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The analysis of large volumes of data is key for knowledge discovery in several scientific domains such as climate, astrophysics, life sciences among others. It requires a large set of computational and storage resources, as well as flexible and efficient software solutions able to dynamically exploit the available infrastructure and address issues related to data volume, distribution, velocity and heterogeneity. This paper presents a data-driven and cloud-based use case implemented in the context of the EUBrazilCC project for the analysis of climate change and biodiversity data. The use case architecture and main components, as well as a Platform as a Service (PaaS) framework for big data analytics named PDAS, together with its elastic deployment in the EUBrazilCC federated cloud infrastructure are presented and discussed in detail.

We are currently experiencing the fourth industrial revolution. This is what the German government initiative, first, has identified with ‘Industry 4.0’. The manufacturing future will be marked and will go through the new automation technologies that are being introduced with Industrial Internet of Things (I2oT). Industrial Control Systems (ICSs) are exploiting I2oT for reducing costs and improving efficiency. However, ICSs are already jeopardized by an increasingly large set of threat vectors. Those threats are used by malicious actors to misuse physical Critical Infrastructures that usually are vital services for well-being. I2oT implementation increases the threat surface, generating new possible vulnerabilities. Information Technology (IT) classical approaches to cyber attacks cannot be applied to ICS due to their extreme differences from main priorities to resource constrains. Therefore, innovative approaches and equipment must be developed to suit with ICS world. In this paper, a Smart Behavioural Filter (SBF) for the PLCs (Programmable Logic Controllers) is proposed aiming to secure the PLC itself against logic attacks, that are stealth for other more classical security approaches. An example of the considered logic attacks is many open and close commands towards a valve in a short time. Those logic attacks are usually a sequence of well-formed packets in which the content represents an anomalous and unpredicted behaviour. This smart field equipment can react in short time to cyber attacks isolating the PLC, communicate with other equipment like itself and increasing in general the resilience of the physical system. It can also generate alarms for the local Intrusion Detection System (IDS). The proposed equipment has been developed and validated in a real test-bed within the FP7 CockpitCI project and H2020 ATENA project.

The continuous increase in the data produced by simulations, experiments and edge components in the last few years has forced a shift in the scientific research process, leading to the definition of a fourth paradigm in Science, concerning data-intensive computing. This data deluge, in fact, introduces various challenges related to big data volumes, formats heterogeneity and the speed in the data production and gathering that must be handled to effectively support scientific discovery. To this end, High Performance Computing (HPC) and data analytics are both considered as fundamental and complementary aspects of the scientific process and together contribute to a new paradigm encompassing the efforts from the two fields called High Performance Data Analytics (HPDA). In this context, the Ophidia project provides a HPDA framework which joins the HPC paradigm with scientific data analytics. This contribution presents some aspects regarding the Ophidia HPDA framework, such as the multidimensional storage model, its distributed and hierarchical implementation along with a benchmark of a parallel in-memory time series reduction operator.

Reducing energy consumption and CO\(_2\) emissions, maintaining an acceptable comfort level within a building, is a very complex problem that in the recent year has caught growing interest by the scientific community. The developed architecture integrates two different monitoring networks: the ‘Fixed Network’ and the ‘Custom Network.’ The ‘Fixed Network’ is an industrial control system able to collect data, control and monitor real-time consumptions through specific power modules plugged into an electrical cabinet. The ‘Custom Network’ is a system realized through low-cost commercial devices, located inside the building, to collect the continuous measurements for the internal comfort. Therefore, thanks to the use of those measuring instruments, it is possible to track both the energy consumptions and the internal comfort indicators that influence the assessment of the building model. Once data are collected, a software architecture has been designed for managing pseudo-real-time data flows, taking into account flexibility and speed of storage and retrieval. Such architecture can apply a pre-processing filtering phase and then analyzes relevant data recognizing anomalies like plant malfunction or users’ bad habits. The system is implemented in academic buildings of “Roma Tre” University, with some preliminary but important results.

