svensson

Alessandro Svensson

Head of the Innovation Lab, Neo4j

Alessandro Svensson is the Head of the Innovation Lab at Neo4j, based in San Francisco, CA, and has over 10 years of experience of working in the intersection of Design Strategy, Business Development and Data Science. He has held roles like Head of Innovation, Head of UX & Design, and Product Owner within large mulitnational corporations in both Europe and United States.

Use Cases

12:50 - 1:10

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.

Polley Wong

Polley Wong

CEO of VIUSPACE, Co-founder of We Create Group, Life-Long Advocate of Workspace Equality

Polley Wong is a serial entrepreneur. She’s the CEO of design technology and research company VIUSPACE 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 in the workplace and sponsors various STEM-related inclusion and diversity initiatives. She is a frequent speaker at conferences on industry and technology issues, and served as the co-chairwoman of the Architecture, Engineer & Construction Committee of VR/AR Association, the global industry association for Virtual Reality & Augmented Reality. Wong earned her Master of Arts degree in New York School of Interior Design, and Bachelor of Science degree in Computer Engineering from the Ming Chuan University in Taiwan.

Use Cases

1:15 - 2:00

RDBMS & SQL to Neo4j & Cypher: Transitioning to the Right Technology for Your Data

Building scalable, competitive and reliable applications means understanding and taming the complexity and dependencies found across the spectrum in today’s IT landscape. Crosscode equips developers with the most comprehensive and automated application dependency mapping tool available, providing the freedom and agility to work with the most scalable and innovative technologies for any datasets.

In this session, you’ll learn when to choose a graph-based database over an RDBMS from Jon Gentsch (Senior Software Engineer, Crosscode) and Robert Vrooman (Senior Software Engineer, Crosscode). They’ll highlight how to easily transition your schema from applications based on the traditional SQL-driven RDBMS to Neo4j graph database technology, with a dynamic-schema architecture and powerful Cypher query language — resulting in speeds, scalability and reliability that give you a 360-degree view of dependencies found across your platforms, applications, databases, and APIs in real time.

Jonathan Gentsch

Jonathan Gentsch

Senior Software Engineer, Crosscode

Jon is a technology industry veteran with over 13 years’ experience in complex software development. As a Crosscode Senior Software Engineer, Jon’s dynamic engineering expertise translates the technical aspects of designing extensible data models into scalable solutions, including transitioning real-world data, modeling the schema, and optimizing mechanisms around backend storage.

Robert Vrooman

Robert Vrooman

Senior Software Engineer, Crosscode

Rob has 7 years’ experience as a full-stack engineer with a specialization in Spring Boot development. As a Senior Software Engineer at Crosscode, Rob is responsible for creating pluggable architecture around the decomposition agents that retrieve external data. Rob’s expert focus on build process, continuous integration and deployment, and rigorous CI/CD practices drives innovation across the whole team.

Use Cases

2:05 - 2:25

Modeling Healthy Communication in Neo4j

Human communication gets messy. But what if we could model communication using graphs to understand prevailing emotions and unmet needs in a population? In this quick talk, we'll analyze public sentiment within a hypothetical scenario: public comments on a proposed local development project. Employing the Nonviolent Communication framework popularized by Marshall Rosenberg, we'll use a graph database to ask and answer questions that can inform compassionate public policy.

Matt Cloyd

Matt Cloyd

Creator of Aspen | Civic Technologist

Matt is a civic technologist, helping support democracy by improving the relationship between constituents and governments via thoughtful technology. They have a background in conflict resolution, mindfulness, and geography/GIS, and see technology as a tool best applied in service of equity, justice, and peace-building.

Matt has worked as a mediator, a project manager & developer at OpenCounter (a civic tech startup), a web developer for the Commonwealth of Massachusetts, and has volunteered as an advisor to local representatives' campaigns and as Chair of a neighborhood association.

Use Cases

2:30 - 2:50

Finding Brazilian Company Groups in a Large Graph

This session will show how we created a graph using Brazilian government open company database and applied graph algorithms such as LPA and Louvain for finding non-explicit company groups.

itor Avancini

Vitor Avancini

Chief Technology Officer, Indicium Tech

Vitor Avancini graduated at Federal University of Santa Catarina, Brazil and worked 5 years as a big data engineer at a company that delivered e-commerce recommendation systems for many of the biggest brazilian ecommerce websites. Vitor also took personal interest in graph analysis and has been using Neo4j in production for around one year. Took personal interest in graph analysis and today been using neo4j in production for around one year."

Daniel Avancini

Daniel Avancini

Chief Data Scientist, Indicium Tech

Daniel Avancini is the Chief Data Scientist at Indicium Tech, a Brazilian data science and big data consulting firm.

Fernanda Doria

Fernanda Doria

Data Scientist, Indicium Tech

Fernanda Doria is a Data Scientist as Indicium Tech.

Use Cases

2:55 - 3:40

We Save Lives! Using Neo4j to Find a Bone Marrow Donor

Be The Match is part of a global network of donor registries that provide access to 40 million volunteer donors who might be the only match for a patient in need of a transplant. The matching is complex due to genetic diversity of the HLA genes which results in partial and ambiguous data. We use Neo4j to represent patients donors and HLA types in a graph to provide matching that is faster, simpler, and more flexible and accurate than previous methods.

Martin Maiers

Martin Maiers

Vice President of Innovation, Be The Match

Martin Maiers is Vice President of Innovation at Be The Match where he has worked for 24 years. He leads an R&D translation program focused on applying new tools for HLA matching and algorithm development to improve cell and gene therapies. With his team he has developed a number of tools and methods for searching large donor registries with missing or partial information to identify suitable hematopoietic stem cell donors. He holds a degree in Mathematics from the University of Wisconsin and a Master’s Degree in Computational Biology from the University of Minnesota.

Use Cases

3:45 - 4:30

Neo4j: A Taxonomy-based Approach on Compliance Risk Management

This session will explore how Neo4j can give insight in connected (compliance) risk management.

Arthur Namias de Crasto

Arthur Namias de Crasto

Compliance Officer

Use Cases

4:35 - 5:20

Power & Emergent Org Structures

Agency Management is built on a simple concept: Experts share knowledge. Most of us are solving common problems, problems we share with colleagues if not industry wide, yet sharing and collaboration depends on the org structure and org culture. In "Accelerate" (by Dr. Forsgren, Jez Humble, and Gene Kim) organizations are characterized as Pathological, Bureaucratic, or Generative; Which one does your org fall in? We can build a simulation that measures the emergent behaviour of sharing and collaboration, enabling us to characterize the emergent org structures and their affects on speed, productivity, and creativity.

Sean Gonzalez

Sean Gonzalez

Dataman, General Influence

Sean is on a mission to change how experts retain agency over their work, and influence their organizations in general. General Influence is an IP Marketplace that enables experts to invest their solutions, and provides organizations with passive profit channels.

Visualization

9:05 - 9:50

Stopping Money Laundering with Graph Analytics

Why is graph technology a perfect match to defeat the criminals responsible for money laundering? In this session, you will discover how Neo4j and Linkurious can help detect complex schemes such as round-trip flows, smurfing or synthetic identities to enhance financial institutions’ compliance.

Jean Villedieu

Jean Villedieu

Head of Sales, Linkurious

Jean is a co-founder of Linkurious.

