Analysis on the Graph Techniques for Data-mining and Visualization of Heterogeneous Biodiversity Data Sets

Abstract

International audienceExtisting biodiversity databases contain an abundance of information. To turn such information into knowledge , it is necessary to address several information-model issues. Biodiversity data are collected for various scientific objectives, often even without clear preliminary objectives, may follow different taxonomy standards and organization logic, and be held in multiple file formats and utilising a variety of database technologies. This paper presents a graph catalogue model for the metadata management of biodiversity databases. It explores the possible operation of data mining and visualization to guide the analysis of heterogeneous biodiversity data. In particular, we would propose contributions to the problems of (1) the analysis of heterogeneous distributed data found across different databases, (2) the identification of matches and approximations between data sets, and (3) the identificaton of relationships between various databases. This paper describes a proof of concept of an infrastructure testbed and its basic operations, presenting an evaluation of the resulting system in comparison with the ideal expectations of the ecologist

IndexMEED cases studies using "Omics" data with graph theory

“…Scientific questions can be resolved by the new data mining approaches that offer new ways to investigate heterogeneous environmental data with graph mining (Muñoz et al 2017). The uses of data from biodiversity research demonstrate the prototype functionalities (David et al 2016) and introduce new perspectives to analyze environmental and societal responses including decision-making at large scale, both at the information system level and the observing system level than at the observed system level.…”