The project Cross-CPP deals with cross-sectorial Cyber Physical Products – CPPs in short – such as vehicles and smart buildings. CPPs can have many sensors that are collecting information about the CPP environment and their use.
The project offers a big data marketplace as “One-Stop-Shop” to data customers who want to tap into the enormous opportunity that arises from collecting data from various cross-sectorial CPPs. But is this enough, just to collect data from CPP and use it for different applications? Can we be sure that the data coming from CPP is not influenced by other factors such as weather, the geographical location or simply the color of a car?
Have you ever heard about word context? According to Oxford dictionary, context is “The circumstances that form the setting for an event, statement, or idea, and in terms of which it can be fully understood.” In the artificial intelligence domain, the concept of context is usually defined as the “generalization of a collection of assumptions”. For Cross-CPP, “Context can be a set of information which characterizes the situation under which sensor data are obtained (e.g. situation under which the data from temperature sensor in a car is obtained)”. Sounds difficult or? Well, let’s take a simple example to understand what context means for a vehicle. Do you know, that for modern vehicles mobile sensor networks can produce over 4000 signals per second per vehicle? This is a huge amount of data isn’t it? Now imagine if this raw sensor data comes with additional information, such as the circumstances under which the data has been collected, or the factors that can influence the sensor measurements that are being observed from vehicles? Such answers can be provided by context. The context information is an additional information that data customers get when they are looking for data collection from Big Data Marketplace. Still not quite clear?
Let’s say we have a black car equipped with an exterior temperature sensor: Wouldn’t it be great if we could retrieve data from this temperature sensor to provide it to a data customer who might build a new service making use of this data? We also know that many factors influence the value measured by the sensor: this could be the colour of car (black), the current location of the car, like altitude, the height of sensor installed in a car, what time of the day or year it is, and many other factors. All this information that is either the car’s metadata or can be measured with other sensors, from now on we will call enhanced monitored data. Furthermore, we can deduce certain situations for the temperature value based on this enhanced monitored data, which certainly defines the context of this car. Such a situation can be that the temperature value measured by the black colour car with sensor located on 20cm above ground level, was standing at mid-day in a summer day in south of France is not very reliable.
We hope that above example is clear enough for understanding concept of context involved in Cross-CPP project. We would also like to use context not only on data collection side but also on security aspect of Cross-CPP modules and usage of services, to provide the CPP user/owner with a flexible (context based) protection for his CPP information.
For Cross-CPP modules, Context will be extracted as specified by the request from the data customer or as needed by internal modules such as the Cross-CPP security module. And as we learned above, in order to extract context the extractor will use an enhanced monitored data (combination of metadata for particular CPP and raw sensor data) together with rules and defined context models.
In case you are wondering, how this is all going to be realised in scope of the project step by step, we are offering several blogs on context topic and we will make sure that you get enough insights to work with context! In the following blogs, we will explain how data customers can work with enhanced monitored and contextual data. We will explain how a context tool can extract context data and how it can be useful for the data customer to make informative decisions. Furthermore we will explain context based security for Cross-CPP modules, where we will learn how context can help to improve security for CPP owners and last but not least we will also provide insights for context related tools to give service providers like Meteologix a toolkit which they can use to improve their innovative services. All of these interesting topics will be provided as series of subsequent blogs, so …
Stay tuned 🙂
Your ATB Team and Cross-CPP consortium partners