Visualization

9:55 - 10:40

Bespoke Visualization Tools for Complex Data

In this session, Sebastian shows how the Covidgraph.org project was able to help researchers across the globe to intuitively navigate the contents of a large Neo4j database. Using a custom made visualization app proved to result in a much improved user experienced and helped researchers to quickly gain insights into the data. You will learn about the tools and frameworks being used by the Covidgraph.org project and how you can reuse that knowledge in your own projects.

Sebastian Müller

Sebastian Müller

Chief Technology Officer, yWorks

Sebastian has been working in the field of graph and diagram visualization for more than 20 years. He joined yWorks 2002 where he works as lead software architect and CTO on the yFiles diagramming libraries, today.

Visualization

10:45 - 11:45

Building a WebGL Graph Visualization with PIXI.js

Choosing a graph visualization library for a web app is usually thought to be a choice between existing open-source and commercial libraries. There is another option though: Build your own custom graph visualization!

Jan Zak

Jan Zak

Freelance Data Visualization Engineer | Consultant

Jan is a Freelance Data Visualization Engineer / Consultant, specializing in high-performance geospatial and node-link graph visualizations in web applications. He is an author of popular open-source libraries, and a frequent contributor.

Visualization

11:10 - 11:30

Scrape from Multiple Sources Easily for Better Visualizations

Data that requires you to scrape from multiple URLs requires more attention in terms of validation. We will show a process to scrape from multiple URLs and to create cypher queries directly using Beautiful Soup, resulting in better visualization without any performance issues. We will demo the visualization below. https://community.neo4j.com/t/visualizing-covid19-spread-index-cases-across-continents-with-neo4j/18039

Rajesh Gaddipati

Rajesh Gaddipati

Senior Application Developer, Fujitsu Consulting India pvt Ltd.,

Rajesh Gaddipati began his career as a Business Intelligence Analyst in 2010 and then progressed in building complex and high volume data warehouse for Analytics. Rajesh has proficient experience in BI tools like IBM Cognos, Qlikview, Tableau, Alteryx, Talend, MSBI, Performance Engineering and Azure Data Lake Store and is a Neo4j Certified Professional. He holds dual master's degrees in Environmental Science and History.

Visualization

11:35 - 12:20

Automating the Neo4j Pipeline for Data Shaping and Visualization with Power BI

According to the AI firm Appen, data scientists spend 80% of their time shaping data; yet data shaping is one of the least impactful aspects of the data-to-decision process. Conversely, both data visualization, which enables decision makers to aggregate and categorize complex information, and analytics, which provides a contextual framework for decision-making, only receive about 20% of the analysis effort. This mismatch in effort is not only unfortunate, it shortchanges the most crucial aspects of the data story-telling intent by limiting the time analysts and decision makers require to identify relevant insights, to establish the evidence necessary to support important enterprise objectives, and to hone the underlying narrative of the data to enable better decision-making.

Bryant Avey

Bryant Avey

Chief Geek, internuntius, inc.

Bryant Avey is the Chief Geek for internuntius. Bryant has over twenty years of technical and management experience in a wide range of industries and organizations from start-ups to Dow 30 corporations. He has also worked with over 40 state and local government entities. Bryant specializes in data integration and data architecture as well as application and solution architecture. He is the author of the upcoming book, Data at the Enterprise Level: The Next Convergence.

Visualization

12:25 - 12:45

10+ Entity States in Graph Visualisation And How To Make The Best Of Them

Entities in graph visualisations change appearance based on data and user interaction. We will explore 10 of the most useful entity states, including selected, highlighted, located, hidden, and grouped. We will use Cypher to enrich entities with contextual information enabling powerful interactions.

Dr. Miro Marchi

Dr. Miro Marchi

Graph Specialised Anthropologist, GraphAware

Dr. Miro Marchi is a Senior Consultant at GraphAware. He holds a Ph.D. in Cultural Anthropology from University of Verona, where he studied the consumption of graph visualisations by members of communities of practice. Miro's skills and interests lie between social and computer sciences. He combines experience in ethnographic analysis (deep immersion in the field to understand stakeholders practices), in graph data modelling with Neo4j, and in JavaScript data visualisation. He is passionate about the game-changing effect of network thinking and graph technology on organisations and processes.

Michal Trnka

Michal Trnka

Senior Consultant, GraphAware

Michal Trnka is a Senior Consultant at GraphAware. He has almost 10 years of experience with software engineering and development of projects ranging from internal tools for corporations through open source frameworks to outsourced development. Michal is passionate about automation of the software development and deployment process and loves to find innovative and creative solutions to problems. He adds experience from academia: he is currently finishing his Ph.D at Czech Technical University and is a Fulbright scholar.

Visualization

12:50 - 1:10

Visualising All Drupal.org Data in Neo4j

Drupal.org, the main platform for the Drupal Community, stores large amount of data related to users, companies, modules and themes, apart of forums, issues and other information related to projects. Recently we consumed all this data using their public API (https://www.drupal.org/drupalorg/docs/apis/rest-and-other-apis) and represented it in Neo4j for a proper analysis and visualisation. In this session we will explain how we did it and what results we obtained from the experiment. Expect some cool code snippets and API integration best practices, apart of sharing our experience during the data analysis and visual representation challenges we faced.

Ruben Teijeiro

Ruben Teijeiro

Co-Founder, Youpal

Ruben is co-founder of Youpal, an ICT company from Sweden, where they create innovative products based on Open Source software. He is an active member of the Drupal Community and participated as speaker is several events around the globe. He has collaborated in relevant projects with companies like Telefonica, Unicef and several Government organisations in Spain and Ericsson and Tieto in Sweden. Currently he is involved in YouData, the Machine Learning and Artificial Intelligence team at Youpal where Neo4j is one of our major technologies.

Visualization

1:15 - 2:00

Getting Graph Questions Answered through Neo4j Bloom

When working with graphs, many graph questions require visual and interactive methods to explore connections in order to discover answers in the graph. Neo4j Bloom helps developers, data scientists and analysts search their graph and explore results visually without having to write Cypher queries.

This session will introduce the latest improvements in Bloom while also covering frequent how-tos and subtle tips/tricks for getting the most out of the Bloom experience.

Anurag Tandon

Anurag Tandon

Director, Product Management, Visualization, 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 passionate about enabling visual experiences that allow end users to freely explore their data assets. Anurag holds a B.Tech. from IIT Bombay and an MS from the University of Maryland, both in Mechanical Engineering, and an MBA from the University of Michigan. He lives in Northern Virginia and enjoys leisure time with family and friends, and wandering to new places; ideally both.

Visualization

2:05 - 2:50

Anomalies, Inconsistencies and Fraudulent Behaviour – Data Mining with Neo4j and GRANDstack

Join Christian’s deep dive into building a graph visualization application with GRANDstack. With interactive examples, he’ll detect and visualize Wikipedia article fraud, using Neo4j to model real-world examples. He’ll also demonstrate techniques for uncovering hidden insight that you can apply to many other use cases.

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 helps spot 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 suspicious networks in a React front-end

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

Christian Miles

Christian Miles

Graph Technology & Visualisation Specialist, Cambridge Intelligence

Christian has worked at the intersection of data analytics and visualisation since completing his Master’s in Computer Science & Maths from Bristol University. He has worked with Fortune 500 companies and government agencies around the world and applies graph analytics techniques to a variety of different domains.

