Explore our thinking about Graph, Data, & AI

Data Products: What Product Managers Need to Know about Productizing Data Science

Energy Round Table with TigerGraph

Learn about fighting fraud with Graph and Machine Learning Experts

Learn more about the technology behind Data Products

Netflix open-sourced Metaflow for performing data science and machine learning on cloud providers such as Amazon Web Services (AWS), Microsoft Azure and Google Cloud (GCP) - although optimized for AWS. What features does it provide?

Getting your graph to scale is only half the battle. Learn here how Expero helps businesses understand their data and make real time decisions using custom visualizations and UX.

Advances in automation of data analysis, graph analytics, data lineage, conversational queries and IoT analytics are widely predicted for 2020. Expero has capabilities and assets already in place.

Deep dimensionality exchange is a new deep learning protocol developed at Expero for performing domain transfer between domains of different dimensions.

Natural language processing is a powerful tool in building investigatory user interfaces for data exploration. See how conversation UI can be used to interrogate highly connected data to produce context driven data visualization and analytics.

Technology for deploying scalable machine learning-based products otherwise known as data products.

Build a single view of your corporate security model using new capabilities in graph and streaming technology through which you can funnel all your access attempts to critical data.

Building data products in your organization to realize data science ROI and never before seen data insights.

JanusGraph tuning and performance best practices

Data products are productized versions of data science and machine learning initiatives that deliver value to end-users.

Utilizing real time optimization with an event streaming platform.

With Confluent and TigerGraph quickly emerging as high-quality enterprise software, learn how you can take your LDAP data, RBACs, and ACLs and quickly model and mirror them in a graph database using Kafka, a real-time streaming software.

A native parallel graph enables manufacturers to identify and manage product delays, shipment status, and other quality control and risk issues, at massive scale in real-time.

Use Machine Learning to increase volume, maintain profits and meet the needs of customers while maintaining a simple business policy.

Given a large batch of healthcare data, we efficiently find similar patients to determine what remedies to recommend using traditional search methods and graph algorithms.

Join us for this quick deep dive on how machine learning and analytics can be used to get insights into your data & scheduling/managing fleets.

Predict, identify, and intervene using graph technology and Moove.ai Machine Learning Analytics.

See how compiled data sources allow for real-time mapping of road safety and current conditions.

Supply chain planning demo showing real-time visibility into demand and capacity planning.

Supply Chain planning and analytics dashboard showing risk visibility, real-time data and time series.

System Integration sourcing data for Graph Analysis.

Understand the differences between supply chain visibility, traceability, and transparency.

Customer 360 in financial services showing segmentation and churn avoidance, sentiment score, and best next conversation.

Automating multifaceted, complex workflows requires hybrid solutions like streaming analytics of IoT data, batch analytics like machine learning solutions, and real-time visualizations.

15 Minute Lightning Talks + Q&A. Powerful Data, Storage & Search & Graph Technology in 1 platform - Cassandra.

Learn more about JanusGraph and its capabilities in this online meetup.

Graph machine learning (graphML) is a subset of deep learning with much higher accuracy because big data records are linked together by their relationships.

Learn how Graph Technology can help to identify risk and fraud patterns in order to quickly respond to threats and anomalies. Many new fraud rings use sophisticated measures for credit card and other methods of fraud. Utilizing Graph and ML will allow you to see beyond individual data points and uncover difficult-to-detect patterns. Join us to learn how to maximize time and resources with Graph vs. traditional relational database platforms. Predict, Identify, and Intervene Fraud with Graph & ML Analytics. ‍

This demo explores Portfolio Optimization for Alternative Investments

#NLP #MachineLearning #algorithm learns to tell #stories by summarizing #commercial #RealEstate #data, #earning #profits and spurring #CustomerRetention. #BINGO!

Join Scylla and Expero to learn how to use JanusGraph and Scylla to architect a corporate compliance platform.

See why accumulators help TigerGraph’s GSQL query language standout among other native Graph Databases.

Learn how Graph & Machine Learning Technology will increase customer loyalty, identify and resolve customer issues and provide strategic up-selling capabilities. Expero will demonstrate how visualizing customer journeys can provide your users with meaningful interactions and increase overall customer satisfaction. Join us to learn how to maximize resources with Graph and finally get an all encompassing view of your customer.

Reinforcement Learning at scale; Scheduling thousands of vehicles in multiple environments - how we made it work.

Cut Costs & Manage Entitlements with Graph Technology & ML Analytics.

This demo explores Entitlements & ID Management

Cut Costs & Optimize Revenue with Supply Chain Technology.

