Ophidia: Toward Big Data Analytics for eScience

Fiore, Sandro; D'Anca, Alessandro; Palazzo, Cosimo; Foster, Ian; Williams, D. N.; Aloisio, Giovanni

doi:10.1016/j.procs.2013.05.409

Cited by 49 publications

(24 citation statements)

References 10 publications

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“…operational processing/analysis chains) at the server level. More details about the Ophidia architectural design and the infrastructural implementation can be found here [17,19]. With regard to the state of the art, SciDB [25] and Rasdaman [11] are key projects that fall in the same research area of Ophidia, but with some key differences.…”

Section: Ophidia Analytics Stack and Indigo Extensionsmentioning

confidence: 99%

Two-level Dynamic Workflow Orchestration in the INDIGO DataCloud for Large-scale, Climate Change Data Analytics Experiments

Pciennik

Fiore

Donvito

et al. 2016

Procedia Computer Science

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In this paper we present the approach proposed by EU H2020 INDIGO-DataCloud project to orchestrate dynamic workflows over a cloud environment. The main focus of the project is on the development of open source Platform as a Service solutions targeted at scientific communities, deployable on multiple hardware platforms, and provisioned over hybrid e-Infrastructures. The project is addressing many challenging gaps in current cloud solutions, responding to specific requirements coming from scientific communities including Life Sciences, Physical Sciences and Astronomy, Social Sciences and Humanities, and Environmental Sciences. We are presenting the ongoing work on implementing the whole software chain on the Infrastructure as a Service, PaaS and Software as a Service layers, focusing on the scenarios involving scientific workflows and big data analytics frameworks. INDIGO module for Kepler worflow system has been introduced along with the INDIGO underlying services exploited by the workflow components. A climate change data analytics experiment use case regarding the precipitation trend analysis on CMIP5 data is described, that makes use of Kepler and big data analytics services.

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Section: Ophidia Analytics Stack and Indigo Extensionsmentioning

confidence: 99%

Two-level Dynamic Workflow Orchestration in the INDIGO DataCloud for Large-scale, Climate Change Data Analytics Experiments

Pciennik

Fiore

Donvito

et al. 2016

Procedia Computer Science

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“…Ophidia system is one of the systems enabling the parallel processing of data analysis [23]. Ophidia acts as a big data analytics in electronic science sphere.…”

Section: Analysis Of Studies On Big Data Analyticsmentioning

confidence: 99%

The Investigation of the Opportunities of Big Data Analytics as Analytics-as-a-Service in Cloud Computing for Oil and Gas Industry

Alıguliyev¹,

İmamverdiyev²,

Abdullayeva³

2016

JPIT

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“…The Data Analytics platform has been built to facilitate the efficient and effective ingestion, analysis, transformation and publication of data related to the climate change domain and, in particular, to the fire danger prevention one. The core architecture is based on the Ophidia analytics framework ( [36], [37]). …”

Section: Data Platform Architecturementioning

confidence: 99%

The OFIDIA Fire Danger Rating System

Mirto¹,

Mariello²,

Nuzzo³

et al. 2015

2015 10th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC)

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Prevention is one of the most important stages in wildfire and other natural hazard management. Fire Danger Rating Systems (FDRSs) have been adopted by many countries to enhance wildfire prevention and suppression planning. With the aim to provide real-time fire danger forecasts and finer-scale fire behaviour analysis, an operational fire danger prevention platform has been developed within the OFIDIA project (Operational FIre Danger preventIon plAtform). The OFIDIA Fire Danger Rating System platform consists of (1) a data archive for managing weather forecasting and wireless sensors data, (2) a data analytics platform for post-processing weather data and for computing fire danger indices, and (3) a web application system for the visualization of weather and fire index maps and related timeseries. The OFIDIA platform is also connected to a Wireless Sensor Network (WSN) that gathers data from several sites in the Apulia (Italy) and Epirus (Greece) regions. The WSN is made by a primary station and several wireless sensors dislocated in wooded areas; the data acquisition process relates to variables like air temperature, relative humidity, wind speed and direction, precipitation, solar radiation, and fuel moisture.

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Ophidia: Toward Big Data Analytics for eScience

Cited by 49 publications

References 10 publications

Two-level Dynamic Workflow Orchestration in the INDIGO DataCloud for Large-scale, Climate Change Data Analytics Experiments

Two-level Dynamic Workflow Orchestration in the INDIGO DataCloud for Large-scale, Climate Change Data Analytics Experiments

The Investigation of the Opportunities of Big Data Analytics as Analytics-as-a-Service in Cloud Computing for Oil and Gas Industry

The OFIDIA Fire Danger Rating System

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