Visualization

2:55 - 3:40

Network Like an Egghead: Analytics and Visualization on LinkedIn

LinkedIn offers a great tool to grow and study your professional network. It even offers ways to directly download your data. This talk demonstrates advanced ways to visualize and analyze your network, given your own network data. This session will cover:

  • How to collect the needed data in safe ways
  • Data pipelines
  • Visualization tools and configurations
  • Analytical methods which deliver actionable insights
Keita Broadwater

Keita Broadwater

Machine Learning Engineer and Founder, Peerce.ai

Dr. Broadwater is a machine learning engineer and technology executive with interest in the application of graph technologies. He is currently writing a book on Graph Neural Networks, to be published in spring 2021

Visualization

3:45 - 4:30

Not All Visualizations are Created Equal

Studies at MIT show that the human brain can process images that the eye sees for as little as 13 milliseconds. Not only does the brain need a very short time to take in an image, but half of the human brain is devoted directly or indirectly to vision. This means that visual representation of data can be a very powerful tool. However, this can only be realized if users can make sense of the visualization. This talk covers how to tailor the visualization to the use case, how to make wise data choices, how to incorporate an iterative process that integrates user feedback, and what kind of in-tool functionality to focus on.

Julie Fisher

Julie Fisher

Risk Modeling Scientist, Asurion

Julie is the subject matter expert on graph at Asurion for the fraud machine learning team, conducts Knowledge Sharing sessions on Python programming for the Risk Analysts, and has a master’s degree in Data Science from Lipscomb University.

Visualization

4:35 - 5:20

Graph Provisioning and Data Enrichment on Election 2020 Twitter Data

Follow along as Alex walks through an end-to-end workflow to gather, enrich, and analyze tweets on the upcoming 2020 election. In a comprehensive demo, she will illustrate how data collection can be automated leveraging serverless cloud services for automated ETL, and using Graph/Neo4j for gluing different data sources. Enabled by GraphXR's visualization platform, she will guide step-by-step the process of enriching data with named entity extraction and intent classification for complex visual analysis and reporting.

Alex Law

Alex Law

Communication Coordinator, Kineviz

Alex Law speaks on the intersection of art and technology with a background in biology, dance, and start-up communications. She is the Communications Coordinator for Kineviz, supporting the community of graph data enthusiasts with the foundations of Kineviz' no-code data analytics platform: GraphXR. Her background combines the sciences (Bachelors in Human Biology from UCSC) and the arts (Pilot Artist at ODC / Director of Marketing and Communications at Kinetech Arts) as well as experience in public healthcare and administration.

Knowledge Graphs

9:05 - 9:50

Knowledge Graphs Powered by NLP and Network Science

How do you turn textual data (such as Covid-19 medical research) into a structured knowledge graph? This session covers the path from a corpus of research papers through Natural Language Processing, entity (relation) extraction and graph algorithms from Neo4j's Graph Data Science Library to highly informative connected insights organized in a knowledge graph.

Vlasta Kůs

Vlasta Kůs

Lead Data Scientist, GraphAware | Dr. Who Fan

Vlasta Kůs is a machine learning, deep learning and natural language processing enthusiast, with a background in particle physics research, 10+ years of experience in software development and statistical data analysis. Vlasta is a Neo4j certified professional and has specialization in using Machine Learning for building Knowledge Graphs (Hume @ GraphAware).

Knowledge Graphs

9:55 - 10:40

Build a Knowledge Graph Using NLP and Ontologies

In this session, Mark and Jesús will show how to build a Knowledge Graph based on public resources, using techniques like:

  • Entity extraction from unstructured data (articles) using NLP
  • Knowledge representation using Ontologies

Once built, they will show how to run different types of analysis and inferencing on the Knowledge Graph for various uses.

Mark Needham

Mark Needham

Developer Relations Engineer, Neo4j

Mark Needham is a graph advocate and developer relations engineer at Neo4j. He helps users embrace graphs and Neo4j, building sophisticated solutions to challenging data problems. He is the co-author of the O'Reilly Book: Graph Algorithms: Practical Examples in Apache Spark and Neo4j.

Jesús Barrasa

Jesús Barrasa

EMEA Sales Engineering Director, Neo4j

Jesús Barrasa is a Neo4j field engineer based in London. He combines over 15 years of professional experience in consulting and professional services in the Information management space. Prior to joining Neo4j, Jesús worked at Ontology Systems for seven years where he got first hand experience with large graph database deployments in many successful graph-based projects for major telecommunications companies all over the world.

Jesús holds a Ph.D. in Computing Science from the Politécnica University of Madrid, where he carried out his research on graph data modeling and Semantic Technologies."

Knowledge Graphs

11:35 - 12:20

QKnows Knowledge Graph

This session explores Knowledge Graphs at Scale, using the world's patent and journal data, 5 bio nodes, 50 bio relationships and counting, and fast global and local traversals with Java-embedded Neo4j enterprise.

Janez Ales

Janez Ales

Data & Algorithms Research Scientist, BASF

Janez Ales made a first graph back-end in 1987 :). All of Janez's degrees are in Algorithmic Graph Theory, and Janez has been working on Knowledge Graphs at Scale at BASF.

Knowledge Graphs

12:25 - 12:45

Representing and Managing Consent for Data Sharing with Knowledge Graphs

The expansion of the Internet of Things has allowed connecting everyday devices to a network that could be accessed at any time and has defined data as a new currency. Data sharing has been a research topic for many years but a unified solution that could be used as a standard has not been presented yet. Further, ways to enable data sharing and make the process fully transparent, from receiving consent until it is revoked, to the end user are yet to be discovered.

My research focuses on sensor data and will solve the issue of data sharing between multiple entities based on informed user consent, and will be implemented with knowledge graphs. This study will raise awareness regarding semantics, data sharing, user consent and the implications of its issuing and withdrawal.

Anelia Kurteva

Anelia Kurteva

Ph.D. Researcher at STI Innsbruck, University of Innsbruck

Anelia is pursuing a PhD in Computer Science (2019-ongoing), has a Master of Science in Advanced Computer Science (2018-2019) and a Bachelor of Science in Computer Science with Visual Computing (2015-2018), in addition to Neo4j Certified Professional and Stanford Online Machine Learning certificates.

Knowledge Graphs

12:50 - 1:10

A Survey as a Graph

Hundreds of surveys and censuses are conducted worldwide every year on a CAPI (Computer Assisted Personal Interview) platform using tablets for data collection. At the end you have the data in dozens of cryptic files cross-referencing each other. Making sense of these data is anything but trivial.

The population underlying a survey usually consists of a network connecting places, households, people, assets and agricultural areas. I used Neo4j to map census data for a Health and Demographic Surveillance System (HDSS) as a graph. Some special challenges of survey data and their graph solutions are presented.

Carefully mapping the data under program control onto a graph saves countless months of data cleaning and preparation for the researchers analyzing them later. Hundreds of similar surveys could benefit from such an approach.

Klaus Blass

Klaus Blass

Survey and IT Consultant, The World Bank

Consultant at the Development Data Group at the World Bank, assisting National Statistical Offices worldwide to conduct surveys and censuses.

Knowledge Graphs

1:15 - 2:00

Extending a Knowledge Graph from Wikidata

Starting from a business oriented knowledge graph, we will see how to build SPARQL queries to query Wikidata and enrich our knowledge. From this enriched graph, we will build Cypher queries to enhance graph-based search.

Estelle Scifo

Estelle Scifo

Data Scientist

Estelle Scifo is a data scientist entered into the field from physics, hobbyist Python and React developer, graph & Neo4j lover. Estelle is the creator and maintainer of neomap (Neo4j Desktop application to visualize spatial data) and the author of "Exploring Graph Algorithms with Neo4j" (video) and "Hands-on Graph Analytics with Neo4j" (book) via Packt Publishing.

