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Antonacci, Marica
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Antonacci, Marica
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- PublicationOpen AccessAn EMSO data case study within the INDIGO-DC project(2017-04-25)
; ; ; ; ; ; ; ; ; ; ; ; ; ;; We present our experience based on a case study within the INDIGO-DataCloud (INtegrating Distributed data In-frastructures for Global ExplOitation) project (www.indigo-datacloud.eu). The aim of INDIGO-DC is to develop a data and computing platform targeting scientific communities. Our case study is an example of activities performed by INGV using data from seafloor observatories that are nodes of the infrastructure EMSO (European Multidisciplinary Seafloor and water column Observatory)-ERIC (www.emso-eu.org). EMSO is composed of several deep-seafloor and water column observatories, deployed at key sites in the European waters, thus forming a widely distributed pan-European infrastructure. In our case study we consider data collected by the NEMO-SN1 observatory, one of the EMSO nodes used for geohazard monitoring, located in the Western Ionian Sea in proximity of Etna volcano. Starting from the case study, through an agile approach, we defined some requirements for INDIGO developers, and tested some of the proposed INDIGO solutions that are of interest for our research community. Given that EMSO is a distributed infrastructure, we are interested in INDIGO solutions that allow access to distributed data storage. Access should be both user-oriented and machine-oriented, and with the use of a common identity and access system. For this purpose, we have been testing: - ONEDATA (https://onedata.org), as global data management system. - INDIGO-IAM as Identity and Access Management system. Another aspect we are interested in is the efficient data processing, and we have focused on two types of INDIGO products: - Ophidia (http://ophidia.cmcc.it), a big data analytics framework for eScience for the analysis of multidimensional data. - A collection of INDIGO Services to run processes for scientific computing through the INDIGO Orchestra175 323 - PublicationOpen AccessINDIGO-DATA CLOUD EC project: A study case applied to one of the EMSO Research Infrastructure Deep sea Observatories(2016-09-28)
; ; ; ; ; ; ; ; ;Monna, S.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Roma2, Roma, Italia ;Marcucci, N.M.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Roma2, Roma, Italia ;Marinaro, G.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Roma2, Roma, Italia ;Rossi, M.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione CNT, Roma, Italia ;Fiore, S.; CMCC Foundation, Lecce ;Antonacci, M.; INFN, Bari ;Beranzoli, L.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Roma2, Roma, Italia ;Favali, P.; Istituto Nazionale di Geofisica e Vulcanologia, Sezione Roma2, Roma, Italia; ; ; ; ; ;; Our case study is a pilot experience used to describe some of the activities performed by INGV in the frame of the European Research Infrastructure EMSO (European Multidisciplinary Seafloor and water column Observatory). EMSO is composed of several deep-seafloor and water column observatories, deployed at key sites in the European waters, thus forming a widely distributed pan-European infrastructure. We consider data collected by the NEMO-SN1 observatory, one of the EMSO nodes used for geohazard monitoring, located in the Western Ionian Sea in proximity of Etna volcano. In this poster we will focus on the Researcher and Data Manager user-types. The INGV EMSO community uses MOIST (Multidisciplinary Oceanic Information System) for storing and visualizing data and metadata produced by NEMO-SN1 Observatory. Data quality control and analysis often requires several steps that include the use of different scripts and software developed in-house, commercials tools (Matlab, R-Studio....), and proprietary tools available from sensor manufacturers. In this chain of events, some operations might require a relevant computing power. Data are retrieved from MOIST through remote mount (via samba or sshfs). Analysis might also be performed on datasets that are produced by other partners and remote access and sharing of these data is needed. At present, in the majority of cases, software is run on individual researchers’ Pcs. The first test for the implementation of our use-case within INDIGO-DataCloud included running an R script on a cloud environment and exploiting data sharing capabilities. The input to this script is data coming from the analysis of Short Duration seismic Events (SDE) automatically detected on the seismometer continuous time series. The script calculates a cumulate energy in the measurement period (8 months) and compares this cumulate curve to N random cumulates calculated by mixing the energy values at the fixed observed times where SDE are detected. Within the INDIGO project (WP2) we defined the users requirements and we identified some useful INDIGO solutions. In particular we are testing Ophidia, a software stack for big data analytics (Fiore et al., 2013), and the execution of R jobs in docker containers described by TOSCA templates through INDIGO Orchestrator and Apache Mesos. This poster illustrates our case study, the users’ requirements and the INDIGO solutions that we have been testing so far and would like to test in the near future.205 179