This can be like the one sentence description, but the more buzzwords the better. This is what google will pull from when people search.

Watch our online seminar to learn how to unlock patient 360 & prevent opioid fraud with graph & ML technology. We discuss how graph-based Patient 360 breaks silos and enables analytics to discover more efficient operations, better healthcare outcomes, and fraud detection.

Trading Alternative Investments using Connected Data & Machine Learning

Understand the differences between supply chain visibility, traceability, and transparency. Learn what graph is and how it can create a unified view across digital and physical supply chain processes, yielding more prescriptive supply chain decision making. Use Graph + ML to improve demand forecasting and production planning, while reducing inventory and operating costs. Focus areas include, inventory, routing, predictive analytics, device & fleet management, materials, and more.

15 Minute Lightning Talks + Q&A

Expressive Data Models with JanusGraph - High Performance JanusGraph Data Load

Building a data ingestion pipeline using Spark, Kafka, DataStax, Nifi, and Pentaho.

Deep learning, specifically recurrent neural networks, forecast nonlinear time series illness signals.

Have you ever stopped to understand what graph databases are and what they can do for you? Graph databases and graph processing frameworks are all the hype in the NoSQL world at the moment. The ecosystem is constantly evolving and different datastores of processing frameworks are coming out what seems like weekly. The truth is that graph databases are a great way to solve certain application problems in areas such as personalization and recommendation, logistics, master data management, social networks, fraud or IoT but many people are completely lost when they step foot into the exosystem. In this session we will help you make sense of the graph ecosystem with an examination of a variety of graph datastores (e.g. Neo4j, DSE Graph, Titan, OrientDB, etc.) and graph processing frameworks (e.g. Giraph, GraphX, Elasticsearch Graph, GraphQL, Pregel, etc.). We will then discussing how you might use these technologies to augment or replace complex portions of your applications. In the end you will walk away with a better appreciation for the practical aspects of the graph ecosystem and you might even find out how to remove that complex recursive SQL CTE that gives you nightmares.

Using unsupervised machine learning, we relate documents together based on their content. This analysis operates directly on the text in the documents, building up clusters of similar context so that an analyst, or another algorithm, can investigate all of their company's data on a single subject.

Use graph and machine learning to optimize routing, inventory, predictive analytics, and fleet management of your supply chain while cutting costs and making your dollar go further.

As of late Q2 of 2018, there’s a new entry into the graph database marketplace, Amazon Neptune.

Using deep machine learning recommender system technology, Expero detects health care opioid fraud.

Patient 360 Application with claims, care plans & customer journey mapping.

This demo explores fraud prevention in healthcare applications

Learn how Google Cloud Bigtable, Pub/Sub, and BigQuery with JanusGraph can help to identify risk and fraud patterns in order to quickly respond. Many new fraud rings use sophisticated measures for credit card and other methods of fraud. Utilizing Google Cloud Platform products and services with JanusGraph will allow you to see beyond individual data points and uncover difficult-to-detect patterns. Join us to learn how to maximize time and resources with Graph.

Learn how Google Cloud Bigtable, Pub/Sub, and BigQuery with JanusGraph will increase customer loyalty, identify and resolve customer issues and provide strategic up-selling capabilities. Utilizing Google Cloud Platform products and services with JanusGraph will allow you to visualize the customer journey, increasing customer satisfaction by providing meaningful interactions. Watch to learn how to maximize resources with Graph and finally get an all encompassing view of your customer.

Customer 360, Customer Journey, Product & Recommendations.

This video is about Healthcare: Opioid Fraud Detection & Avoidance.

A sneak peak of this year's Graph Day San Francisco talk on ACID & integrating JanusGraph with FoundationDB.

Example of how financial institutions can use an all encompassing view of their customer to cross-sell and up-sell.

Take a look at how to optimize your telecom. Visualize points of connectivity, and future connections to be made. Designed to accommodate a back office and on site workers.

Get more insight into your resources. Estimate ahead of time certain needs and gaps in your production line.

The graph database space is rapidly expanding as more and more companies identify potential use cases that require the traversal of highly connected network and hierarchical data sets in ways that are cumbersome with RDBMSs and NoSQL solutions.

Dashboard showing details regarding infections resistant to antibiotics found in hospitals geographically.

Graph, Enterprise search, and enterprise analytics for financial services. Specific topics include customer 360, best next conversation, cross-sell and up-sell for product recommendation, and churn avoidance.

Transaction investigation and treasure mapping with KPIs using graph technology, high performance search, and high performance analytics.