Knowledge Graphs

2:05 - 2:25

Using Software Agents for Querying Knowledge Graphs

In this talk, I'll introduce question-answering chatbots over the command line to query. Querying will be rule-based as proof of concept. Neo4j will be used for dealing with knowledge graphs.

Siraj Munir

Siraj Munir

Data Scientist | AI Researcher | Student

Siraj Munir has been using graphs for last two years to solve real-world problems semantically and graphically. Siraj has published five papers in this area of interest and two more are in the publication pipeline.

Knowledge Graphs

2:30 - 2:50

Project Domino: Fighting COVID Misinformation at Scale with Citizen Data Science

With most adults getting news from social media or from colleagues who do, misinformation has grown into an infodemic. Project Domino is an open data science effort to tackle COVID public health awareness by detecting social media manipulation and powering data-driven interventions. This talk shares how graph analytics has been core to how we’ve reported on COVID topics from donation scams to mass political manipulation. We focus on three areas: Project Domino, handling large-scale streaming connected social media data, and facilitating open collaboration over best-of-breed graph data tools. A recurring theme will be how we use Neo4j as the common knowledge graph layer for working with technologies like GPU analytics (Graphistry, Nvidia RAPIDS), deep learning (BERT), automation (Prefect), and more.

Sean Griffin

Sean Griffin

Chief Executive Officer, Disaster Tech

Sean Griffin is founder and CEO of Disaster Tech. Sean served as Director for Incident Management Integration Policy on the US National Security Council at the White House, and is an active duty veteran of the U.S. Navy and Naval Nuclear Power Program.

During his White House term (under two U.S. Presidents), Sean led the Executive Office of the President and inter-agency policy coordination for major disasters and incidents.

Additionally, Sean led emergency management, training, and exercise programs at the U.S. Department of State, Defense Logistics Agency, & National Institutes of Health. He also volunteered time to Chair the Federal Sector Emergency Managers Caucus for the International Association of Emergency Managers (IAEM), and is a leading member of the Private Sector Committee for the National Emergency Management Association (NEMA).

Leo Meyerovich

Leo Meyerovich

Founder, Graphistry, Inc.

Leo Meyerovich founded Graphistry to supercharge visual investigations with GPUs, graphs, and automation. That has led to working with many teams on problems challenging cybersecurity, fraud, cancer genomics, and even medical misinformation. Graphistry builds on his research at UC Berkeley. Leo's most-referenced publications are in securing programming languages, and his award-winning projects include the first functional reactive web language, the first parallel web browser, programmable GPU visual analytics, and the sociological foundations of programming languages. These ideas are now used by popular browsers and web frameworks, and received awards including SIGPLAN 10 year Test of Time, Best of Year, and multiple best paper awards. He helped start ProjectDomino.org where citizen data scientists are tackling COVID medical misinformation.

Knowledge Graphs

2:55 - 3:40

NLP and Graphs Go Hand in Hand

How can we leverage the power of Knowledge Graphs in NLP? This session shows how to give structure to unstructured data.

Tomaz Bratanic

Tomaz Bratanic

Graph Data Analyst

Tomaz Bratanic loves to work with graphs and write about various graph analytics approaches in his blog. Tomaz is very excited about the intersection of ML and Graph technologies.

Knowledge Graphs

3:45 - 4:30

Developing a Knowledge Graph of Your Knowledge, Skills, Abilities, Tasks and Training (KSATT)

Understanding occupation elements and employee skillsets is essential to properly align your workforce, identify skill gaps, emerging skills and career/training paths. In this presentation we will develop an occupation knowledge graph from open data, augment with inferred employee attributes and use graph data science procedures to explore the dataset.

David Meza

David Meza

Sr. Data Scientist, NASA

David Meza currently serves as a Sr. Data Scientist for the NASA’s Office of Human Capital in D.C. He previously served as Chief Knowledge Architect at NASA Johnson Space Center (JSC). During his tenure at NASA, he has worked in all aspects of the Information Technology field developing and deploying several IT systems in use at JSC. His desire to improve IT processes and systems lead him to earn a Master’s certificates in Project Management and Six Sigma in addition to becoming a NASA certified Lean Six Sigma Master Black Belt. Mr. Meza is conducting research in People Analytics, Automatic Classification algorithms, domain specific search interfaces, topic modeling, data driven visualization and a graph neural nets. He holds a Master’s in Engineering Management from the University of Houston Clear Lake where he is currently pursuing a Doctorate in Education.

Knowledge Graphs

4:35 - 5:20

Mastering Enterprise Metadata with Neo4j

Modern enterprises not only have a myriad of data sources, from real-time events, transactional, Big Data, and many other systems, but they also boast a rich ecosystem of thousands of APIs & treasure of deep technical metadata. How do you organize and gain insights from all of this? In addition, there is a trove of data coming from other sources such as millions of datasets, SQL queries, slack chats, thousands user hierarchies, orgs & locations, access controls, Wiki pages, JIRA tickets and more. Normally, these sources are all disconnected from each other, and valuable insights are missed.

At PayPal, we are implementing GEM: the Graph of Enterprise Metadata, a system that connects and puts all the critical metadata under one umbrella. GEM is built on top of Neo4j and Apache Spark and sports a range of metadata ingestion components. GEM manages a rich graph of entities and connections, it applies graph algorithms for analysis and recommendations. And in the future - GEM would apply ML model to derive insights. These help answer critical questions around data catalog, security, and governance initiatives for systems supporting financial transactions for our 346 millions of users. In addition, we envision this graph of enterprise metadata to empower PayPal at scale & accelerate the journey of reaching 1 Billion Customers.

Dr. Vladimir Bacvanski

Dr. Vladimir Bacvanski

Principal Architect, PayPal

Dr. Vladimir Bacvanski is a Principal Architect with Strategic Architecture at PayPal. He is the lead architect for Privacy and Developer Experience. Before joining PayPal, Vladimir was the CTO and founder of a custom development and consulting firm. He is the author of the popular O'Reilly course "Introduction to Big Data" and a coauthor of the O'Reilly course on Kafka. Vladimir received a PhD degree in Computer Science from Aachen University of Technology in Germany.

Deepak Chandramouli

Deepak Chandramouli

Software Engineering | Enterprise Data Platforms, PayPal

Deepak Chandramouli is an Engineering Lead in PayPal’s Enterprise Data Platforms Organization. Deepak currently manages the engineering for products - UDC (Unified Data Catalog) and Gimel.io (Apache Spark based Data Abstraction Layer). Deepak incubated Gimel and helped open source it. More recently, Deepak is focusing on building scalable Data Catalog in the context of emerging Data Governance & Regulatory demands. Deepak’s prior speaking experiences at conferences include:

Graph Data Science

9:05 - 9:50

Malt Aware: Discovering What to Drink with Neo4j

The ability to build simple but effective recommendations is underrated. This talk focuses on how to produce good starting-point recommendations for whisky using Cypher that are of higher quality than those we see at our favourite online stores.

Luanne Misquitta

Luanne Misquitta

Vice President of Engineering, GraphAware

Luanne Misquitta is VP of Engineering at GraphAware, the World's #1 Neo4j solutions and consultancy and has been (happily) working with Neo4j for over 10 years. She was a core committer to Neo4j OGM and SDN 4, has spoken at GraphConnect in Europe and the US. She currently enjoys consulting at GraphAware's key clients on graph modelling, Cypher, best practises and the application of graphs in various domains.

