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The potential of healthcare systems worldwide is expanding as new medical devices and data sources are regularly presented to healthcare providers which could be used to personalise, improve and revise treatments further. However, there is presently a large gap between the data collected, the systems that store the data, and any ability to perform big data analytics to combinations of such data. This paper suggests a novel approach to integrate data from multiple sources and formats, by providing a uniform structure to the data in a healthcare data lake with multiple zones reflecting how refined the data is: from raw to curated when ready to be consumed or used for analysis. The integration further requires solutions that can be proven to be secure, such as patient-centric data sharing agreements (smart contracts) on a blockchain, and novel privacy-preserving methods for extracting metadata from data sources, originally derived from partially-structured or from completely unstructured data. Work presented here is being developed as part of an EU project with the ultimate aim to develop solutions for integrating healthcare data for enhanced citizen-centred care and analytics across Europe.

Many Markovian stochastic structured modeling formalisms like Petri nets, automata networks and process algebra represent the infinitesimal generator of the underlying Markov chain as a descriptor instead of a traditional sparse matrix. A descriptor is a compact and structured storage based on a sum of tensor (Kronecker) products of small matrices that can be handled by many algorithms allowing affordable stationary and transient solutions even for very large Markovian models. One of the most efficient algorithms used to compute iterative solutions of descriptors is the Shuffle algorithm which is used to perform the multiplication by a probability vector. In this paper we propose an alternative algorithm called Split, since it offers a flexible solution between the pure sparse matrix approach and the Shuffle algorithm using a hybrid solution. The Split algorithm puts the Shuffle approach in perspective by presenting a faster execution time for many cases and at least the same efficiency for the worst cases. The Split algorithm is applied to solve two SAN models based on real problems showing the practical contribution of this paper.

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