A preliminary investigation into SPARQL query complexity and federation in Bio2RDF When users query a SPARQL endpoint, they normally face an empty text box in which they have to write the desired queries. This obstructs the process of obtaining the data they want, since users rarely have any assistance in querying a (possibly huge) RDF database. In this paper we report a deep analysis of the server log files that record the queries that users send to the SPARQL endpoints, focusing in the Bio2RDF cluster. This log analysis reveals the large number of repeated queries that users submit, and how they pursue a trial and error process by adding and removing operations from the submitted queries to obtain the desired results. We also show how users try to connect to other RDF datasets in the Linked Open Data cloud. Our results offer insight into the interaction between users and a schemalight RDF dataset, and secondly, suggest improvements to SPARQL server optimizations in terms of optimization and results caching.

Data Provenance Management of Bioinformatics Workflows in Federated Clouds In Bioinformatics, the reproducibility of experiments is a fundamental principle to which the data provenance significantly contributes through the acquisition and management of information about the trajectory of the data in a workflow. In addition to the data provenance, aspects such as program configurations and the entire computational environment must be considered to achieve this goal. Cloud computing can provide computational resources, hiding technical details and providing an accessible and configurable ondemand environment for researchers. Cloud federation enables the broad distribution of services and a flexible combination of computing power. Considering this particular scenario, we propose a management platform that collects the provenance data of Bioinformatics workflows allowing portability between different clouds in federated clouds using the Infrastructure as a Service model. This platform is composed of tools that support running these workflows with data provenance captured in NoSQL databases. The retrospective data provenance is captured according to the PROVDM norms. The findings indicate that the proposed platform can be used for some of the most commoninly available clouds.