Graph Data Science

9:55 - 10:15

Low Latency Kafka to Neo4j Data Streaming on Google Cloud

This will demo a project Hop pipeline streaming data from Kafka into Neo4j on Google Cloud DataFlow (using Apache Beam). We'll show the running pipeline and explain what's going on.

Matt Casters

Matt Casters

Chief Solutions Architect, Neo4j and Kettle Project Founder

Matt is the founder of the Kettle Project since 2001. He's working as a chief architect of solutions at Neo4j using Kettle to load data into graphs.

Graph Data Science

10:20 - 10:40

Halal Detection Using Graph Algorithms

Halal food is the main concern for a Moslem in daily life. The number of halal-certified products is small. Therefore, We will present how to detect the halal status of a product based on its similarity ingredients. We will exploit the Neo4j similarity algorithms to compare between halal-certified product and halal-uncertified products. The dataset is available at http://halal.addi.is.its.ac.id/.

Nur Aini Rakhmawati

Nur Aini Rakhmawati

Vice Head of Halal Centre Institut Teknologi Sepuluh Nopember Surabaya

Nur Aini Rakhmawati is the founder of Linked Open Data Halal (http://halal.addi.is.its.ac.id) and the Vice head of Halal Centre Institut Teknologi Sepuluh Nopember (ITS) Surabaya (http://halal.its.ac.id). She is an assistant professor of Information Systems Department, ITS, Surabaya, Indonesia.

Graph Data Science

10:45 - 11:30

Artificial Intelligence: To Trust or Not To Trust?

When we use AI to predict the risk of an investment, take lending decisions or provide explanations for high-scoring cases of suspected fraudulent activities, black-boxed AI is not suited: human experts must be able to validate the truth of AI produced results and gain new insights and graph technologies add the required context for this level of explainability.

In this session, Lorenzo and Surya will introduce "Galileo XAI", the brand new LARUS graph-based platform for explainable AI powered by Fujitsu Deep Tensor®.

Lorenzo Speranzoni

Lorenzo Speranzoni

Founder and CEO, LARUS

Lorenzo has been a pragmatic and passionate IT-expert in software architectures and Agile methodologies since 1997.

After some significant experiences in business critical projects, in 2004 he founded LARUS Business Automation where he currently hold the position of CEO.

He still maintains his great passion for software development by supporting his R&D team and actively contributing to the implementation of several projects.

Since 2013, he's been focusing on NoSQL technologies and in 2016 he was nominated Neo4j Ambassador.

Surya Josyula

Surya Josyula

Director, Fujitsu Laboratories of America, Inc.

Surya Josyula is Director of Marketing at Fujitsu Laboratories of America where he works on outbound initiatives for new innovations including technology incubation and co-creation with customers and partners. Prior to Fujitsu Laboratories, Surya spent 15 years at Sun Microsystems in various engineering and marketing roles.

Graph Data Science

11:35 - 12:20

Molecules are Graphs! Graph Data Science for Drug Discovery

Matthew Sellwood, currently a Product Manager for IQVIA, will share an open source project that makes use of the graph data science library for lead optimisation of molecules in drug discovery. The project makes use of open source databases alongside the graph data science library to find new insights that could help chemists decide what molecule to make next in the process of designing a potential new drug.

Matthew Sellwood

Matthew Sellwood

Product Manager, IQVIA

Graph Data Science

12:25 - 12:45

Graph Database as Catalyzer from Machine Learning in Pharma

Graph databases are a great catalyzer for development applications and models based on machine learning. This session is about how can we take advantage of graphs and use them for apply in the pharma sector. Today, in light of COVID-19, the capacity on well-modeling data is very important. In this sense, graphs have many capabilities to collect and get data.

Benito Gerónimo Marcos

Benito Gerónimo Marcos

Chief Technology Officer, Hadox Human Networks

Benito Gerónimo Marcos is a Master in Computer Science. For about 20+ years, Benito has been working as a Innovation Manager in several industries and companies, with expertise in Artificial Intelligence applied to Robotics and Computer Vision. For many years, Benito has participated as speaker presenting on projects about machine learning, computer vision, big data and recently blockchain.

Graph Data Science

12:50 - 1:10

Beyond the (Wine) Blend: Using Graph Structures to Fill the Blanks

In this session, Lju will show an approach she used to do just that, based on a wine data set she's been working on from Kaggle. Her approach is based on generating reference data based on existing information, and then using that to cross-reference and infer what the blanks should contain.

Lju Lazarevic

Lju Lazarevic

Developer Advocate, Neo4j

Graph Data Science

1:15 - 2:00

Write Your Own Algorithms Using the Pregel API

Ever wanted an algorithm that wasn’t available in the GDS library? Now we make it super easy for you to write your own algorithms to leverage our super efficient analytics infrastructure, without needing to learn our low level, internal APIs. We’ll cover the basics of pregel, a parallel graph processing framework, and demonstrate how you can quickly use our Pregel API to implement an algorithm and expose it via Cypher.

Sören Reichardt

Sören Reichardt

Software Engineer, Neo4j Team Graph Analytics

Sören is a software engineer in the Neo4j Graph Analytics team concentrating on high performance java implementations of graph algorithms. Outside of work he likes to play guitar and do all kinds of sports. Prior to joining Neo4j, he was studying at Leipzig University.

Martin Junghanns

Martin Junghanns

Software Engineer, Neo4j Team Graph Analytics

Martin Junghanns has been writing code for graph-based systems for quite a few years.

Graph Data Science

2:05 - 2:50

Graph Native Learning: Introducing GraphSAGE and Model Catalogs in Neo4j

We’ll present on how to build an end to end machine learning pipeline using graph embeddings from Neo4j’s graph data science library to build predictive models. We’ll cover everything from loading your data, calculating your embeddings, extracting data into Python and training a classifier model.

Alicia Frame

Alicia Frame

Lead Product Manager, Neo4j Team Graph Analytics

Alicia is the lead product manager for data science at Neo4j (and still a data scientist at heart!).

She’s excited to build tools that empower users to solve critical problems. Her background is in the life sciences, and during the course of her career she’s always worked to develop models and dashboards to help scientists and end users answer business critical questions using data and predictive models. As a product manager at Neo4j, she sees her role as democratizing state of the art academic research so it can be leveraged by end users in a scalable, easy to understand way.

Amy Hodler

Amy Hodler

Graph Analytics Program Manager, Neo4j

Amy is a network science fan, 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 predict dynamic behavior.

Graph Data Science

2:55 - 3:40

Leveraging Dimensionality for Graph-Based Recommendations with Sparse Data

Similarity metrics between products or customers forms the foundation of many recommendation engines, by compressing data onto a low-dimensional space by comparing vector similarity using Cosine or Jaccard distances to find similar products or customers. However, most of our data sets tend to be sparse and high dimensional, where we have limited information about many different aspects of a customer or product, so compressing our data loses valuable information. In this talk we show how data sparseness and high dimensionality can be solved using Cypher built on a property graph model of beer data. This talk will present the entire end-to-end process of generating recommendations, covering: 1) schema design; 2) data ingestion; 3) Cypher development; and 4) finding similarity for recommendations. By leveraging the new Neo4j GDS, getting to cosine distances from user reviews will be covered. A graph-based solution to overcome the cold-start problem for beers without user reviews will also be presented. As an outcome, attendees of this presentation will be able to use Neo4j to generate recommendations using sparse data without the need for feature engineering and large-scale machine learning algorithms.

