MetaData for Efficient, Secure and Extensible Access to Data in a Medical Grid

International audienceIn this paper we present the metadata usage in a medical imaging project grid. Metadata represent data about the data: In our case, the data are medical images and the metadata store relative information on the patient and hospital records, or even data about the image algorithms used in our application platform. Metadata are either static or dynamically constructed after computations on data. We show how the metadata is used, produced and stored to provide a secure and efficient access to medical data (and metadata) through a dedicated architecture. Experiments include times to access data and to secure the transactions

MetaData for Efficient, Secure and Extensible Access to Data in a Medical Grid

International audienceIn this paper we present the metadata usage in a medical imaging project grid. Metadata represent data about the data: In our case, the data are medical images and the metadata store relative information on the patient and hospital records, or even data about the image algorithms used in our application platform. Metadata are either static or dynamically constructed after computations on data. We show how the metadata is used, produced and stored to provide a secure and efficient access to medical data (and metadata) through a dedicated architecture. Experiments include times to access data and to secure the transactions

Context-awareness for adaptive information retrieval systems

Philosophiae Doctor - PhDThis research study investigates optimization of IRS to individual information needs in order of relevance. The research addressed development of algorithms that optimize the ranking of documents retrieved from IRS. In this thesis, we present two aspects of context-awareness in IR. Firstly, the design of context of information. The context of a query determines retrieved information relevance. Thus, executing the same query in diverse contexts often leads to diverse result rankings. Secondly, the relevant context aspects should be incorporated in a way that supports the knowledge domain representing users’ interests. In this thesis, the use of evolutionary algorithms is incorporated to improve the effectiveness of IRS. A context-based information retrieval system is developed whose retrieval effectiveness is evaluated using precision and recall metrics. The results demonstrate how to use attributes from user interaction behaviour to improve the IR effectivenes