Panda (for Provenance and Data) is a new project whose goal is to develop a general-purpose system that unifies concepts from existing provenance systems and overcomes some limitations in them. Panda is designed for “data-oriented workflows,” fully integrating data-based and process-based provenance. Panda’s provenance model will support a full range from fine-grained to coarse-grained provenan...

Secure provenance that records the ownership and process history of data objects is vital to the success of data forensics in cloud computing. In this paper, we propose a new secure provenance scheme based on group signature and attribute-based signature techniques. The proposed provenance scheme provides confidentiality on sensitive documents stored in a cloud, unforgeability of the provenance...

Today’s programming languages provide no support for data provenance. In a world that increasingly relies on data, we need provenance to judge the reliability of data and therefore should aim for making it easily accessible to programmers. We report our work in progress on an extension to the Links programming language that builds on its support for language-integrated query to support where-pr...

Web resources, such as publications, datasets, pictures and others can be directly linked to their provenance data, as described in the specification about Provenance Access and Query (PROV-AQ) by the W3C. On its own, this approach places all responsibility with the publisher of the resource, who hopefully maintains and publishes provenance information. In reality, however, most publishers lack...

Provenance describes how objects came into their current state, e.g. it describes different contributions to a document. This information provides detailed audit trails, verification of existing objects as well as reenactment of recorded activities. Provenance solutions consist of data gathering, storage and analysis, its implementation is delegated to domain application developers. This leads ...

A necessary first step in the preservation of digital scientific data is gathering enough information “about” a scientific outcome or data collection, that it can be discovered and used a decade from now as easily as it is reused next week. Data provenance, or lineage of a collection, can capture how a particular scientific collection was created, when and by whom. Our goal is to devise tools a...

Data provenance allows scientists in different domains validating their models and algorithms to find out anomalies and unexpected behaviors. In previous works, we described on-the-fly interpretation of (Python) scripts to build workflow provenance graph automatically and then infer finegrained provenance information based on the workflow provenance graph and the availability of data. To broade...

The interest in data provenance and trust has been increasing in the last years and the community is putting now a lot of effort in finding a standard model representation. The W3C provenance incubator group is focused on this area, analyzing different provenance models and making mappings between them and the Open Provenance Model (OPM)[1], which is the model they intend to make the standard. ...

It is generally acknowledged that when, in 1988, John Chambers and Richard Becker incorporated the S AUDIT facility into their S statistical programming language and environment, they created one of the first provenance-aware applications. Since then, S has been spiritually succeeded by the open-sourceR project; however,R has no such facility for tracking provenance. This paper looks at how pro...

Data Provenance refers to the lineage of data including its origin, key events that occur over the course of its lifecycle, and other details associated with data creation, processing, and archiving. We believe that tracking provenance enables users to share, discover, and reuse the data, thus streamlining collaborative activities, reducing the possibility of repeating dead ends, and facilitati...