In this paper, We present how we combined visualization and machine learning techniques to provide an analytic tool for web log data.We designed a visualization where advertisers can observe the visits to their different pages on a site, common web analytic measures and individual user navigation on the site. In this visualization, the users can get insights of the data by looking at key elements of the graph. Additionally, we applied pattern mining techniques to observe common trends in user segments of interest.

Trough serum concentrations (Cmin) of gentamicin were followed during up to 96h of treatment in 44 neonates (17 preterm and 27 term), treated with intramuscular gentamicin 2.5 +/- 0.3 mg/kg (mean +/- SD) twice daily, a dosage that was not changed during the follow-up period. Relationships with patients' gestational age, postnatal age, postconceptional age and bodyweight were analysed to identify circumstances in which gentamicin should be monitored. Gentamicin Cmin values after 24h correlated better with neonate's postconceptional age (r = -0.42) or gestational age (r = -0.37) than with postnatal age or bodyweight. Correlations with postconceptional age and gestational age improved after 96h (r = -0.71 and r = -0.67, respectively). From 24 to 96h Cmin increased from 1.5 to 2 mg/L (p < 0.001) in the preterm neonates and from 1.5 to 2.5 mg/L (p < 0.01) in those preterm neonates < or = 32 weeks of gestational age, while differences between neonates < or = 3 days and > 3 days of postnatal age were nonsignificant. The Cmin at 24h was potentially toxic (> 2 mg/L) in 9% of the neonates (12% of preterm and 7% of term neonates). At 96h, the percentage of neonates with toxic Cmin values increased to 25% (65% of all preterm neonates and 100% of preterm neonates < or = 32 weeks of gestational age), whereas in term neonates it decreased to 0%. In conclusion, in preterm neonates < or = 32 weeks of gestational age a dosage of 2.5 mg/kg every 24h should be used, and gentamicin concentrations should be monitored. However, in term neonates > 7 days of postnatal age a dosage of 3.5 mg/kg twice daily should be recommended.

The development of data management services capable to cope with very large data resources is a key challenge to allow the future einfrastructures to address the needs of the next generation extreme scale scientific experiments. To face this challenge, in November 2017 the H2020 eXtreme DataCloud -XDC project has been launched. Lasting for 27 months and combining the expertise of eight large European research organisations, the project aims at developing scalable technologies for federating storage resources and managing data in highly distributed computing environments. The targeted platforms are the current and next generation e-Infrastructures deployed in Europe, such as the European Open Science Cloud (EOSC), the European Grid Infrastructure (EGI), and the Worldwide LHC Computing Grid (WLCG). The project is use-case driven with a multidisciplinary approach, addressing requirements from research communities belonging to a wide range of scientific domains: High Energy Physics, Astronomy, Photon and Life Science, Medical research. XDC is aimed at implementing data management scalable services, combining already established data management and orchestration tools, to address the following high level topics: policy driven data management based on Quality-of-Service, Data Life-cycle management, smart placement of data with caching mechanisms to reduce access latency, meta-data with no predefined schema handling, execution of pre-processing applications during ingestion, data management and protection of sensitive data in distributed e-infrastructures, intelligent data placement based on access patterns. This contribution introduces the project, presents the foreseen overall architecture and the developments that are being carried on to implement the requested functionalities.EPJ Web of Conferences 214, 04044 (2019)

The eXtreme DataCloud (XDC) project is aimed at developing data management services capable to cope with very large data resources allowing the future e-infrastructures to address the needs of the next generation extreme scale scientific experiments. Started in November 2017, XDC is combining the expertise of 8 large European research organisations. The project aims at developing scalable technologies for federating storage resources and managing data in highly distributed computing environments. The project is use case driven with a multidisciplinary approach, addressing requirements from research communities belonging to a wide range of scientific domains: Life Science, Biodiversity, Clinical Research, Astrophysics, High Energy Physics and Photon Science, that represent an indicator in terms of data management needs in Europe and worldwide. The use cases proposed by the different user communities are addressed integrating different data management services ready to manage an increasing volume of data. Different scalability and performance tests have been defined to show that the XDC services can be harmonized in different contexts and complex frameworks like the European Open Science Cloud. The use cases have been used to measure the success of the project and to prove that the developments fulfil the defined needs and satisfy the final users. The present contribution describes the results carried out from the adoption of the XDC solutions and provides a complete overview of the project achievements.