Explore our thinking

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.

Globalization (G11n) of an application involves more than just translating text. Internationalization (I18n) is the process of enabling your application to be used in different languages and culture. Localization (L10n) covers the work to provide the application in one specific language and culture. Selected locales can help in providing translated text, but some information needs to be converted (times, dates, currencies).

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.

When localizing an application, treat the capabilities as features. Consider the specific use cases and work with the users to refine the approach. There may be design and layout adjustments needed per language. If the application is a CMS, content as well as application resources may need translations.

Learn how to avoid bugs and gain confidence when refactoring code by writing tests for your React code using Jest, React Testing Library and a Test Driven Development approach.

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.

Learn how to protect your resources by setting up serverless OAuth authentication with Auth0 and AWS Lambda@Edge.

Cassandra, DataStax, ScyllaDB, CosmosDB, RedShift - they all scale horizontally and with you will pay more as you add nodes - even if the SW is free, the compute time is not. Use Gatling to forecast your budget needs so you don’t surprise your CFO.

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.

Structured Finance investors often miss opportunities to maximize portfolios due to a lack of state of the art tools and processes.

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.

Wireframes are intended to call out key moments and interactions in software design in order to provide clarity into how something should look, feel, and function.

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.

Accelerating IoT, Finserv, and Supply Chains with JanusGraph and Cloud Bigtable

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.

Angular, React, Ember, Vue… There are a lot of javascript frameworks out there. Discover why an Angular programmer would use Vue over Angular any day of the week.

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.

Neo4j, DataStax Graph and Janus property graph schema design decisions for vertex and edge definitions.

Connecting to Microsoft Cosmos DB with Apache TinkerPop Dropwizard.

Deep-learning convolutional generative adversarial neural network crushes web design.

Graphs and graph datasets are rich data structures that can be used uniquely to improve the accuracy and effectiveness of machine learning workflows. Some of the key interactions are graph analytics as features, semi supervised learning, graph based deep learning, and machine learning approaches to hard graph problems.

Utilizing 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.

Learn how 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 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.

Congratulations, you have identified what software products to modernize and you have identified what cloud technology you need to use for the upgrade! Now what? Join us to identify how to get your business case and modernization project funded.

Dave and Ted discuss Graphday and their thoughts on DSE, Neo4j, Microsoft's CosmosDB, and JanusGraph.

In this post, we're going to dive into the client-side single-page application, commonly abbreviated as “SPA”. What is considered an SPA? What are important choices to be made when building one? How do you deploy it? When is an SPA a good choice or a bad choice?

Moving to a public or private cloud can dramatically reduce operational costs and speed up delivery of key features. But how do you get there from here? In this seminar we’ll jump into pragmatic ways to improve the way legacy applications are built and deployed as well as how to take an incremental approach to modernize those applications.

The next generation of React, aka Fiber, is eagerly anticipated. Expero's front-end team chimes in with their first impressions. If you’re like us, you’re eagerly awaiting the release of the new version of React (commonly referred to as React Fiber). We don’t intend to comprehensively go into the differences between React Fiber and the current React architecture (code named React Stack). However, when upgrading React, explicitly deprecated features tend to be pretty straightforward and easily called out with tooling like eslint. Still, some changes can be more insidious as they may have side effects that will be difficult to spot or reliably reproduce.

Is your software built on sunsetting technology like VB6, .NET, etc? Do you have low user satisfaction because of outdated software? Does it take too long to develop and roll out new features? Are you losing ground to solutions that offer mobile versions? Is it becoming difficult and expensive to maintain staff with legacy knowledge? Is your on-premise legacy infrastructure costly to maintain?

In this session of our client-server to cloud seminar series, we share proven methods for determining if modernizing makes sense for your application, a checklist of considerations, and a Lean process for creating a sound modernization strategy.

In this blog post, I'll discuss the process of building a micro service that is backed by a graph database and the technologies leveraged to accomplish it. I'll be building this microservice in Java using Maven for its declarative dependency management and build process and Dropwizard for its straightforward architecture and configuration, and then connect everything up to an Apache Tinkerpop enabled Graph Database.

Trying to modernize monolithic legacy applications is hard: these applications are core drivers of the business and the risk of messing them up is too great. However, as time goes on, the cost of maintaining these monoliths grows.

We get asked that question a lot given our early customer work with Titan evaluations, participation in the JanusGraph project and usage of Apache TinkerPop while concurrently being a premier DataStax Graph partner.

DataStax released 5.1, Neo4j released 3.2, Microsoft announces CosmosDB; there’s a lot of stuff happening in the graph database world. Looks like a prime time for some Gremlin training.

Scott Heath and Patrick McFadin talk about how Expero and Datastax are partnering together to solve complex problems with graph data.

Software and web developers often wear many hats, including the UX/UI hat. But some developers lack the knowledge and skills to design UIs or to collaborate effectively with UX designers and researchers.

Software and web developers often wear many hats, including the UX/UI hat. But some developers lack the knowledge to design UIs or to collaborate effectively with UX designers and researchers.