Will Evans

Will Evans

Vice President, Strategy and Innovation, Graphable

As the VP of Strategy and Innovation at Graphable, Will works with companies large and small to help them develop industry leading technologies based on graph. With a focus on event data, recommendations, and NLP, Will helps guide companies to success

Graph Data Science

3:45 - 4:30

Doom, Kafka, and Neo4j - Building a (Near) Real-time Telemetry Graph

Video games like Doom, in addition to being nostalgic fun, let us experiment with ways to tackle real world challenges faced by developers trying to harness the power of connected data including common challenges encountered by developers of IoT systems.

Using a Doom fork designed to emit telemetry as the user plays, we'll explore three methods of wrangling telemetry streams into a graph:

  • a simple Java Reactor-Netty UDP stream processor,
  • a more robust Kafka approach
  • and a cloud-native approach in Google Cloud Platform

All code will be provided, so you'll be able to build, run, and experiment with your own graph building while replaying the classic 90's hit PC game.

Dave Voutila

Dave Voutila

Sales Engineer, Neo4j

A flatlander now living in Vermont (USA), Dave's been a part of Neo4j's field team since 2019. He's proudly never seen "Jerry Maguire" and intends to keep it that way.

Graph Data Science

4:35 - 5:20

From Local Strategies to Global Patterns

Interesting global patterns like scale-free networks, small world phenomena, and viral transmission of innovations turn up in graphs all the time. How do they get there? Independent, rational actions of network members at local scale can lead to these global patterns. Game theory and graph theory explain how. Neo4j gives us tools to make the theories understandable and practical.

Nathan Smith

Nathan Smith

Senior Data Scientist, Lovevery

Nathan Smith is a senior data scientist at Lovevery. He uses Neo4j to help provide Lovevery subscribers developmentally appropriate toys and activities for the infants and toddlers in their lives. He is active in the Neo4j community as the organizer of the Kansas City Graph Databases Meetup, and he frequently contributes to the Neo4j developer blog. Nathan has over a decade of experience in business intelligence and data science. He also holds a doctorate in piano performance from the University of Missouri-Kansas City.

Building Applications

9:05 - 9:50

Project Hades | Event Management to the Masses

Project Hades, built by Developer Student Clubs VIT is a free and open source software aimed at bringing on site event management to the masses. This is about how we used Neo4j and graph technologies to build and scale and application for the smooth operation of our events.

We used Neo4j to power a Women Empowerment Hackathon with over 300 participants on-site as well as multiple events with an aggregated dataset of over 20000+ points.

Angad Sharma

Angad Sharma

Backend Developer | Technical Blogger | DevOps Enthusiast

Angad Sharma is a server side developer, solutions architect, DevOps SysAdmin, Course Instructor and an avid blogger. Angad previously worked at Atlan as a Backend Developer intern in Golang, Cloudify Technologies as Backend Developer intern in node.js, FindMind Analytics as DevOps intern and GryNow as Backend intern in node.js and has published courses on Udemy and Winuall.

Angad is a tech Lead at CodeChef-VIT chapter, Community Lead (2020-21) at DSC VIT powered by Google Developers, Campus Ambassador at Hackerearth and RCPL, Core Committee Member at Facebook Developer Circles Vellore and is currently in love with Golang and Domain Driven Design.

Building Applications

9:55 - 10:40

Transforming a REST Service to a Graph Service

Discover how you can transform a REST API to a graph API with Neo4j and GraphQL.

Lennert Van Sever

Lennert Van Sever

Full Stack Developer, Borealis Digital Studio

Lennert Van is working as a full-stack developer for the Borealis Digital Studio and is experienced with React, GraphQL and Apollo, so using the GRANDstack feels very natural to Lennert. Currently, Lennert based in Brussels, Belgium.

Building Applications

10:45 - 11:05

From (:RDBMS)-[:TO]->(:Neo4j) Tips and Tricks

All journeys start somewhere! When getting acquainted with Neo4j one of the very first tasks is to migrate your existing data from a traditional relational DBs such as Postgres or MySQL to Neo4j. Very few companies start out on Neo4j from day one, and we were no exception! This talk will cover some tips and tricks we learned the hard way as we began our own journey with Neo4j.

Mike Blum

Mike Blum

Software Engineer, LogicGate

Mike Blum does operations and development on Neo4j at LogicGate – a platform for building compliance and ERM workflows for enterprises.

Building Applications

11:10 - 11:30

Post-Union Processing with Cypher

Continuing the processing of a Cypher query on the results of a UNION was a heavily requested feature. This session shows how it's used.

Luanne Misquitta

Luanne Misquitta

Vice President of Engineering, GraphAware

Luanne Misquitta is VP of Engineering at GraphAware, the World's #1 Neo4j solutions and consultancy and has been (happily) working with Neo4j for over 10 years. She was a core committer to Neo4j OGM and SDN 4, has spoken at GraphConnect in Europe and the US. She currently enjoys consulting at GraphAware's key clients on graph modelling, Cypher, best practises and the application of graphs in various domains.

Building Applications

11:35 - 12:20

IOT Data Compression in Neo4j

Imagine you have a week to put together a POC, or perhaps a weekend hack-a-thon. Your project requires that you capture streaming IOT data from multiple devices. The streams must be combined meaningfully real-time before being stored. Of course the event data is arriving out of sync at volumes so high that you should compress the data upon arrival. You don't want a lot of moving parts in your solution: Kafka, Spark, InfluxDB, lots of glue scripts... No, let's keep it simple. Let Neo4j do it all! The bonus, we won't need to do any further ETL work before we analyse the data in Neo4j, it's already there!

This presentation will show how Neo4j can be coerced into being 'reactive' to incoming IOT data stream event patterns, to intelligently coordinate the inflow of data. It's half inspired by a community post by David Allen: https://tinyurl.com/dallen-iot.; The remaining inspiration comes from Apache Flink's powerful 'Stateful Stream Processing' concept. We will combine these ideas and apply a time-series compression algorithm via an APOC stored procedure written entirely in CYPHER.

Joe Chesak

Joe Chesak

Senior Managing Consultant, Capgemini Norway Chief Data Officer, Bolder AS CEO, FabLabs AS

Joe Chesak is an American living in Norway. He discovered Neo4j in 2011 and it changed his life. Joe love this community, and is growing it in Western Norway.

Building Applications

12:25 - 1:10

Which Comes First - The Data Model or the Algorithm?

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?

Liz Maida

Liz Maida

Founder and Chief Executive Officer, Uplevel

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 efforts in DDoS mitigation, fraud detection, and mobile authentication, as well as security products including Akamai’s cloud-based web application firewall and an analytical engine that leveraged Akamai’s visibility into almost 30% of Internet traffic to assess the security risk of end user requests. Liz holds a Bachelor of Science in Engineering degree from Princeton University and dual Masters degrees in Computer Science and Engineering Systems from MIT. Her graduate school research examined the application of graph theory to network interconnection.

Building Applications

1:15 - 2:00

Low-Code Development with Graphs - Taking Application Development to a New Level

To define the components of a software application, we use graphs as abstract models for the data scheme, program and process flows, web component trees, HTML and CSS, as well as file system and organization hierarchies. Why not put them into a graph database and create dynamic web applications directly from these definitions?

That's the idea behind Structr, the unique low code platform that uses the capabilities of Neo4j to take application development to a whole new level.

In his presentation, Axel Morgner, co-founder and CEO of Structr, will explain and demonstrate why building software applications is much more efficient by simplifying and accelerating the development and operation on many levels by skillfully taking advantage of the benefits of graph technology.

