PubMed 2009

Mouillet, Évelyne

doi:10.1684/san.2008.0136

Cited by 1 publication

(1 citation statement)

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“…This example scenario is based on the PubMed article 19770275, so the Scenario:sourceArticle property was used to connect the scenario object to the Bio2RDF_pubmed:19770275 article (Figure 4 , node 16). This indirectly links MESH terms [ 23 ], GO terms [ 24 ], PubMed articles [ 22 ] and similar entities already connected via the Bio2RDF project. These terms and articles can be used to find scenarios that are related.…”

Section: Resultsmentioning

confidence: 99%

Scenario driven data modelling: a method for integrating diverse sources of data and data streams

et al. 2011

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BackgroundBiology is rapidly becoming a data intensive, data-driven science. It is essential that data is represented and connected in ways that best represent its full conceptual content and allows both automated integration and data driven decision-making. Recent advancements in distributed multi-relational directed graphs, implemented in the form of the Semantic Web make it possible to deal with complicated heterogeneous data in new and interesting ways.ResultsThis paper presents a new approach, scenario driven data modelling (SDDM), that integrates multi-relational directed graphs with data streams. SDDM can be applied to virtually any data integration challenge with widely divergent types of data and data streams. In this work, we explored integrating genetics data with reports from traditional media. SDDM was applied to the New Delhi metallo-beta-lactamase gene (NDM-1), an emerging global health threat. The SDDM process constructed a scenario, created a RDF multi-relational directed graph that linked diverse types of data to the Semantic Web, implemented RDF conversion tools (RDFizers) to bring content into the Sematic Web, identified data streams and analytical routines to analyse those streams, and identified user requirements and graph traversals to meet end-user requirements.ConclusionsWe provided an example where SDDM was applied to a complex data integration challenge. The process created a model of the emerging NDM-1 health threat, identified and filled gaps in that model, and constructed reliable software that monitored data streams based on the scenario derived multi-relational directed graph. The SDDM process significantly reduced the software requirements phase by letting the scenario and resulting multi-relational directed graph define what is possible and then set the scope of the user requirements. Approaches like SDDM will be critical to the future of data intensive, data-driven science because they automate the process of converting massive data streams into usable knowledge.

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Section: Resultsmentioning

confidence: 99%