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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.

Companies across a variety of industries are trying to attain a holistic view of their customers. From creating a more personalized experience, to creating more timely and relevant support, to simply marketing to them more efficiently and effectively, there are tremendous gains to be had with a 360-degree view.

As a user researcher, I’m always inclined to say, “Test everything, all the time!” when people ask, “What/when/how should we validate with users?” That’s my pie in the sky: the place where there’s all the time and all the budget in the world to get every last detail or spec just right for the good of the user, the product, and, ultimately, the business. But that’s not real life. Projects run on strict budgets and tight timelines, and there’s not always a lot of wiggle room.

Do you remember the first time you saw a commercial about “the Cloud?” That was one of the pivotal moments for technology buzzwords going mainstream. It’s been a nonstop thrill ride since then: Web 2.0. Internet of Things. Big Data. Machine Learning. Like “the Cloud,” the term “machine learning” is thrown around a lot, but it’s not entirely clear who it is useful to. People who follow it are aware that machine learning techniques were used by Google to create an unstoppable Go playing machine, and that it allows computers to drive with abilities getting closer to human drivers by the day.

Congratulations! Your data is up and running in a graph database and you have big plans for this technology. But now the system is overtaxed and not performing properly. Don't worry- the database may not be the culprit. Proper user experience and visualization techniques can greatly improve performance and dynamic data interaction.

Webinar presentation on how to combine Graph databases with an iterative, user-driven approach and the latest UI tools to enable your team to start creating valuable user experiences with graph data.

This year at the Data Day 2017 conference in Austin,TX, keynote speaker Emil Eifrem declared 2017 the Year of Graph. Graph data storage certainly is becoming more mainstream, with a myriad of both commercial and open-source options currently available and maturing at an accelerated pace. But so what? Why should user experience practitioners, or anyone else that is not a database administrator, care about this trend in data storage technology?

What does the term “search” mean to you?

A quick recipe for bringing up your first web worker with Webpack in the mix and share some tips to keep your implementation clean.

Web application types include static website, traditional server-side rendering, client-side single-page application, and isomorphic single-page applications.

Product owners and stakeholders have a tendency to skip over discovery research and go straight to design—and then skip over validation research and go right to release. One of the main drivers behind this tendency is the fact that looking at designs is fun. Looking at numbers and bulleted lists of findings is not (as much) fun (for stakeholders). With designs, they get to see their product progressing and growing from inception to build. Data is more behind-the-scenes; it may drive design, but so what?

What are the next big trends in UX? At our recent Expero Summit, we discussed many advances that promise to transform how users interact with technologies. As augmented reality and other technologies take substantive form, it’s more and more about what the user needs from these amazing technologies and less about how cool the technology actually is. It’s a given that the technology is only going to get cooler. What’s not as obvious is whether the user is ready for it.

One of the major hesitations from product stakeholders regarding end-user engagement, specifically user testing, is that they often don’t want anyone to see it till it’s “perfect” or “ready” or “MVP.”

A supernode is a vertex with a large number of edges. In graph theory, these high-degree vertices are known as hubs.

When designing and developing software, it is critical to take into account the limitations of the technology employed, especially hardware—things like computers, boxes and other physical devices. But there’s another aspect to hardware that should be taken into account and is often overlooked: the user.

OrientDB is one of several popular graph data stores on the market today. It provides a multi-model approach with the powerful nature of a graph database and the flexibility of a document data store. If you have decided to build out your multi-tenant application on top of OrientDB, you are in luck as it has several built-in, out-of-the-box methods for handling multi-tenancy.

How do you handle customer #2? You delivered an MVP of some hosted software for customer #1. Your brother-in-law knows a guy who has a similar problem and after a lunch meeting, now you need to add customer #2 to your incubating SaaS tool. Of course customer #1 and customer #2 shouldn’t be able to see each other’s data, but you don’t necessarily want to install and configure everything all over again just because you added another customer.

In one of the projects Expero worked on several years ago, the client chose to build their own custom authentication solution. For three weeks, one developer’s status at the scrum every morning was “security.” It took that competent developer several weeks to get a very basic custom solution in place. Additionally, that solution didn’t even have integration with other identity providers or any other bells and whistles! You can easily double that estimate if you want even a few providers and a user interface that doesn’t look drab.

Neo4j is the most popular graph data store available today. It leverages graph technologies to help build modern high-performing applications, but it does not have any native multi-tenant support. However, you may have decided to build out your multi-tenant application and that Neo4j is the right graph data store to fit your needs. In any multi-tenant system, the trick (from a data-store side) really comes down to how to isolate one tenant’s data (physically or logically) from another tenant’s.