Our Concept

How Cross-CPP enables the creation of new & innovative services

In contrast to today's sporadic proprietary CPP ecosystems, which are in most cases restricted to CPP manufacturer specific services and which are not open for third parties interested in these CPP data, the Cross-CPP project focuses on what CPP and their sensor data can bring to the outside world. Therefore, as key challenges, Cross-CPP has to overcome several obstacles by establishing a CPP Big Data Ecosystem, which should develop the following main characteristics:

  • Brand independent concept, open for integration of diverse CPP data providers coming from different industrial areas, also providing a standardized cross industrial CPP data model which needs to be flexible enough to incorporate data coming from various industrial sectors.

  • CPP Big Data marketplace providing to service providers a single CPP data access point with just one interface (one-stop-shop), as well as support functionalities for easy data mining/analytics. By these means, data customers (Service Providers) just need to set-up and maintain one interface to gather diverse CPP data from different CPP providers.

  • Controlled access to diverse CPP data streams and optimal management of data ownership and data rights, applicable to various cross CPP data streams.

  • Win-Win value chain for all ecosystem partners, due to the fact that the costs for the ecosystem in place can be shared by a great many data customers, which will make a single service much more economical.


In general the ecosystem can be separated into three pillars:

  • Left pillar: Data Providers (CPP Manufacturers) -> Comprising data harvesting and making CPP data from various industrial sectors available, transfer brand specific data streams into the common CPP data model.

  • Middle pillar: Cross-CPP Cloud Storage & Big Data Marketplace (MP) -> Comprising a cloud based concept for CPP Cloud Storage. Enabling controlled access to CPP data from different sources, offering support to Service Providers in the form of an easy access and detection of needed data, as well as of flexible cross data stream analysis tools.

  • Right pillar: Data Customer/Service Provider -> Cross-sectorial industries or manufacturers of CPP using CPP data from various products to create new value out of that data (“CPP-data” has no value in itself), by improving services or establishment of diverse new cross-sectorial services.


Left pillar - Data Harvesting and CPP Company Backend

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Data Harvesting:

The main role of the Proprietary CPP Data Harvesting is to acquire the data from a proprietary CPP data source and send them to CPP Company Backend. To ensure that the data harvesting process is in consent with end user needs, a data acquisition configuration is downloaded from the CPP Company Backend and deployed in the CPP data logger. CPP data will be measured and stored as CPP data packages. In order to reduce the amount of information to be transmitted the CPP signals can be pre-processed and aggregated. These CPP data will be transmit to CPP Company Backend module according to the transmission strategy defined by the CPP manufacturer.

Company Backend:

It represents the central data access point to the CPPs for thousands to millions of CPPs of a brand. It will be based on proprietary brand specific solution, interpreting and transforming proprietary manufacture-specific CPP data into physical information in reference to agreed owner permissions. Furthermore, the information will be validated and can be masked to enforce privacy. Finally, the information is converted into the required quasi standard information representation, the Unified Cross-Industrial Data Model format and is published to the owner’s CPP Cloud Storage. The CPP Company Backend has to handle also the consent of the data owner for data harvesting as well as with a cloud storage provider. The CPP data owner can also terminate these contracts a any time.

Middle pillar - Cloud Storage and Big Data Marketplace

Cloud Storage:

The data are stored in a brand-independent, open and transparent data model represented by the Common CPP Data Model. However, the CPP Data Model will not be a rigid, rather representing a living data structure, where in reference to the needs of the service provider community the number of signals to be recorded, as well as the type of measurement channels can be modified or extended. Therefore, the storage management must be able to handle a CPP Data Model update. The retrieved CPP information from the storage cloud will be decrypted, verified, anonymised where needed.

Big Data Marketplace:

For the data customer (service provider) side the CPP Big Data Marketplace represents a “One-Stop-Shop” Marketplace provide a single point of access to data streams from multiple mass products. Therefore, the marketplace will also offer instruments enabling an easy access and detection of needed data, as well as a data analytics toolbox, which will provide easy to use big data analytic functionalities for Service Providers with low big data expertise and knowledge. On the other hand, in respect to a service provider request to access specific CPP information sets the CPP Big Data Marketplace has to identify the relevant data owners and manage their permission for the data access by the service provider.

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Right pillar - Data customers & Service providers

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Once all contractual agreements for the data access by service providers have been arranged, for the service run-time phase the service providers have to enable to bring brand-independent cross-sectorial data stream content to their worlds. Therefore, the marketplace offers a quick and easy access to the CPP data from various products, including flexible cross-stream analysis tools for large data volumes, covering features such as data cleaning, filtering, etc., transforming the CPP information into service relevant input information. On service provider side an integration of other big data information sources and the CPP information forwarded by the CPP Big Data Marketplace will happen and big data analytics concepts applied. Based on specialized algorithms for the various services, the final service products are generated. These services may be offered at already existing portals (e.g. Apple App Store, Google Play Store, etc.) in order to distribute their services and applications.

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