Section: New Results

Big Data Integration

In this context, we have started the design of a Cloud Multi-datastore Query Language (CloudMdsQL), and its query engine. CloudMdsQL is a functional SQL-like language, capable of querying multiple heterogeneous data stores, e.g. relational, NoSQL or HDFS) [19] , [31] . The major innovation is that a CloudMdsQL query can exploit the full power of the local data stores, by simply allowing some local data store native queries to be called as functions, and at the same time be optimized. Our experimental validation, with three data stores (graph, document and relational) and representative queries, shows that CloudMdsQL satisfies the five important requirements for a cloud multidatastore query language. In [32] , we extend CloudMdsQL to allowing the ad-hoc usage of user defined map/filter/reduce operators in combination with traditional SQL statements, to integrate relational data and big data stored in HDFS and accessed by a data processing framework like Spark.

The AgroPortal project [49] aims at developing and supporting a reference ontology repository for the agronomic domain. The ontology portal features ontology hosting, search, versioning, visualization, comment, with services for semantically annotating data with the ontologies, as well as storing and exploiting ontology alignments and data annotations. All of these within a fully semantic web compliant infrastructure. The main objective of this project is to enable straightforward use of agronomic related ontologies, avoiding data managers and researchers the burden to deal with complex knowledge engineering issues to annotate the research data. Thus, we specifically pay attention to the requirements of the agronomic community and the specificities of the crop domain. AgroPortal will offer a robust and stable platform that we anticipate will be highly valued by the community.

The volumes of molecular biology data available on the web are constantly increasing. Accessing and integrating these data is crucial for making progress in biology. In [26] , we provide all the necessary pointers to identify the reference databases capable of providing bioinformatic data for molecular biology. We also discuss the problems posed by the exploitation of these very highly heterogeneous and distributed data. Finally, in order to guide a prospective user on the choice of one of these systems, we provide an overview of the systems that provide unified access to these data.