Prescriptive Recommendation Engines

The most common recommendation engines we interact with daily involve social media (the people we may know), retail (the products we may like), and entertainment (the music, clips and/or movies presented next in our streams).

Yet these are just the tip of the recommendation iceberg. When we look deeper into the inner workings of the largest organizations, enterprises and agencies in the world with critical business and mission decisions to make – based on a constant flow of ever changing, highly inter-connected data – we find much more complex and significant types of recommendations coming into play through prescriptive recommendation engines.

The ability to quickly and confidently make informed decisions is paramount in rapidly evolving mission scenarios. Designing and building reliable prescriptive recommendation engines starts with taking advantage of real-time connected data; the fuel for what will drive forward-thinking and innovative mission goals within an organization.

Prescriptive Recommendation Engines Guide Critical Decision Making

Organizations have many dynamic objectives but only a limited number of authorized resources for critical analysis and decision-making. Prioritizing decisions requiring skilled human input and

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Connected Data Analytics: Basics

As organizations adopt graph databases, their available connected data will grow, which will drive the need for analytics to leverage the connected data as a core component of their analysis. The key to unlocking new insights is to leverage the connectedness of the data as part of a graph analytics solution. Through graph analytics enterprises have gained competitive advantages because they are now discovering the cause, effect, and influence of certain patterns present in their organizations data.

When it comes to exploring how graph analytics can be used in solving problems, it boils down to its ability to compare “many to many to many.” For example, it makes it possible to not only ask about “friends” of a person, but also friends of their friends as well with details include beyond the fact they’re connected. Building up on such scenarios allows you to see influencers within a network. Graph analytics can infer paths via complex relationships to determine connections that aren’t easy to find and surface these to human analysts for confirmation, validation and action.

Graph Analytics: Connected Data Discovery and Business Impact

One aspect where graph analytics is an advantage is data discovery. It allows you to see patterns within data when you have no idea what question you want to ask. This makes it possible to find a needle in a haystack. As patterns start emerging from data sets, you are able to surface a clear picture of the precise elements and 

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Data Validation and Testing Your Graph Data State

Data validation lets you gain insight on the quality of your data assets. This involves grading your organization consistently to monitor your progress. When testing data, it’s essential to set metrics, as well as succeeding steps and goals to drive improvements. Data testing is even more crucial when loading data into a schema free graph database like Neo4j. So how do we it efficiently and continuously?

Schema-Free Nature of Neo4j and Data Validation

Neo4j is schema-free by nature, but does provide some schema concepts that can be enforced. This means, when your data flows via your Neo4j data pipeline and graph, there won’t be enforced constraints on data type. This also means Neo4j will try to pick the best data type when a property is being written if it isn’t specifically enforced for variations in numerical precision and all numerical values that are desired to be stored as strings. So if you happen to load data into Neo4j using LOAD CSV and you write a property consisting only of numerical value and want it stored as a string, then it’s essential you always wrap it in the Cypher toString() function to ensure you won’t end up with properties consisting of varying data types.

Data Validation with Postman, REST Requests, and Newman

For large scale automated data validation it’s beneficial to make use of a REST-client like Postman to create a 

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Graph Advantage: Fraud Detection

Financial institutions and insurance firms with traditional fraud detection capabilities lose billions of dollars to fraud. Traditional approaches in detecting fraud play a critical aspect in minimizing financial losses. However, an increasing number of fraudsters have created different methods to avoid being discovered. In order to gain the upper hand again these financial institutions are need to combine the traditional subject matter expertise of an analyst with enhanced exploration and discovery capabilities enabled through a highly connected data set in a graph database.

Real-Time Fraud Detection with Graph Databases

Graph databases provide new ways of unearthing fraud rings and other high-tech scams with incredibly precision. This predictive assist, allows your company to focus on the important data necessary to uncover and halt advanced fraudulent actives in real-time. At the same time, a graph database can offer insight based on data relationships to help you create advanced fraud detection systems according to connected intelligence.