Data Management for Oil and Gas - Predictive Maintenance Intelligence

Data Management for Oil and Gas using IOT and Machine Learning for Asset Management

Dashboard screens from an application showing detection of weather anomalies and other interferences in supply-chain. Includes user friendly abilities to reroute shipments, allowing companies to save money by limiting lost or late shipments and and changing plans at a moments notice.

Case Study

Use Graph Technology and Machine Learning to finally get a usable all encompassing view of your customers to stop churn and drive revenue.

Use Machine Learning and Graph to Stop Fraud Before it Happens

Don't be in the dark when the next natural disaster happens. Visualize your data with real-time supply-chain mapping powered by Graph Technology.

Follow these tips to speed up your JanusGraph queries when running against a variety of storage backends including Apache Cassandra, ScyllaDB, Apache HBase, and Google Cloud Bigtable.

Use Keylines to increase customer loyalty from within your graph database.

Don't be in the dark when the next natural disaster happens. Visualize your data with real-time supply-chain mapping powered by Graph Technology.

Reinforcement Learning of a deep neural network has been applied to the problem of supply chain logistics: In a stochastic environment, how to optimize pickup and delivery schedules.

Lock Down Funding for Intelligent, Graph-Enhanced Fraud Solutions

Graph machine learning finds dissatisfied customer cohorts an recommends optimal intervention measures.

So many options in fast moving the graph database world, which one should you choose?

So many options in fast moving the graph database world, which one should you choose?

Use case modeling timeline and possible future events that could interfere with supply chain. Allows user to see all options for rerouting and the implications of each decision.

Dashboard application showing relevance, search, and Graph. Created for an analyst (or similar type of user) in real estate. Buying and selling with suggestions and matches.

Use Machine Learning and Graph to Stop Fraud Before it Starts

Money laundering and credit card fraud detection by graph machine learning.

Thwart Fraud Using Graph and AI

Dashboard application showing filtering and connections in real estate using Graph Technology.

The property graph database space has been dominated by a handful of names who on balance are not that big in the software marketplace generally speaking.

Graph convolutional networks exhibit optimal deep learning on big graph data to gain business insight.

A method of classifying nodes in an information network by application of a non-Euclidean convolutional neural network

Ted Wilmes discusses the state of JanusGraph in the year 2018. Throughout the session he touches on where we are in the world of graph, what is new, and where JanusGraph and Apache Tinkerpop are headed.

Identify ROI and business case costs at a high level. Increase delivery with real-time impact and transport analysis.Learn how Graph Technology + Machine Learning will improve delivery times, minimize transport risk through data analysis and increase product satisfaction. Use predictive analytics to reduce operational bottle necks and discover areas of concern affecting supply. Using Graph + ML allows you to increase customer satisfaction through visualization of supply and demand impacts.

Machine learning entity resolution deduplication of FBI criminal records using supervised learning logistic regression and unsupervised learning clustering.

Dashboard showing treasure mapping, social influence, and other aspects of customers. Ideal application for call centers and customer service reps allowing them to see the entire customer journey - reducing churn and meeting needs more efficiently.

Learn how DataStax Graph Technology will improve delivery times, minimize transport risk through data analysis and increase product satisfaction.

Learn how DataStax Graph Technology can help to identify risk and fraud patterns in order to quickly respond. Many new fraud rings use sophisticated measures for credit card and other methods of fraud. Utilizing DataStax Graph Technology will allow you to see beyond individual data points and uncover difficult-to-detect patterns. Join us to learn how to maximize time and resources with Graph Technology vs. traditional relational database platforms. If there are new ways to commit fraud these days, shouldn't your company have new ways to prevent it? What You'll Learn Detection and anomaly identification - discuss ROI and time: the value of early detection.Progressive Disclosure - The ability to drill in as items are presented as requiring interaction. Create the ability to allow large data viewing without overwhelming users with millions of data points.Scoring and use of algorithms - The ability to score and create usable information from multiple sources. Creation of scorecards and risk calculations for indicating to users what to spend time on and look at more closely.Visual representation of risks, anomalies, hot spots and heat maps and need for human interaction. Creation of easy to identify visual screens and dashboards for key decision makers that allows for real time diagnostics.

This demo shows a multitouch application that displays a 3D space for a Nuclear Power Plant. This application allows for a timeline as well as details for different areas in the plant.

Learn how DataStax Graph Technology will increase customer loyalty, identify and resolve customer issues and provide strategic up-selling capabilities. Utilizing DataStax Graph Technology will allow you to visualize the customer journey increasing customer satisfaction by providing meaningful interactions. Join us to learn how to maximize resources with Graph Technology and finally get an all encompassing view of your customer.