Knowledge Graphs for a Connected World - AI, Deep & Machine Learning Meetup

We live in an era where the world is more connected than ever before and the trajectory is such that data relationships will only continue to increase with no signs of slowing down. Connected data is the key to your business succeeding and growing in today’s connected world. Leading enterprises will be the ones that utilize relationship-centric technologies to leverage connections from their internal operations and supply chain to their customer and user interactions. This ability to utilize connected data to understand all the nuanced relationships within their organization will propel them forward as they act on more holistic insights.

Every organization needs a knowledge graph because connected data is an essential foundation to advancing business. Additional reading on connected can be found here: https://www.graphgrid.com/why-connect...

Knowledge Graphs: Journey to the Connected Enterprise

We live in an era where the world is more connected than ever before and the trajectory is such that data relationships will only continue to increase with no signs of slowing down. 

Connected data is the key to your business succeeding and growing in today’s connected world. 

Leading enterprises will be the ones that utilize relationship-centric technologies to leverage connections from their internal operations and supply chain to their customer and user interactions. This ability to utilize connected data to understand all the nuanced relationships within their organization will propel them forward as they act on more holistic insights.

Every organization needs a knowledge graph because connected data is an essential foundation to advancing business. Knowledge graphs provide: - Increased visibility between internal groups - Efficiency gains - Cross-functional data collaboration - Core complete and reliable business insights - Better customer engagement 

Additional reading on why connected data is beneficial: https://www.graphgrid.com/why-connect...

Connected data solutions available by Benjamin and his team via GraphGrid and AtomRain: https://www.graphgrid.com and https://www.atomrain.com

Neo4j 3.0 Welcomes a New Era for Graphs

At GraphConnect at the end of April the Neo4j team announced the release of Neo4j 3.0. We had the opportunity to celebrate this release at The Honest Company last night with the Graph Database LA Meetup group where I shared many of these highlights from the official Neo4j announcement. The first release in the 3.x series ushers in a new era of scalable yet reliable graph database technology with, this version of Neo4j based on a completely redesigned architecture that offers enhanced developer productivity, and varying deployment options at a massive scale.

3 Things to Expect in Neo4j 3.0

Here’s what to be expected with the new Neo4j 3.0:

• Redesigned internals that eradicates limits on node numbers and restoration of indexed and stored properties and relationships.
• Official support for language drivers via Bolt binary protocol and Java Stored Procedures support, while enabling full-stack developers for powerful application creation.
• Streamlined deployment structure and configuration for deploying Neo4j in the cloud or on premise.

Diving Deeper into Neo4j 3.0

Here’s an in-depth look of what’s new in the latest version:

• Unlimited Graph Storage
  By far the biggest headline in the release. Graph to size infinite – challenge accepted! Dynamic pointer compressions expands the available address of Neo4j as needed, making it possible to house

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Graph Advantage: Research Organizations

Many enterprises today build their business around research that involves piecing together meaningful data from the public domain for their customers. When trying to connect data across a domain in a meaningful way building around a graph database is a great tool because it models very well exactly how the business analysts at these research organizations are piecing together the real-world data they are finding during their research.

A business analyst may begin with one person and from there, move to the company they’re working for and then shifting to colleagues before moving on to places where their current colleagues previously worked, before finally settling for their past colleagues. Suddenly the business analysts has nearly finished building out an intuitive network of complex connections around this person of interest which would have been challenging and time consuming to try to represent in Excel.

Graph Database in Research Organizations

Research organizations are more than just managing large data volumes, their core goal is finding understanding that comes through research to gain insight of the available data. To properly leverage data relationships, a research organization requires a database technology that houses data relationship as a first-class entity.

As a native graph database, Neo4j provides several essential advantages for businesses today:

• Neo4j structures data connections precisely as they exist in the world around us with the contextually specific connections between entities as primary entities that can be explored in constant time.
• Real-time results for queries that are exploring the many different and complex connections Read More...... 

Graph Advantage: Connected Enterprise

The connected enterprise is the new norm. Traditional chain paradigm with sequential and siloed operations lacking a connection between customer and factory is no longer cutting it. Today enterprises are excepted to be sufficiently in touch and aware of how to interact with each uniquely individual person they are fortunate to call their customer. Technologies and operational procedures are rapidly changing to enable information to be connected and taken together to drive decision 

making, direction, and interaction with the customer.

Connected Enterprise: Data Essentials

Connected data is the lifeblood of today’s enterprise. Yet, it’s frequently isolated in varying silos across an organization, with different accessibility, redundancy, quality, and varying data formats. Managing connected data involves identifying, cleaning, storing, and governing increased data volumes within an enterprise. Connected data involves essential information such as customers, users, products, services, sites, and business units.

Adequate practices for connected data management differ along a wide range of approaches. On one end, many believe that connected data should be united in one location; while on the other end, some recommend managing data assets from one application or service, even if information is housed in multiple locations.

In both cases, data architects require a data model that’s versatile and fluid when exceptions arise and business needs change. And the only model that can answer this is the graph database.

Data Management and Graph Databases

Enterprises today are flooded with “big data”, a majority of which is master data. Dealing with complex relationships between data points could be the biggest problem facing today’s

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Every Organization Needs a Knowledge Graph

A knowledge graph as it relates to individual organizations is a unification of information across that organization enriched with contextual and semantic relevance. Introducing a knowledge graph creates a comprehensive and baseline set of knowledge accessible by personnel, applications and customers alike to gain understanding and drive actions and direction.

This foundational knowledge graph is not only useful for people and applications, but provides a relevant and evolving dataset for sophisticated learning and intelligence software systems to utilize in providing personalized internal guidance as well as highly engaging interactions with customers.

Knowledge Sharing Falling Short

To engage all personnel in collaboration and knowledge sharing, a majority of organizations today have adopted social networking trends and offering different kinds of internal tools. However, such applications can generate large volumes of unstructured organization data stored in isolated systems across an organization. This attempt at creating a holistic understanding falls short because all this knowledge sharing and information isn’t actually being connected together.

The main result from this approach is a complex infrastructure containing data silos filled with duplicated, expired, and redundant information. This makes it hard to see the right information and acquire important insights. Organizations today need a graph data platform to support increasingly complex 

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systemd Neo4j service on Ubuntu

systemd ships as the default process manager on Ubuntu 15; get the neo4j systemd service file to install and learn about the configuration. systemd has a lot of great features and I encourage you to check it out as it’s a big improvement IMO from SystemV and Upstart. If you’ve started working with 

systemd to manage your neo4j process, then you

might have stumble through the new setup. Here are a couple things I found helpful and some config that might get you moving along faster. I won’t go into detail on how to install services with systemd but it basically breaks down into these steps:

1.  Save service file:   /lib/systemd/system/neo4j.services 
2.  Reload config:  sudo systemctl daemon-reload 
3.  Enable on startup:  sudo systemctl enable neo4j.service 

Service file for systemd and neo4j

For those simply looking for a working neo4j.service file one is shown below. The rest of the writeup goes into detail how to install this service file and why some of the properties are set the way they are. This configuration assumes you have linked the neo4j script into /etc/init.d/neo4j and that your neo4j installation is at /opt/neo4j e.g. NEO4J_HOME=/opt/neo4j. The file below should be located at:

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