ProvDS: Uncertain Provenance Management over Incomplete Linked Data Streams

Data processing in distributed environments is often across heterogeneous systems, bearing the need to exchange provenance information, such as, how and when data was generated, combined, recombined, and processed. Distributed systems involve multiple participants and data sources which can produce unreliable, erroneous data. Besides, there maybe exists oceans amount of data to deal with, e.g., in fields such as Internet of Things (IoT) and Smart Cities. Therefore, dynamic stream-based data processing mechanisms are more reasonable in these environments. Hence, we propose provenance and recovery-aware data management techniques that take dynamic, incomplete streams as inputs, 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 focuses on dynamic and incomplete heterogeneous data.