Graph Management to Improve Querying of Health and Social Data

Autor: Martha Millan, Claudia Roncancio, María Constanza Pabón
Přispěvatelé: Laboratoire d'Informatique de Grenoble (LIG), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF), Systèmes d’Information - inGénierie et Modélisation Adaptables (SIGMA), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)
Rok vydání: 2014
Předmět:
Zdroj: HEALTHINF
Actes de 7th. International conference on Health informatics, Healthinf
7th. International conference on Health informatics, Healthinf
7th. International conference on Health informatics, Healthinf, Mar 2014, France. pp.1
Scopus-Elsevier
DOI: 10.5220/0004805403430350
Popis: International audience; Large amount of data related to health care are stored in heterogeneous data sources. Independently of health information systems, social media provides information about people's environment and activities, such as family relationships or patient's habits and social interaction. This information could be used to complement patients medical profiles, in order to improve patient's care. Providing expert users with mechanisms to integrate and query such sources is crucial to retrieve useful information to analyze patient's situations. This work contributes to facilitating visualization and querying of data coming from all such sources. We adopt a graph data model at the conceptual level as it facilitates the integration of structured and semi-structured data. Such a model also allows to represent complex relations between data and offers good possibilities to link data. Our purpose is to go a step forward by providing a conceptual query language intended to allow end users, medical domain experts, to retrieve data from heterogeneous data sources by ad hoc queries. In this paper we introduce a set of operators to query data by transforming a graph and we analyze how they fulfill some design features of the conceptual language. These operators allow successive graph transformation to generate sub-graphs with filtered data and to derive new relations representing information that is implicit or that is sparse in the data. The operators have been implemented as part of the query engine in DIG, a mediation based data integration system.
Databáze: OpenAIRE
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