The Internet of Things (IoT) hype may have slowed down, but the number of connected devices is still accelerating: a Gartner estimation places the number last year at 8.4 billion — and growing to 20.8 billion within two years. The problem: how to tie these devices together and make the vision of smart cities and other IoT use cases a reality. So far, the answer to that issue has proved elusive. Introducing the Telia Zone.
The consensus around graph is building
According to many technology analysts, the missing element is the emerging non-SQL data approach coalescing around graph databases. Graph technology is a proven data model, used initially by Google, Facebook** and LinkedIn, that’s great at linking multiple elements together, at scale, and finding interesting relationships that other data software formats, like relational, struggle to support. Andy Mulholland, VP & Principal Analyst at Constellation Research, has gone on record, for example, to state that, “The IoT revolution is a revolution that depends totally on graph technology. It’s simply not a feasible answer to go beyond piloting with anything else. It has to be a graph.” Other analysts agree: Alex Woodie, Managing Editor of Datanami, says “One of those key enabling technologies [of IoT] is graph databases.” Tony Baer at Ovum: “Graph technology will allow the Internet of Things to be represented transparently… without the need to force fit into arbitrary relational models. And finally, Matt Aslett, Research Director at 451 Research comments: “Graph databases offer the potential to store and analyse data from the IoT”.
How will this work in practice?
A graph can be used to better understand the relationships between data points, such as IoT devices — and those devices and machine-to-machine relationships a smart city or network rely on are easily captured in a graph data model, say, practitioners. So in a transport network, the question, “What road is experiencing the most congestion?” or “Which train is going to arrive late?” becomes: “What is the impact of the problem on the rest of the network?”
In parallel, with telecoms data, each time you call a new person or authorise a new device, you make a new connection. The same is true of the smart parking meters, smart traffic lights, or the cameras in the hospital driveways. When a new piece of equipment comes online, it will look around for the relevant controllers or other devices that it needs to listen to or send data to. The powering up or down of a device will make or break dozens of connections — and the natural way to represent such connections is using a graph database. In addition, connections are more than lines between entities: they each include a richness of information, such as direction, type, quality, weight, and more — all of which can be represented in a graph database as an integral part of each relationship.
Finally, those smart cities will, of course, be full of smart homes, and a smart network will be needed to deliver great services to those connected energy meters, thermostats and entertainment devices. A graph is also being seen as the default platform for organising the complex networks these services will need. Take Telia, the incumbent telecommunications carrier in the Nordic market across mobile, broadband, consumer and enterprise markets, for instance. In order to stay competitive, the Telia team decided to reinvigorate an area of least innovation: their consumer broadband business.
The Telia Zone
The Telia team recognized their roughly 1 million installed Wi-Fi routers as underutilised assets. Their goal was twofold: help consumers simplify their lives and help consumers better entertain within their homes.
The solution: The Telia Zone. After tracking all Wi-Fi connected devices within their customers’ homes, the Telia team provides this connected dataset and a number of APIs to a partner-driven ecosystem of apps and services.
The result is a smart home platform that allows consumers to pick and choose apps that meet the two goals of simplification and home entertainment.
Telia Zone apps do everything from reminding consumers when they forget to lock the door after they leave for the day to let them know when their kids arrive home after school (and turning on the lights when they get there).
By tracking patterns of connected data around when users leave and arrive home, the Telia Zone also offers highly accurate and relevant suggestions to other apps regarding when users will most likely be at home — information that is both elusive and salient to every home delivery service.
Powering it all on the backend is a graph database, which can be deployed on-premise or in the cloud. The Telia team chose graph technology because their smart home dataset was already a graph of connected data.
Also, with Neo4j’s Causal Clustering architecture, the Telia team was able to horizontally scale their operations without always knowing what given traffic loads might be. Furthermore, the graph data model allows them the flexibility to keep evolving the Telia Zone platform without breaking existing components.
A connected world strewn with brilliant smart cities and networks is on the horizon — and we have found the right data engine to manage it at last.
**Thanks to Twitter user MusicComposer1, Paul Colmer for pointing out that Facebook does not use graph technology as originally stated.
For more information on Facebook’s architecture, this Quora article may better explain.
Emil Eifrem is CEO and co-founder of Neo4j. Emil famously sketched out what today is known as the property graph model on a flight to Mumbai in 2000. Since then Emil has devoted his professional life to building and evangelising graph databases. He is a frequent conference speaker and a well-known author and blogger on NoSQL and graph databases, as well as co-author of the agreed Bible on graph databases, O’Reilly’s Graph Databases