: Data provenance is becoming increasingly important for biosciences with the advent of large-scale collaborative environments such as the iPlant collaborative, where scientists co...
E-science applications use fine grained data provenance to maintain the reproducibility of scientific results, i.e., for each processed data tuple, the source data used to proce...
Mohammad Rezwanul Huq, Andreas Wombacher, Peter M....
In information extraction, uncertainty is ubiquitous. For this reason, it is useful to provide users querying extracted data with explanations for the answers they receive. Provid...
ce that allows navigation from an abstract model of the experiment to instance data collected during a specific experiment run. We outline modest extensions to a commercial workflo...
Provenance in the context of workflows, both for the data they derive and for their specification, is an essential component to allow for result reproducibility, sharing, and know...
: The increasing ability for the sciences to sense the world around us is resulting in a growing need for data driven applications that are under the control of workflows composed ...
Provenance is information that aids understanding and troubleshooting database queries by explaining the results in terms of the input. Slicing is a program analysis technique for...
The importance of maintaining provenance has been widely recognized, particularly with respect to highly-manipulated data. However, there are few deployed databases that provide p...
The need to understand and manage provenance arises in almost every scientific application. In many cases, information about provenance constitutes the proof of correctness of re...
Agent-oriented cooperation techniques and standardized electronic healthcare record exchange protocols can be used to combine information regarding different facets of a therapy re...