Extracting Causal Graphs from an Open Provenance Data Model. Miles, S., Groth, P., Munroe, S., Jiang, S., Assandri, T., & Moreau, L. Concurrency and Computation: Practice and Experience, 20(5):577--586, 2008. abstract bibtex The open provenance architecture (OPA) approach to the challenge was distinct in several regards. In particular, it is based on an open, well-defined data model and architecture, allowing different components of the challenge workflow to independently record documentation, and for the workflow to be executed in any environment. Another noticeable feature is that we distinguish between the data recorded about what has occurred, process documentation, and the provenance of a data item, which is all that caused the data item to be as it is and is obtained as the result of a query over process documentation. This distinction allows us to tailor the system to separately best address the requirements of recording and querying documentation. Other notable features include the explicit recording of causal relationships between both events and data items, an interaction-based world model, intensional definition of data items in queries rather than relying on explicit naming mechanisms, and styling of documentation to support non-functional application requirements such as reducing storage costs or ensuring privacy of data. In this paper we describe how each of these features aid us in answering the challenge provenance queries.
@article{ firstProvenanceChallenge,
author = {Simon Miles and Paul Groth and Steve Munroe and Sheng Jiang and Thibaut Assandri and Luc Moreau},
title = {Extracting Causal Graphs from an Open Provenance Data Model},
journal = {Concurrency and Computation: Practice and Experience},
abstract = {The open provenance architecture (OPA) approach to the challenge was distinct in several regards. In particular, it is based on an open, well-defined data model and architecture, allowing different components of the challenge workflow to independently record documentation, and for the workflow to be executed in any environment. Another noticeable feature is that we distinguish between the data recorded about what has occurred, process documentation, and the provenance of a data item, which is all that caused the data item to be as it is and is obtained as the result of a query over process documentation. This distinction allows us to tailor the system to separately best address the requirements of recording and querying documentation. Other notable features include the explicit recording of causal relationships between both events and data items, an interaction-based world model, intensional definition of data items in queries rather than relying on explicit naming mechanisms, and styling of documentation to support non-functional application requirements such as reducing storage costs or ensuring privacy of data. In this paper we describe how each of these features aid us in answering the challenge provenance queries.},
pages = {577--586},
volume = {20},
number = {5} ,
year = {2008}
}
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