Why Neo4j is Effective at Detecting Fraud

With fraudulent activity becoming more sophisticated and disconnected, enterprises have augmented their fraud-detecting capabilities with Neo4j to discover fraud rings and other scams accurately and in real-time. Regardless if it’s money laundering, e-commerce fraud, or bank fraud, Neo4j aids you in detecting elements 

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Neo4j Production Ready: Enterprise Cloud

The cloud today has become the primary deployment option for startups and is gaining adoption across the worlds largest enterprises. As with other critical infrastructure holding sensitive organization or customer data, there are several key questions enterprises must consider when evaluating the Neo4j graph database cloud deployments.

When running Neo4j in production, especially for an enterprise, the obvious baseline is to use the Neo4j Enterprise edition because it offers high availability clustering, cache sharding, hot backup, enhanced monitoring, and a several other critical production features. Taking a shortcut here will leave your enterprise vulnerable to outages, data loss and little ability to upgrade as new versions of Neo4j are released.

Essential Neo4j Enterprise Database Features

In addition to the high availability clustering and hot backups a few of the key benefits of Neo4j Enterprise include:

• Enterprise Lock Manager
  The enterprise lock manager enables high levels of concurrency through fast lock resolution, which provides vertical scaling of concurrent applications beyond 5 CPU cores.
• Cache Sharding
  For large graphs this is very useful when paired with sticky sessions because it provides a high cache hit

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Neo4j Production Ready: Security

With cloud adoption consistently accelerating in all organizations and industries, selecting a Neo4j cloud platform that offers your business security and scalability while eradicating lead time of internal-building is important. To simplify such a process for utilizing Neo4j Enterprise, the GraphGrid Data Platform provides a Neo4j Amazon Web Services (AWS) cloud offering. This Neo4j Enterprise data platform not only enables management of global Neo4j Enterprise clusters but is capable of helping you free up time from laying the foundation in operations, to let you concentrate on your product and services development for your business.

VPC Security for Neo4j Enterprise

Security is the biggest question on any organization’s mind when transitioning to the cloud, which is why that has been the core of the architecture and design for enabling a Neo4j Enterprise cloud offering in AWS.

In AWS it is important to utilize a VPC, which guarantees that all your Neo4j Enterprise resources can be launched in an isolated network that only your authorized personnel, infrastructure and services can access.

The VPC configuration must be properly configured to adhere to your enterprise security requirements. For instance, a public subnet is made so your servers can gain entry to the internet while your backend systems within a private subnet virtually has no internet access. It is important to establish controls for multiple security layers as well as security groups and network access control lists, for controlled access to Neo4j Enterprise clusters.

Furthermore, you can make a Peering or Direct VPN connectivity between your enterprise data center/VPC 

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Neo4j Production Ready: Deployment Basics

If you intend to perform a Neo4j production deployment successfully, you’ll likely think about the best application architecture to use and how you’ll operate your Neo4j Enterprise deployment at a scale. Some things you’ll need to think about should include how you intend to guarantee availability uptime, handle failures and efficiently facilitate zero downtime upgrades, which is really just the required baseline to be considered production ready. It may go without say, but to go to production without using Neo4j Enterprise is a huge risk to your applications availability. 

Neo4j Deployment Options

In terms of deployment options, there are two ways in which you can incorporate the graph database Neo4j Enterprise version within your app. These can be:

• Using Neo4j embedded: This means you’ll be utilizing the Neo4j Java libraries and packaging it with the rest of your application code into a WAR or JAR file that is deployed to the Java server of your choice such as JBoss or Tomcat.
• Using Neo4j server: This means you’ll be utilizing the default Jetty server wrapper that is provided with Neo4j and communicating with the database over rest, which is the recommended approach for almost all applications because it keeps your database decoupled from your application and enables the two to 

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