Cross-CPP - Ecosystem for Services based on integrated Cross-sectorial Data Streams from multiple Cyber Physical Products and Open Data Sources

Cross-CPP - Ekosystém pro služby založené na integrovaných datových proudech, získávaných napříč sektory z více kyber-fyzikálních výrobků a otevřených datových zdrojů

grant

Data stream analysis, Real time data analytics, Web and information systems,
database systems, Cyber Physical Products, Marketplace, Context Sensitivity,
Security, Data Protection, Cross, Sectorial Services, Agreed Data Model

The objective is to establish an IT environment for the integration and analytics
of data streams coming from high volume (mass) products with cyber physical
features, as well from Open Data Sources, aiming to offer new cross sectorial
services and focusing on the commercial confidentiality, privacy and IPR and
ethical issues using an context sensitive approach. The project addresses
cross-stream analysis of large data volumes from mass cyber physical products
(CPP) from various industrial sectors such as automotive, and home automation.
The business objective of the research is to allow for analyses of such data
streams in combination to other (non-industrial, open) data streams and for the
establishment of diverse enhanced sectorial and cross-sectorial services. The
project will develop: (i) New models for integration and analytics of data
streams coming from multi-sectorial CPP, including shared systems of entity
identifiers applicable to multi-sectorial CPP (as well as the definition of
agreed data models for data streams from multiple CPP aiming at defacto standard;
(ii) Ecosystem, including a common Marketplace, and methodology to use such
models to build multi-sectorial cloud based services, (iii) Toolbox for real-time
and predictive cross-stream analytics, context modelling and extraction, and
dynamically changing security policy, privacy and IPR conditions/rules and (iv)
set of services such as services based on a combination of data streams from home
automation and (electrical) vehicles to provide enhanced local weather forecast
and predict and optimise energy consumptions in households. The project will
build upon the results from past and current projects, where results from the
project AutoMat, addressing services developed based on data streams from
vehicles, will be used as a basis for further development aiming to extend it to
integrated, cross-sectorial data streams analytics.

Smrž Pavel, doc. RNDr., Ph.D. (DCGM) – research leader
Fajčík Martin, Ing., Ph.D. (DCGM)
Zemčík Pavel, prof. Dr. Ing., dr. h. c. (DCGM)