Axel Morgner

Axel Morgner

Founder and CEO, Structr

Axel is a physicist, co-founder and managing director of Structr GmbH in Frankfurt am Main, Germany. As the initiator of the Structr project, his vision is to make software development easier using graph technology and to make it accessible to people without special knowledge.

Building Applications

2:05 - 2:50

Quarkus on Neo4J: Real-World Experiences & Live-Coding

If you're in the enterprise Java world, you've probably heard “supersonic subatomic Java” with Quarkus, a new runtime for modern Java applications. With Quarkus, cloud-native Java has arrived in year 2020, with excellent support for containerized environments, and a highly-effective developer experience.

In this session, I'll live code a modern, cloud-native application that is powered by Quarkus and that uses Neo4j at the core of its business domain. We'll see how to map the graph domain model into our Java code, and what to take into account when using Neo4j on enterprise Java. We’ll further see how to run our applications and graph databases in managed Kubernetes environments and what to consider for production workloads. The examples are based on real-world experience of running Quarkus with Neo4j.

Sebastian Daschner

Sebastian Daschner

Java Champion | IBMer | Author

Sebastian Daschner is a Java Developer Advocate at IBM, a consultant, author, and trainer. He is the author of the book ‘Architecting Modern Java EE Applications’. Sebastian is participating in open source standardization processes such as the JCP or the Eclipse Foundation, helping forming the future standards of Enterprise Java, and collaborating on various open source projects.

For his contributions in the Java community and ecosystem he was recognized as a Java Champion, Oracle Developer Champion, and JavaOne Rockstar. Besides Java, Sebastian is a heavy user of cloud native technologies and anything related to enterprise software. He evangelizes computer science practices on https://blog.sebastian-daschner.com, his newsletter, and on Twitter via @DaschnerS. Sebastian kickstarted the JOnsen and jSpirit unconferences that connect Java developers throughout the globe. When not working with technology, he also loves to travel the world.

Building Applications

2:55 - 3:40

Lightning Fast Zero to Production with Spring, Neo4j and jHipster

JHipster is a development platform to generate and deploy Spring Boot applications with different modules and configurations. Neo4j support has been recently added to the JHipster ecosystem and in this talk we'll introduce you to this amazing platform and its tools. We will also do a live coding session to help us understand how fast it is to get production ready code with JHipster and Neo4j.

Giuseppe Villani

Giuseppe Villani

SW Developer, LARUS Business Automation

Giuseppe Villani started his journey into the world of computer science 5 years ago with a 3-year course in Web Design and Development. Recently, he focused on Java and Javascript technologies.

Davide Fantuzzi

Davide Fantuzzi

Data Engineer, LARUS Business Automation

Davide Fantuzzi is a Data Engineer at LARUS Business Automation.

Building Applications

3:45 - 4:30

Developing Applications with Neo4j Aura on Google Cloud

Developers and Data Engineers are turning to the power, agility, reliability, and security of Google Cloud to build applications that meet the challenges faced by today’s businesses. In this session, Google Cloud Developer Advocate Robert Kubis, will demonstrate the many ways Google Cloud supports developers and data engineers in building and enriching their applications while keeping productivity, and application + data security at top of mind. David Allen, Technology Partner Architect for Neo4j will then give you a live demo putting these fundamentals into practice, and show how to easily and quickly build and connect compelling Cloud applications with Aura, the only fully managed graph database-as-a-service on Google Cloud.

Robert Kubis

Robert Kubis

Cloud Developer Advocate, Google

Robert Kubis is a Developer Advocate for Google Cloud based in Colorado, US. He specialises in container, storage and scalable technologies. Robert is a frequent speaker and keynote speaker on technology and best practices in cloud application development. Before joining Google, he collected over ten years of experience in software development and architecture, driving multiple full-stack application projects

M. David Allen

M. David Allen

Technology Partner Architect, Neo4j

David is a deeply technical generalist with experience in managing teams and driving towards complex goals. The most fun he has had in his career is when he is learning something new, or trying to figure out how to do something that hasn't been done before.

When not trying to improve something technical, you can usually find David playing guitar or cycling. He loves meeting new people, and has a very keen interest in language and culture and loves to find common ground with other people through travel and music.

Building Applications

4:35 - 5:20

Enterprise 360 - Graphs at the Center of the Data Fabric

Providing multi-dimensional views across the enterprise requires weaving a complex network of connected data and metadata together to deliver context, insight and strategic focus. Industry analysts are increasingly finding that graph databases are a key enabling technology in supporting Data Fabric architectures, where detailed data catalogs and semantic definitions are deployed on top of graph data models to provide true self-service and democratization of data, contextualizing information across domains. Join Precisely as we illustrate how we help our customers use graph to implement Data Fabric patterns in support of their key strategic objectives.

Aaron Wallace

Aaron Wallace

Product Manager, Precisely Context Graph

Aaron Wallace is a Global Product Manager for Data Context at Precisely. He is based in Austin, Texas and has been with Precisely for 15 years. Aaron has 25 years of experience in the enterprise software space with an extensive background in designing and building data management solutions across a number of industry verticals.

Deep Dive

9:05 - 9:50

A Humane Presentation about Graph Database Internals

Databases are everywhere, but did you ever wonder what goes on inside the box? In this talk we’ll dive into the internals of Neo4j - a popular graph database - and see how its designers deal with common functional and non-functional requirements. In particular we’ll see how data is stored safely and queried performantly by understanding the way Neo4j makes use of the network and file system. So if you’re a curious person looking for a humane and light-hearted introduction to database internals and distributed systems, this talk is for you!

Jim Webber

Jim Webber

Chief Scientist, Neo4j

Dr. Jim Webber is Neo4j’s Chief Scientist and Visiting Professor at Newcastle University. At Neo4j, Jim works on fault-tolerant graph databases and co-wrote O’Reilly’s Graph Databases book.

Prior to Neo4j, Jim worked on fault tolerant distributed systems. First at Newcastle University startup Arjuna and then for a variety of clients for global consulting firm ThoughtWorks. Along the way Jim previously co-authored the books REST in Practice and Developing Enterprise Web Services - An Architect’s Guide.

Jim is active in the software development and database research communities, presenting regularly around the world. His blog is located at https://jimwebber.org/blog and he tweets sometimes at @jimwebber.

Deep Dive

9:55 - 10:40

Site Reliability Engineering at Neo4j Aura

The Neo4j-as-a-service offering Aura makes it very easy for customers to get access to a production grade Neo4j database without worrying about all the operational bits and pieces.

However, we really care about those things and want to take you on a journey what it means to keep the lights on.

Johannes Unterstein

Johannes Unterstein

Graphs, Containers and Fun, Neo4j

Johannes (@unterstein) organizes the java user group in his home town Kassel, teaches java at the DHBW Stuttgart and works as software engineer at Neo4j. He spent the last few years building distributed and containerized systems with focus on orchestration frameworks. Currently he is working on the managed Neo4j cloud product to enable users to enjoy graphs without worrying about their operations.

Julien Grobbelaar

Julien Grobbelaar

Site Reliability Engineer, Neo4j Aura

Julien studied engineering at Stellenbosch in the Western Cape in South Africa, where he also managed to inspect several vineyards during his residency there. After university, he moved to London and spent ten years at Cisco Systems working on and implementing a range of networking protocols and security features which powers today's internet. For the last three years he has been busy building Neo4j Aura and heads up the Site Reliability Team to ensure the lights always stay on.

