FishGraph: A Network-Driven Data Analysis

There are a lot of data about biodiversity stored in different database models and most of them are relational. Recent research shows the importance of links and network analysis to discover knowledge in existing data. However, the relational model was not designed to address problems in which the links between data have the same importance as the data -- a common scenario in the biodiversity area. Moreover, the Linked Data and Semantic Web efforts empowered the fast growth of open knowledge repositories on the web, mainly in the RDF (Resource Description Framework) graph model. The flexible graph database model contrasts with the rigid relational model and is also suitable for data analysis focusing on links and the network topology, e.g., a connected component analysis. Our research is inspired by the data OLAP (OnLine Analytical Processing) approach of creating a special database designed for data analysis, a network-driven data analysis using graph databases, in our case. Beyond an initial ETL (Extract, Transform and Load) approach, we are facing the challenge of migrating the data from the relational to the graph database, managing a dynamic coexistence and evolution of both, not supported by related work. This work is motivated by a joint research involving network-driven data analysis over the FishBase global information system. We present a novel approach to analyzing the connections among thousands of identification keys and species and to linking local data to third party knowledge bases on the web.