Chimera: AVirtual Data System for Representing, Querying, and Automating Data Derivation
SSDBM '02 Proceedings of the 14th International Conference on Scientific and Statistical Database Management
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
A survey of data provenance in e-science
ACM SIGMOD Record
Provenance-based validation of e-science experiments
Web Semantics: Science, Services and Agents on the World Wide Web
Provenance-aware storage systems
ATEC '06 Proceedings of the annual conference on USENIX '06 Annual Technical Conference
International Journal on Digital Libraries
Query capabilities of the Karma provenance framework
Concurrency and Computation: Practice & Experience - The First Provenance Challenge
Special Issue: The First Provenance Challenge
Concurrency and Computation: Practice & Experience - The First Provenance Challenge
A Vision and Agenda for Theory Provenance in Scientific Publishing
Database Systems for Advanced Applications
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
The Open Provenance Model core specification (v1.1)
Future Generation Computer Systems
A protocol for recording provenance in service-oriented grids
OPODIS'04 Proceedings of the 8th international conference on Principles of Distributed Systems
CombeChem: a case study in provenance and annotation using the semantic web
IPAW'06 Proceedings of the 2006 international conference on Provenance and Annotation of Data
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In this paper, we implement a provenance-aware system for documenting publications, called PADS. It employs a three-layered provenance hierarchy, which can output diverse types of provenance data related to the research life cycle. From this, we generate different profiles for research ventures, reviewers, and authors. PADS employs the standard Open Provenance Model (OPM) specification for capturing provenance data, and stores this data as ontological instances. We show that data is retrieved without any apparent delay in the execution time of the queries. We also demonstrate how this data can be used to make useful recommendations to the organizers, in order to manage upcoming research ventures.