Deep Dive

10:45 - 11:30

Cypher Deep Dive: From Query String to Result

This session aims to provide a general overview of the internal workings of Neo4j Cypher. We will go into detail about the different processing steps, starting from a string representation of a query and ending up at the iterators, that can be used to fetch the results from.

Sascha Peukert

Sascha Peukert

Software Engineer, Neo4j

Sascha joined Neo4j as a software developer in 2017. Previously he studied computer science at TU Dresden and wrote his master thesis about developing graph views on Neo4j. Together with his colleagues in the Cypher team, he works on implementing and optimizing new features in Neo4j Cypher and security.

Deep Dive

11:35 - 12:20

Tips and Tricks for Running Neo4j Clusters with Large Stores

Operating distributed systems with large amounts of data can often be challenging; especially if you care about performance and stability.

Neo4j Causal Clusters are no exception. Historically, once a user's store gets above a certain size, careful tuning and management are required to maintain a good experience.

Worse, given the many configuration options Neo4j has to offer, and the wide variety of deployment hardware, some solutions you try might not work, or have unintended consequences.

In this session a member of Neo4j's clustering team and another from field engineering would like to set the record straight.

We throw a lot of data at Neo4j, then show which changes to config or environment *work* to preserve performance and stability, along with those which ... don't.

Hugo Firth

Hugo Firth

Cluster Wrangler, Neo4j

Hugo Firth is a distributed systems enthusiast, clustering engineer at Neo4j and erstwhile graph data researcher, and was likely voted most popular conversationalist at parties (everyone is quarantined so you can't prove otherwise).

Tom Geudens

Tom Geudens

Field Team Member (not engineer, I don't build bridges), Neo4j

At the age of 15, Tom Geudens' parents gave him a choice. Either become a baker or go into IT. That Christmas Santa brought a MSX homecomputer, the choice was made and the rest, as they say, is history ...

Deep Dive

12:25 - 1:10

Building Spatial Search Algorithms for Neo4j

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 within Neo4j.

Craig Taverner

Craig Taverner

Senior Software Engineer, Neo4j

Craig is a Senior Software Engineer at Neo4j. 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 Product Engineering team to work full time on Cypher and Spatial in Neo4j.

Deep Dive

1:15 - 2:00

Low Code GraphQL APIs With Neo4j

Learn how to build efficient GraphQL APIs without writing boilerplate code and duplicating schemas in your API and database.

William Lyon

William Lyon

Developer Relations Engineer, Neo4j

William Lyon is a software developer at Neo4j and author of the book "Fullstack GraphQL Applications With GRANDstack" published by Manning. You can find him on Twitter at @lyonwj.

Deep Dive

2:05 - 2:50

Create a Graph Data Pipeline with Apache Spark and Neo4j

Regardless of the fact you're a Data Scientist or a Data Engineer, at 99%, you have met in some way with Apache Spark. In this session, we’ll show you the capabilities we’ve been working on discovering how you can combine Spark and Neo4j for reading and ingesting data from/to Neo4j leveraging several Programming Languages (such as Scala and Python) via Notebooks; showing some simple use case that will allow you building a Graph Data Pipeline from scratch.

Andrea Santurbano

Andrea Santurbano

CTO, LARUS Business Automation

Andrea Santurbano is CTO at LARUS Business Automation (the integrator leader for Neo4j) with seven years of experience developing high-performance, mission-critical systems, and large-scale data pipelines across multiple and heterogeneous systems. Andrea loves open source and contributed to several big data projects, such as Apache Zeppelin, Cypher for Apache Spark and others for the Neo4j ecosystem. As a developer, he’s very curious about discovering new technologies, and enjoys the continuous learning process that allows making things better every day.

M. David Allen

M. David Allen

Technology Partner Architect, Neo4j

David is a deeply technical generalist with experience in managing teams and driving towards complex goals. The most fun he has had in his career is when he is learning something new, or trying to figure out how to do something that hasn’t been done before.

When not trying to improve something technical, you can usually find David playing guitar or cycling. He loves meeting new people, and has a very keen interest in language and culture and loves to find common ground with other people through travel and music.

Deep Dive

2:55 - 3:15

Relate Framework - How Neo4j Builds Neo4j Developer Tools

Neo4j Desktop is written in TypeScript and built on Nest.js. That's the foundation of our Relate Framework for building a growing collection of Developer Tools. And you can use it, too.

Andreas Kollegger

Andreas Kollegger

Director of PM for Developer Tools, Neo4j

Andreas Kollegger has been working to bring graph thinking into the world for 10 years at Neo4j.

Deep Dive

3:20 - 3:40

Sharding Graphs with Neo4j

Graphs and sharding, do they really work together? In this talk, we will present the latest additions to Cypher and how we can execute queries spanning in parallel on multiple graphs, in a sharding and federation scenario. We will also look at modelling techniques that we can use to split graphs, improve scalability and speed up queries.

Ivan Zoratti

Ivan Zoratti

Director of Product Management, Neo4j

Ivan leads the Product Management team for the Neo4j Database. He started his career at Digital Equipment Corporation, following his passion for the kernel of the PdP 11 machines and later with VAX/VMS Macro programming and networking. An author, an entrepreneur, he was founder of Dianomic Systems (an IoT startup), CTO at SkySQL (now MariaDB) and led Systems and Sales Engineering teams at MySQL, Sun and Oracle.

Deep Dive

3:45 - 4:30

Cypher Workbench: Development Tool Suite for Cypher

The path from inception to deployment of your Neo4j solution involves many steps: documenting key questions, modeling your data, importing data, building queries, and verifying the data works for your needs. Additionally, running systems have a different set of needs: understanding query execution, troubleshooting, and analyzing the health of your database. Cypher Workbench is a collection of visual tools that can assist in the development of new Neo4j solutions as well as help maintain existing deployments. This talk will showcase several tools within Cypher Workbench that may help you with your Neo4j solution.

Eric Monk

Eric Monk

Principal Solutions Engineer, Neo4j

Eric Monk is a Neo4j Principal Solutions Engineer and has been using Neo4j for nearly six years, helping customers implement Neo4j within their enterprise. Eric has a rich background in many aspects of data, including enterprise data integration, data wrangling, data analysis, and data visualization. Eric is based in the greater Washington, DC metro area.

Deep Dive

4:35 - 5:20

Parallel Data Import Into a Running Database

Importing relationships into a running graph database using multiple threads running concurrently is a difficult task, as multiple threads cannot write information to the same node at the same time. Here we present an algorithm in which relationships are sorted into bins, then imported such that no two threads ever access the same node concurrently. When this algorithm was implemented as a procedure to run on the Neo4j graph database, it reduced the time to import relationships by up to 69% when 32 threads were used.

Aleks Ontman

Aleks Ontman

Manager, Deloitte

Aleks Y. M. Ontman received his B.S. in electrical engineering from SUNY Binghamton in 2005 and his Ph.D. in Materials Science and Engineering from the University of Virginia in 2012. He is currently a manager in Strategy & Analytics practice with Deloitte Consulting LLP, specializing in application of Natural Language Processing and graph theory to government and commercial clients.

Joshua R. Porter

Joshua R. Porter

Senior Consultant, Mission Analytics, Deloitte

Joshua R. Porter received his B.S. in electrical and computer engineering from Lafayette College in 2006 and his Ph.D. in electrical and computer engineering from Johns Hopkins University in 2012. From 2012-2017, he did postdoctoral work in the National Cancer Institute, researching the p53 tumor suppressor protein and using mathematical models to understand its function. He is currently a Senior Consultant with Deloitte Consulting LLP, using graph databases to organize data and yield valuable insights.