Open Science is key to future scientific research and promotes a deep transformation in the whole scientific research process encouraging the adoption of transparent and collaborative scientific approaches aimed at knowledge sharing. Open Science is increasingly gaining attention in the current and future research agenda worldwide. To effectively address Open Science goals, besides Open Access to results and data, it is also paramount to provide tools or environments to support the whole research process, in particular the design, execution and sharing of transparent and reproducible experiments, including data provenance (or lineage) tracking. This work introduces the Climate Analytics-Hub, a new component on top of the Earth System Grid Federation (ESGF), which joins big data approaches and parallel computing paradigms to provide an Open Science environment for reproducible multi-model climate change data analytics experiments at scale. An operational implementation has been set up at the SuperComputing Centre of the Euro-Mediterranean Center on Climate Change, with the main goal of becoming a reference Open Science hub in the climate community regarding the multi-model analysis based on the Coupled Model Intercomparison Project (CMIP). This paper reports about some ESiWACE WP3 activities described in the deliverable D3.10 "ESiWACE Scheduler development and support activities"

A case study on climate models intercomparison data analysis addressing several classes of multi-model experiments is being implemented in the context of the EU H2020 INDIGO-DataCloud project. Such experiments require the availability of large amount of data (multi-terabyte order) related to the output of several climate models simulations as well as the exploitation of scientific data management tools for large-scale data analytics. More specifically, the paper discusses in detail a use case on precipitation trend analysis in terms of requirements, architectural design solution, and infrastructural implementation. The experiment has been tested and validated on CMIP5 datasets, in the context of a large scale distributed testbed across EU and US involving three ESGF sites (LLNL, ORNL, and CMCC) and one central orchestrator site (PSNC).

The availability of systems able to process and analyse big amount of data has boosted scientific advances in several fields. Workflows provide an effective tool to define and manage large sets of processing tasks. In the big data analytics area, the Ophidia project provides a cross-domain big data analytics framework for the analysis of scientific, multi-dimensional datasets. The framework exploits a server-side, declarative, parallel approach for data analysis and mining. It also features a complete workflow management system to support the execution of complex scientific data analysis, schedule tasks submission, manage operators dependencies and monitor jobs execution. The workflow management engine allows users to perform a coordinated execution of multiple data analytics operators (both single and massive - parameter sweep) in an effective manner. For the definition of the big data analytics workflow, a JSON schema has been properly designed and implemented. To aid the definition of the workflows, a visual design language consisting of several symbols, named Data Analytics Workflow Modelling Language (DAWML), has been also defined.

Recently, there has been much effort to integrate heterogeneous wireless technologies in order to make overlay networks able to provide mobile users with ubiquitous Internet access. Handover management is still one of the most challenging issues to be solved for seamless integration of wireless networks. In this paper, an algorithm for vertical handover with QoS guarantees between IEEE 802.16e and IEEE 802.11e networks, without computational complexity added on mobile nodes, has been proposed. It uses a decision mechanism based on the following metrics: the available bandwidth, the signal-to-noise ratio (SNR) and the access delay. Its characteristics are perfectly compatible with the guidelines specified in the IEEE 802.21 standard. The effectiveness of the defined algorithm has been evaluated, using the ns-2 tool, in terms of packet loss rate (PLR), satisfaction of minimum applications requirements and number of useless handovers. The performance of the defined algorithm has been compared with that obtained by using the algorithm suggested by IEEE 802.21 standard that exploits only the SNR in the decision procedure.

Telecommunication networks based on commonplace technologies (such as Ethernet) often constitute a vulnerable attack vector against modern critical infrastructures (CIs), particularly for supervisory control and data acquisition (SCADA) systems, which rely on them for monitoring and controlling physical components. This paper presents a unique platform that encompasses a range of capabilities, from cyber-attack detection to mitigation strategies, through interdependency and risk evaluation. The platform is made of two main components: a cyber-attack detection subsystem and a risk assessment framework. Both blocks are innovative from research point of view and they have been developed and customized to fit the CIs' features, that are completely different from telecommunication networks. This platform has been tested on a hybrid environment testbed, made of virtual and real components, within the scope of the EU FP7 CockpitCI and EU H2020 ATENA projects. The case study corresponds to a medium voltage power grid controlled by a SCADA control center, where the platform has been validated with optimal results in terms of detection capabilities and time response.