Sapienza Università di Roma, Università degli Studi di Brescia, Politecnico di Torino, Italtel S.p.A.
Monitoring business processes that occur all along a supply chain requires to collect data provided by the different stakeholders and, depending on the type of phenomena to be explored, to support efficient data analytics. Due to the ever-increasing dynamicity of supply chains, monitoring solutions should be able to be set up and run on different environments. This calls for distributed solutions which break the usual architectural assumption to have a central entity in charge of collecting, integrating and offering tools for the analysis. This requires to innovate the current solutions based on data warehouses or data lakes which are logically distributed, but usually physically deployed on a single cloud and managed by a single provider. While, on the one side, these solutions offer to store significant amounts of data and to support high-demand computation for data analytics, on the other side, locating all the resources on the cloud results in a data analytics efficiency that depends on the efficiency of the network. Especially in an industry setting, data analytics requires high efficiency to properly react in case of problems during the production, as well as to intercept the possible fluctuations of the market.
Goal of this project is to propose an innovative distributed monitoring supply-chain solution able to easily set up trusted and efficient data exchange among the organizations, to evaluate whether the agreements among the parties belonging to the supply chain are respected. Trust is supported by a blockchain-based solution which monitors and stores information about the compliance of the running processes w.r.t. the agreements. Data analysis relies on adaptive data-lake architectures that take advantage of the computing continuum along the cloud-edge deployment to increase the efficiency of the data analytics. The envisioned adaptation relies on the possibility to decide – at run-time – where and when data should be deployed along the
continuum, based on the required analytics. Security and data sovereignty will be ensured through an in-depth adversarial analysis of all the elements of the supply chain and a blockchain-based solution to track the resulting data flows. Exploiting the 5G network, which can be assumed to be pervasive and to offer connectivity to all the devices involved, efficiency can be improved by 5G slicing methods to enable adaptive QoS data flows.
Innovative federated data lake architecture to support data sharing of business process monitoring data.
5G slice management and orchestration solutions to support reliable and fast access to
heterogeneous data.
An in-depth adversarial analysis of the supply chain: The aim of this deliverable is to conduct an in-depth adversarial analysis of all the elements of the supply chain to ensure security and data sovereignty. The analysis will identify potential security threats and vulnerabilities that can be exploited by malicious actors. The output of the analysis will be the threat model and risk analysis, alongside an evaluation of the current state of the art security solutions to support the design of the monitoring solution.
A PoC implementation of the blockchain-based solution designed to track the data-flows of the distributed data lake. The focus of the implementation will be that of studying the capabilities and limitations of the system, alongside testing its security and resilience to attacks.
