1. INGV-MOIST Case Study and INDIGO solution for:
Presented by Nicola M. Marcucci
INGV, Istituto Nazionale di Geofisica e Vulcanologia
A. D’anca, S. Monna, G. Marinaro, S. Fiore
INDIGO SUMMIT
RIA
EGI-INDIGO workshop on community application support Catania, 10th May 2017

2. INDIGO CASE STUDY: P9 – INGV-MOIST
INGV SIIAM - Integrated Systems for Marine Environmental Infrastructures
WHO:
Our INDIGO working group belongs to a INGV research unit.
We study geophysical and environmental phenomena in marine areas through a multidisciplinary approach. We mainly focus on areas in the Mediterranean sea that show a relevant seismic and volcanic activity.
AIM:
The quality of long term measurements, heavily depends on the data acquisition and processing system.
The SN1 observatory
INDIGO CASE STUDY: P9 – INGV-MOIST

3. INDIGO CASE STUDY: P9 – INGV-MOIST
INGV-MOIST Case Study
Our CS is based on activities performed by INGV on data collected by NEMO-SN1 seafloor observatory, a node of EMSO (European Multidisciplinary Seafloor and water column Observatory) network.
Data processing
Identity and Access Management
Areas of
interest
Data management
We identified three areas of interest that can benefit from INDIGO components.
NEMO-SN1
INDIGO CASE STUDY: P9 – INGV-MOIST

4. Ophidia Starting situation: Evolution: Ophidia
Researchers use several data processing tools for data analysis (SAC, R studio, Matlab) running on single PC .
SAC (Seismic Analysis Code)
Is a general purpose interactive program designed for the study of sequential signals, especially time series data.
Evolution:
Some analysis might require great computational power and could benefit from INDIGO services.
We focused mainly on Ophidia.
Ophidia
Is a big data analytics framework for eScience for the analysis of multidimensional data.

7. Ophidia first step: development of SAC IMPORT operator
C library for reading/writing
SAC data format
IMPORTSAC Operator
Diagram of the experiment implementation with
Ophidia-seismic event detection algorithm
Produce
Integration
Branch on github

8. Better Software for Better Science.
Short Video Time
Gdrive
Dropbox
Better Software for Better Science.

9. Conclusion and future steps
With this simple but useful example, We found that Ophidia offers
an added value in terms of performance.
Our next step will be to extend the application of ophidia to algorithms of interest to our community, for both data integrity testing and research.
Better Software for Better Science.

10. Thanks for your attention and now, enjoy your meal! 
Contacts:
Stephen Monna
Nicola M. Marcucci
Better Software for Better Science.