Confirmed Graph Sessions

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We are now just beginning to announce the sessions. Please check back for updates. If you would like to speak at the Global Graph Summit, visit our proposals page for details.

Knowledge Graph Keynote
A Brief History of Knowledge Graph's Main Ideas

Juan Sequeda -

Knowledge Graphs can be considered to be fulfilling an early vision in Computer Science of creating intelligent systems that integrate knowledge and data at large scale. The term “Knowledge Graph” has rapidly gained popularity in academia and industry since Google popularized it in 2012. It is paramount to note that, regardless of the discussions on, and definitions of the term “Knowledge Graph”, it stems from scientific advancements in diverse research areas such as Semantic Web, Databases, Knowledge Representation and Reasoning, NLP, Machine Learning, among others.
The integration of ideas and techniques from such disparate disciplines give the richness to the notion of Knowledge Graph, but at the same time presents a challenge to practitioners and researchers to know how current advances develop from, and are rooted in, early techniques.
In this talk, Juan will provide a historical context on the roots of Knowledge Graphs grounded in the advancements of the computer science disciplines of Knowledge, Data and the combination thereof, starting from the 1950s.

90 min Workshop
Graph Feature Engineering for More Accurate Machine Learning

Amy Hodler / Justin Fine - Neo4j

Graph enhanced AI and ML are changing the landscape of intelligent applications. In this workshop, we’ll focus on using graph feature engineering to improve the accuracy, precision, and recall of machine learning models. You’ll learn how graph algorithms can provide more predictive features. We’ll illustrate a link prediction workflow using Spark and Neo4j to predict collaboration and discuss how you avoid missteps and tips to get measurable improvements.

Modeling, Querying, and Seeing Time Series Data within a Self-Organizing Mesh Network

Denise Gosnell - DataStax

Self-organizing networks rely on sensor communication and a centralized mechanism, like a cell tower, for transmitting the network's status.
So, what happens if the tower goes down? And, how does a graph data structure get involved in the network's healing process?
In this session, Dr. Gosnell will show you how we see graphs in this dynamic network and how path information helps sensors come back online. She will walk through the data, model, and Gremlin queries which help a power company have real-time visibility into different failure scenarios.

Building a Graph User-Interface for Malware-Analysis

Stefan Hausotte - G DATA / Ethan Hasson - Expero / Kristin Stone - Expero

As a security company, G DATA built a large JanusGraph database with information about different malware threats over the years. In this talk, the presenters will show how they built a web-interface to explore the data. This allows malware analysts to get insights about threats without the need to query the database manually and helps to get an understanding of the connections between malware through an appealing visualization. They will also discuss how they build a GraphQL API for the JanusGraph instance and how the user interface was built with open-source JavaScript libraries.

NuGraphStore: a Transactional Graph Store Backend for JanusGraph

Dr. Jun Li / Dr. Mohammad Roohitavaf / Dr. Gene Zhang - eBay

JanusGraph is a distributed graph database system with pluggable storage backend servers, such as Cassandra, HBase, or BerkeleyDB (which is non-scale-out). There were no fully transactional scale-out backends for JanusGraph. Without transaction support, there would be challenges for applications to deal with index/data inconsistency, and inconsistency related to vertices and edges, such as dangling edges, as well as data loss or data duplication. We have been developing a scale-out KCV storage engine with distributed transaction support for JanusGraph, called NuGraphStore. In this talk, we will present the architecture and design of NuGraphStore, its storage engine and distributed transaction mechanisms. NuGraphStore is (going to be) open-sourced under Apache 2.0 license. We invite interested developers and users to join the community to make NuGraphStore the best backend storage engine for JanusGraph. Its distributed transaction protocol could be adapted for use with other KV store engines as well.

Responsible AI Requires Context and Connections

Amy Hodler - Neo4j

As creators and users of artificial intelligence (AI), we have a duty to guide the development and application of AI in ways that fit our social values, in particular, to increase accountability, fairness and public trust. AI systems require context and connections to have more responsible outcomes and make decisions similar to the way humans do.
AI today is effective for specific, well-defined tasks but struggles with ambiguity which can lead to subpar or even disastrous results. Humans deal with ambiguities by using context to figure out what’s important in a situation and then also extend that learning to understanding new situations. In this talk, Amy Hodler will cover how artificial intelligence (AI) can be more situationally appropriate and “learn” in a way that leverages adjacency to understand and refine outputs, using peripheral information and connections.
Graph technologies are a state-of-the-art, purpose-built method for adding and leveraging context from data and are increasingly integrated with machine learning and artificial intelligence solutions in order to add contextual information. For any machine learning or AI application, data quality – and not just quantity – is critical. Graphs also serve as a source of truth for AI-related data and components for greater reliability. Amy will discuss how graphs can add essential context to guide more responsible AI that is more robust, reliable, and trustworthy.

