Computational graphs provide context how objects, people and environments relate to each other. In a way, they are digital twins for everything that is not an object: Processes, systems and interactions. They provide the glue between objects and the implicit and explicit rules in which they operate. Computational graphs are not new. The [semantic web](https://en.wikipedia.org/wiki/Semantic_Web) has tried to bring together information and how it relates to each other since the beginning of the world wide web. Especially with the rise of social platforms, computational graphs have been used to map relationships between people and groups. In terms of things, computational graphs model how individual objects relate to each other. This might be a connection ("Object A is physically connected to object B in this manner"), how they work together ("Object A, B, C and D together create machine X") and how they depend on each other ("Object A needs object B to operate"). With [people](https://en.wikipedia.org/wiki/Social_graph), graphs provide information about the state of a person ("busy", "bored", "talking", "happy"), their contacts and how they relate to each other ("brother", "son", "friend", "colleague"), the messages they exchanged and their current sentiment, the documents they have been working on alone or together, the objects they used or have access to, the places they are, have been or will be. In terms of environments, graphs group together places spatially (This is a country, a city, a district, block, house, room), but also logically (this is mine, ours, what I want to visit). Arguably, the purpose of computational graphs is to provide access to deep insights, generated from usage patterns. Building on top of [[Digital Twins]] (bringing places and devices into [[Virtuality]]), this will allow smart systems to find similarities and proactively generate rich, personalized scenarios based on the collected data. The result is a network of people, places and things. The model to look at each item individually is [[Augmented References and Referents]]. ## Key drivers for Computational Graphs Even if things become smart and people interact with them in a natural, intuitive way, they would still not be personal per se. They would have generic abilities and allow scenarios that would fit the average user, but not necessarily tailored to specific individuals. There is a need to add a personalization layer to digital dimensions that knows the user, its context as well as specific needs and relationships with their environments. Let’s consider such a personalized scenario: As the user gets up in the morning, their fitness band and bed would be aware that they have not slept well. Not only did they only sleep for 5 hours in total, but the sleep quality was very low as well. As they brush their teeth, the smart toothbrush would pick up indicators that they might be on the verge of getting a cold. As they get to their car to get to work, the calendar reminds them that they have a very important meeting in 25 minutes. From all that the car assumes that the user might feel a bit stressed and exhausted and automatically chooses a more comfortable seating position and selects the music accordingly. This scenario is based on multiple specialized objects providing information, which other objects can then use to make decisions. Effectively multiple smart things can then work together semi-autonomously to deliver complex scenarios, turning smart (but individual) things into smart environments. But there is a catch: There needs to be a trusted platform that keeps track of the user, its context and relationships with the environment. It would contain their contacts, the messages they exchanged, the documents they have been working on alone or together, the objects they used or have access to, the places they are, have been or will be. A platform that all things could send information to and some trusted things can pull information from. This is the core need for computational graphs. ## Summary **What it is**: A personalization layer that knows about the user, its context, its specific needs and relationships with their environment. **What it enables**: Everything digital benefits from understanding personal context to make sure that it interprets the current context correctly and does not act against the interests of the user.