As we are moving from a process oriented approach where data is the input and output of designed processes to a data-centric approach where data directs processes within organization and society, we need to radically change the way we approach data and the way we manage organizations and how we deal with compliance. Processes are designed, but data is embedded with “knowledge” itself, so how can we trust the outcome of a process that is directed by data itself? And how can we – as a society – trust organizations that uses data in such a manner?
RDF Stream Processing (RSP) has been proposed as a way of bridging the gap between the Complex Event Processing (CEP) paradigm and the Semantic Web standards. Uncertainty has been recognized as a critical aspect in CEP, but it has received little attention within the context of RSP. In this paper, we investigate the impact of different RSP optimization strategies for uncertainty management.
The growing web of data warrants better data management strategies. Data silos are single points of failure and they face availability problems which lead to broken links. Furthermore the dynamic nature of some datasets increases the need for a versioning scheme. In this work, we propose a novel architecture for a linked open data infrastructure, built on open decentralized technologies. IPFS is used for storage and retrieval of data, and the public Ethereum blockchain is used for naming, versioning and storing metadata of datasets.