The TSIMMIS Approach to Mediation: Data Models and Languages
Journal of Intelligent Information Systems - Special issue: next generation information technologies and systems
The essential guide to data warehousing
The essential guide to data warehousing
On wrapping query languages and efficient XML integration
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
RQL: a declarative query language for RDF
Proceedings of the 11th international conference on World Wide Web
A Statistical Metadata Model for Simultaneous Manipulation of both Data and Metadata
Journal of Intelligent Information Systems
Cell Suppression Methodology: The Importance of Suppressing Marginal Totals
IEEE Transactions on Knowledge and Data Engineering
An Integrated Metadata Model for Statistical Data Collection and Processing
SSDBM '04 Proceedings of the 16th International Conference on Scientific and Statistical Database Management
International Journal on Digital Libraries
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Journal of Biomedical Informatics
EBM Metadata Based on Dublin Core Better Presenting Validity of Clinical Trials
Journal of Medical Systems
Management of Data in Clinical Trials
Management of Data in Clinical Trials
Pivoting approaches for bulk extraction of Entity-Attribute-Value data
Computer Methods and Programs in Biomedicine
QAV: querying entity-attribute-value metadata in a biomedical database
Computer Methods and Programs in Biomedicine
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We introduce a statistical, process-oriented metadata model to describe the process of medical research data collection, management, results analysis and dissemination. Our approach explicitly provides a structure for pieces of information used in Clinical Study Data Management Systems, enabling a more active role for any associated metadata. Using the object-oriented paradigm, we describe the classes of our model that participate during the design of a clinical trial and the subsequent collection and management of the relevant data. The advantage of our approach is that we focus on presenting the structural inter-relation of these classes when used during datasets manipulation by proposing certain transformations that model the simultaneous processing of both data and metadata. Our solution reduces the possibility of human errors and allows for the tracking of all changes made during datasets lifecycle. The explicit modeling of processing steps improves data quality and assists in the problem of handling data collected in different clinical trials. The case study illustrates the applicability of the proposed framework demonstrating conceptually the simultaneous handling of datasets collected during two randomized clinical studies. Finally, we provide the main considerations for implementing the proposed framework into a modern Metadata-enabled Information System.