BIGProv 2013 - International Workshop on Managing and Querying Provenance Data at Scale

Provenance data is poised to become pervasive in key areas of information management, ranging from traditional areas of science (i.e., life sciences, earth sciences, astronomy, etc.), to new applications enabled by the Web (e.g., social sciences, social network analysis, quality and trust in Web publishing).
As the volume of provenance metadata increases with the volume of the underlying data whose history it describes, new challenges for managing and querying provenance at scale emerge, i.e., provenance data is growing in both "count" and "complexity". It is growing in count because of the very large number of provenance traces (one for each Twitter message, for example), and in complexity in the case of provenance graphs that are generated from provenance-enabled programming environments (e.g., scientific workflow systems) and middleware. Data-intensive science is bound to produce provenance that fares high on both accounts.
At the same time, emerging standards such as PROV, the W3C recommendation for provenance modelling and Web-based access, suggest that provenance data will increasingly be encoded using Semantic Web technology. This in turn suggests that provenance data will soon form a natural extension of, and seamlessly blend with, the growing Linked Data Cloud.
The new Managing and Querying Provenance Data at Scale workshop (BIGProv) stems from these premises. We are interested in exploring the system and modelling challenges associated with collecting, storing, querying, and exploiting large volumes of possibly complex provenance data. We seek to map the state of the art, elicit new research problems, and learn about existing systems. More specifically, the workshop scope includes the following topics:
Automated capture of provenance at multiple layers (system, middleware, applications)
Database models, languages, and systems for storing and querying large-scale provenance
Provenance and Linked Open Data (LOD): seamless representation and query models
Comparison and performance benchmarking of different data architectures and query models for provenance
Analysis of existing graph query models and systems for provenance graphs
Reference datasets for provenance benchmarking
System descriptions and demonstrations of large-scale provenance and graph data
Uniform querying over heterogeneous provenance traces
Abstraction models for provenance and their applications to user presentation, visualization, and privacy preservation