Authors:
Stefan Pröll
1
and
Andreas Rauber
2
Affiliations:
1
SBA Research, Austria
;
2
SBA Research and Technical University of Vienna, Austria
Keyword(s):
Dynamic Data Citation, Relational Databases, SQL, Persistent Identifiers.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Business Analytics
;
Change Detection
;
Data Curation
;
Data Engineering
;
Data Management and Quality
;
Databases and Data Security
;
Databases and Information Systems Integration
;
Digital Libraries
;
Enterprise Information Systems
;
Informatics in Control, Automation and Robotics
;
Information Quality
;
Knowledge Management and Information Sharing
;
Knowledge-Based Systems
;
Large Scale Databases
;
Modeling and Managing Large Data Systems
;
Ontologies and the Semantic Web
;
Signal Processing, Sensors, Systems Modeling and Control
;
Symbolic Systems
;
Web Information Systems and Technologies
;
Web Interfaces and Applications
Abstract:
Data forms the basis for research publications. But still the focus of researchers is a paper based publication, data is rather seen as a supplement that could be offered as a download, often without further comments. Yet validation, verification, reproduction and re-usage of existing knowledge can only be applied when the research data is accessible and identifiable. For this reason, precise data citation mechanisms are required, that allow reproducing experiments with exactly the same data basis. In this paper, we propose a model that enables to cite, identify and reference specific data sets within their dynamic environments. Our model allows the selection of subsets that support experiment verification and result re-utilisation in different contexts. The approach is based on assigning persistent identifiers to timestamped queries which are executed against time-stamped and versioned databases. This facilitates transparent implementation and scalable means to ensure identical resu
lt sets being delivered upon re-invocation of the query.
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