Reproducibility of scientific research relies on accurate and precise citation of data and the provenance of that data. Earth science data are often the result of applying complex data transformation and analysis workflows to vast quantities of data. Provenance information of data processing is used for a variety of purposes, including understanding the process and auditing as well as reproduci...

Information retrieval on RDF data benefits greatly from additional provenance information attached to the individual pieces of information. Provenance information such as origin of data, certainty, and temporal information on RDF statements can be used to rank search results according to one of those dimensions. In this paper, we consider the problem of aggregating provenance information from d...

The web lacks support for explaining information provenance. When web applications return answers, many users do not know what information sources were used, when they were updated, how reliable the source was, or what information was looked up versus derived. Support for information provenance is expected to be a harder problem in the Semantic Web where more answers result from some manipulati...

Data transformations that involve (partial) ordering, and consolidate data in presence of uncertainty, are common in the context of various applications. The complexity of such transformations, in addition to the possible presence of meta-data, call for provenance support. We introduce, for the first time, a framework that accounts for the conjunction of these needs. To this end, we enrich the ...

Most provenance capture takes place inside particular tools – a workflow engine, a database, an operating system, or an application. However, most users have an existing toolset – a collection of different tools that work well for their needs and with which they are comfortable. Currently, such users have limited ability to collect provenance without disrupting their work and changing environme...

Intelligence analysis is the process of reasoning about information in order to produce hypothetical explanations for a situation. In this process, it is fundamental to assess how, when, and where this information has been elaborated. A model of provenance can capture this contextual information. Provenance data inevitably affects the identification of plausible conclusions, thus, it must be in...

The current state of the art for provenance in data stream management systems (DSMS) is to provide provenance at a high level of abstraction (such as, from which sensors in a sensor network an aggregated value is derived from). This limitation was imposed by high-throughput requirements and an anticipated lack of application demand for more detailed provenance information. In this work, we firs...

Provenance allows decision-makers to evaluate the importance of pieces of data. PROV is the standardised model of provenance for use on the web, particularly suited for situations where data is generated by systems under distributed control, such as in coalition operations. If human decision-makers are to make effective use of provenance data, they need to understand it, and this work establish...

We present a formalism for provenance in distributed systems based on the π-calculus. Its main feature is that all data products are annotated with metadata representing their provenance. The calculus is given a provenance tracking semantics, which ensures that data provenance is updated as the computation proceeds. The calculus also enjoys a pattern-restricted input primitive which allows proc...

Many modern applications involve collecting large amounts of data from multiple sources, and then aggregating and manipulating it in intricate ways. The complexity of such applications, combined with the size of the collected data, makes it difficult to understand the application logic and how information was derived. Data provenance has been proven helpful in this respect in different contexts...