Provenance information in eScience is metadata that's critical to effectively manage the exponentially increasing volumes of scientific data from industrial-scale experiment protocols. Semantic provenance, based on domain-specific provenance ontologies, lets software applications unambiguously interpret data in the correct context. The semantic provenance framework for eScience data comprises e...

Users can determine the precise origins of their data by collecting detailed provenance records. However, auditing at a finer grain produces large amounts of metadata. To efficiently manage the collected provenance, several provenance management systems, including SPADE, record provenance on the hosts where it is generated. Distributed provenance raises the issue of efficient reconstruction dur...

We review the basics of data provenance in relational databases. We describe different provenance formalisms, from Boolean provenance to provenance semirings and beyond, that can be used for a wide variety of purposes, to obtain additional information on the output of a query. We discuss representation systems for data provenance, circuits in particular, with a focus on practical implementation...

Data provenance is becoming increasingly important for biosciences with the advent of large-scale collaborative environments such as the iPlant collaborative, where scientists collaborate by using data that they themselves did not generate. To facilitate the widespread use and sharing of provenance, ontologies of provenance need to be developed to enable the capture and standardized representat...

Data intensive scalable computing (DISC) systems, such as Apache Hadoop or Spark, allow to process large amounts of heterogenous data. For varying provenance applications, emerging provenance solutions for DISC systems track all source data items through each processing step, imposing a high space and time overhead during program execution. We introduce a provenance collection approach that red...

Provenance metadata in e-Science captures the derivation history of data products generated from scientific workflows. Provenance forms a glue linking workflow execution with associated data products, and finds use in determining the quality of derived data, tracking resource usage, and for verifying and validating scientific experiments. In this article, we discuss the scope of provenance coll...

Data mining aims at extracting useful information from large datasets. Most data mining approaches reduce the input data to produce a smaller output summarizing the mining result. While the purpose of data mining (extracting information) necessitates this reduction in size, the loss of information it entails can be problematic. Specifically, the results of data mining may be more confusing than...

Article history: Received 21 December 2011 Received in revised form 30 August 2013 Accepted 31 August 2013 Available online xxxx Provenance has become increasingly important in scientific workflows to understand, verify, and reproduce the result of scientific data analysis. Most existing systems store provenance data in provenance stores with proprietary provenance data models and conduct query...

We present an architecture that supports provenance queries in large, dynamic, multi-organizational environments. The Provenance Challenges have explored exchange across disparate provenance systems, yet this is only a first step. We describe requirements for multi-organizational provenance, evaluate candidate architectures, describe the approach implemented in the PLUS prototype provenance man...

Gathering data provenance at the operating system level is useful for capturing system-wide activity. However, many modern programs are complex and can perform numerous tasks concurrently. Capturing their provenance at this level, where processes are treated as single entities, may lead to the loss of useful intra-process detail. This can, in turn, produce false dependencies in the provenance g...