ProvDS: Uncertain Provenance Management over Incomplete Linked Data Streams

• Applicant: Professor Dr. Manfred Hauswirth
• Subject Area: Security and Dependability, Operating-, Communication- and Distributed Systems
• Term: from 2017 to 2022
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 323223507

Provenance describes how results are produced starting from data sources, curation, recovery, intermediate processing, to the final results. Provenance has been applied to solve many problems and in particular to understand how errors are propagated in large-scale environments such as the Internet of Things. In fact, in such environments operations on data are often performed by multiple uncoordinated parties, each potentially introducing or propagating errors. These errors cause uncertainty of the overall data analytics process that is further amplified when many data sources are combined and errors get propagated across multiple parties. The ability to properly identify how such errors influence the results is crucial to assess the quality of the results. This problem becomes even more challenging in the case of Linked Data Streams, where data is dynamic and often incomplete. In the ProvDS project, we investigate methods to compute provenance over Linked Data Streams that are incomplete. More specifically, we propose provenance and recovery-aware data management techniques that take as input incomplete streams, and simultaneously recover the missing data and compute the provenance over the reconstructed streams. Unlike traditional provenance management techniques, which are applied on complete and static data, our research agenda focuses on dynamic and incomplete heterogeneous data. At a technical level, our main objectives are to provide i) means to deliver a dynamic provenance trace of the results to the user, ii) methods to discover the probable provenance of recovered pieces of data, and iii) provenance-aware compression and storage techniques. The accuracy and the efficiency of the developed techniques will be evaluated and tested using a real-world datasets.

• DFG Programme: Research Grants
• International Connection: Switzerland
• Partner Organisation: Schweizerischer Nationalfonds (SNF)
• Co-Investigator: Professor Dr. Philippe Cudre-Mauroux