GQL: Get Ready for a Standard Graph Query Language

Stefan Plantikow - Neo4j

A new standard query language is coming. For the first time in decades, the ISO Standards Committee has initiated work on a new language, a graph query language (GQL). With the backing of experts from seven nations and major database vendors, an early draft of this query language for property graphs is now ready.
Attend this session to learn about the initial features for GQL (ISO/IEC 39075), as well as ongoing efforts by the community and in the official standardization bodies. Get early information on capabilities such as the generalized property graph data model, advanced pattern matching, graph schema, parameterized graph views, query composition, and the advanced type system. As GQL is a sibling to SQL, we’ll also discuss how it aligns with shared features from the upcoming edition of SQL.
This talk will help you get ready for GQL, an unprecedented development in the graph landscape, with tips on planning for future transitions. You’ll also get guidance on how to engage with the GQL community and how to keep up to date with the official standardization process.

Creating Explainable AI with Rules

Jans Aasman - Franz. Inc

This talk is based on Jans' recent article for Forbes magazine.
"There’s a fascinating dichotomy in artificial intelligence between statistics and rules, machine learning and expert systems. Newcomers to artificial intelligence (AI) regard machine learning as innately superior to brittle rules-based systems, while the history of this field reveals both rules and probabilistic learning are integral components of AI.
This fact is perhaps nowhere truer than in establishing explainable AI, which is central to the long-term business value of AI front-office use cases."
"The fundamental necessity for explainable AI spans regulatory compliance, fairness, transparency, ethics and lack of bias -- although this is not a complete list. For example, the effectiveness of counteracting financial crimes and increasing revenues from advanced machine learning predictions in financial services could be greatly enhanced by deploying more accurate deep learning models. But all of this would be arduous to explain to regulators. Translating those results into explainable rules is the basis for more widespread AI deployments producing a more meaningful impact on society."

Automated Encoding of Knowledge from Unstructured Natural Language Text into a Graph Database

Chris Davis - Lymba

Most contemporary data analysts are familiar with mapping knowledge onto tabular data models like spreadsheets or relational databases. However, these models are sometimes too broad to capture subtle relationships between granular concepts among different records. Graph databases provide this conceptual granularity, but they typically require that knowledge is curated and formatted by subject matter experts, which is extremely time- and labor-intensive. This presentation presents an approach to automate the conversion of natural language text into a structured RDF graph database.

Improving Real-Time Predictive Algorithms with Asynchronous Graph Augmentation

Dave Bechberger / Kelly Mondor - DataStax

Shop online, swipe a credit card, check-in on social media – predictive algorithms are watching all of this in real-time, analyzing the behaviors in order to find fraud, or tailor a news feed, or just suggest some other product to purchase.
Graphs are frequently helpful when working with these sorts of predictive algorithms as these use cases can benefit heavily from examining how data is connected. The difficulty lies in that the relevance of connections change over time and efficiently finding the connections that matter becomes exponentially harder as more and more data is added. Historically due to the length of time and amount of computation required this has been solved by running large batch process runs daily, weekly, or even less frequently to update the relevance of connections within a graph. However, in today's world, this is not always fast enough.
What we will show is a method for decoupling these complex analytical transactions from real-time transactions to improve these predictions in near real-time without performance degradation. We will discuss how this method can leverage algorithms such as graph analytics or machine learning to provide optimized graph connections leading to more accurate predictions. Wrapping up we will demonstrate how to apply this process to common use cases such as fraud or personalization to provide better real-time predictive results.

JGTSDB: A JanusGraph/TimescaleDB Mashup

Ted Wilmes - Expero

Time series data is ubiquitous, appearing in many use cases including finance, supply chain, and energy production. Consequently, “How should I model time series data in my graph database?” is one of the top questions folks have when first kicking the tires on a graph database like JanusGraph. Time series provides a number of challenges for a graph database both as it’s coming into the system and when it’s being read it out. High volume and velocity means you need to ingest tens to hundreds of thousands of points per second (or more!). Users expect to be able to perform low latency aggregations and more complicated analytics functions over this time series data. JanusGraph can meet the ingest requirements but it requires some very specific data modeling and tuning tricks that frequently are not worth the extra development complexity. Because of this, we’d usually recommend storing this data in an entirely different database that is more suited to time series workloads. For this talk, we will discuss an alternative approach where we integrate TimescaleDB access into JanusGraph itself, allowing users to write a single Gremlin query that transparently traverses their graph and time series data. This setup inherits the operational characteristics of Timescaledb while providing a single, unified and low latency query interface that does not require careful and specific graph data modeling and tuning.