Big Data Analytics for Earth Sciences: the EarthServer approach

Big Data Analytics is an emerging field since massive storage and computing capabilities have been made available by advanced e-infrastructures. Earth and Environmental sciences are likely to benefit from Big Data Analytics techniques supporting the processing of the large number of Earth Observation datasets currently acquired and generated through observations and simulations. However, Earth Science data and applications present specificities in terms of relevance of the geospatial information, wide heterogeneity of data models and formats, and complexity of processing. Therefore, Big Earth Data Analytics requires specifically tailored techniques and tools. The EarthServer Big Earth Data Analytics engine offers a solution for coverage-type datasets, built around a high performance array database technology, and the adoption and enhancement of standards for service interaction (OGC WCS and WCPS). The EarthServer solution, led by the collection of requirements from scientific communities and international initiatives, provides a holistic approach that ranges from query languages and scalability up to mobile access and visualization. The result is demonstrated and validated through the development of lighthouse applications in the Marine, Geology, Atmospheric, Planetary and Cryospheric science domains

The importance of disease associations and concomitant therapy for the long-term management of psoriasis patients

It is well established that several inflammatory-type conditions, such as arthritis, diabetes, cardiovascular disease, and irritable bowel disease exist comorbidly and at an increased incidence in patients with psoriasis. Psoriasis and other associated diseases are thought to share common inflammatory pathways. Conditions such as these, with similar pathogenic mechanisms involving cytokine dysregulation, are referred to as immune-mediated inflammatory diseases (IMIDs). Considerable evidence for the genetic basis of cormobidities in psoriasis exists. The WHO has reported that the occurrence of chronic diseases, including IMIDs, are a rising global burden. In addition, conditions linked with psoriasis have been associated with increasing rates of considerable morbidity and mortality. The presence of comorbid conditions in psoriasis patients has important implications for clinical management. QoL, direct health care expenditures and pharmacokinetics of concomitant therapies are impacted by the presence of comorbid conditions. For example, methotrexate is contraindicated in hepatic impairment, while patients on ciclosporin should be monitored for kidney function. In addition, some agents, such as beta blockers, lithium, synthetic antimalarial drugs, NSAIDs and tetracycline antibiotics, have been implicated in the initiation or exacerbation of psoriasis. Consequently, collaboration between physicians in different specialties is essential to ensuring that psoriasis treatment benefits the patient without exacerbating associated conditions

TEMPORAL ANALYSIS OF ATMOSPHERIC DATA USING OPEN STANDARDS

The continuous growth of remotely sensed data raises the need for efficient ways of accessing data archives. The classical model of accessing remote sensing (satellite) archives via distribution of large files is increasingly making way for a more dynamic and interactive data service. A challenge, though, is interoperability of such services, in particular when multi-dimensional data and advanced processing are involved. Individually crafted service interfaces typically do not allow substitution and combination of services. Open standards can provide a way forward if they are powerful enough to address both data and processing model. The OGC Web Coverage Service (WCS) is a modular service suite which provides high-level interface definitions for data access, subsetting, filtering, and processing of spatio-temporal raster data. WCS based service interfaces to data archives deliver data in their original semantics useful for further client-side processing, as opposed to the Web Map Service (WMS) (de la Beaujardière, 2006) which performs a pre-rendering into images only useful for display to humans. In this paper we present a case study where the OGC coverage data and service model defines the client/server interface for a climate data service. In particular, we show how flexible temporal analysis can be performed efficiently on massive spatio-temporal coverage objects. This service, which is operational on a several Terabyte data holding, has been established as part of the EarthServer initiative focusing on Big Data in the Earth and Planetary sciences

Validation of PM MAPPER Aerosol Optical Thickness Retrievals at 1x1Km2 of Spatial Resolution

The polar-orbiting MODerate resolution Imaging Spectrora- diometer (MODIS) on-board Terra and Aqua satellites is a key instru- ment for the daily monitoring of global aerosol properties over a large spectral range. Its aerosol retrieval algorithm is set to a size of 10×10 km2 of spatial resolution, and hence may not be adequate for detailed analy- sis at local scale. PM MAPPER is a software system capable of handling the multispectral data acquired by the MODIS sensors. It produces maps of Aerosol Optical Thickness (AOT) at increased spatial resolution up to 1×1 km2 , which are then available online in a GIS environment. This article shows the validation results of these products, obtained by com- parison with AOT measurements of several ground-based radiometers of the AErosol RObotic NETwork (AERONET) over Europe, for a period of 3 years (2007-2009). They show a good correlation between satellite prod- ucts and ground measurements. Different sizes of the spatio-temporal window to associate satellite and ground observations have been tested, and trends have been searched by tuning the comparisons for different years, seasons and land-cover classes. An optimal spatio-temporal win- dow for this kind of validation is also suggested. This could be used for other purposes as well, e.g. to perform improvements of AOT retrieval algorithm with machine learning techniques

On the Automatic Prediction of PM10 with In-Situ Measurements, Satellite AOT Retrievals and Ancillary Data

Abstract: Daily monitoring of unhealthy particles suspended in the low troposphere is of major concern around the world, and ground- based measuring stations represent a reliable but still inadequate means for a full spatial coverage assessment. Advances in satellite sensors have provided new datasets and though less precise than in- situ observations, they can be combined altogether to enhance the prediction of particulate matter. In this article we evaluate a method- ology for automatic multi-variate estimation of PM10 dry mass con- centrations along with a comparison of three different cokriging es- timators, which integrate ground measurements of PM10 , satellite MODIS-derived retrievals of aerosols optical thickness and further auxiliary data. Results highlight the need for further improvements and studies. The analysis employs the available data in 2007 over the Emilia Romagna region (Padana Plain, Northern Italy), where stag- nant meteorological conditions further urge for a comprehensive air quality monitoring. Qualitative PM10 full maps of Emilia Romagna are then automatically yielded on-line in a dynamic GIS environment for multi-temporal analysis on air quality

PlanetServer: Innovative approaches for the online analysis of hyperspectral satellite data from Mars

International audienc

The influence of prosthesis size and design on exercise dynamics after aortic valve replacement

Background and aim of the study: Residual gradient following aortic valve replacement (AVR) may adversely affect clinical outcome. The size and design of the valve may influence these characteristics. The study aim was to determine the influence of prosthesis physical size and leaflet design on hemodynamic performance after mechanical AVR. Methods: After AVR, two patient groups with a range of valve sizes were studied. Group 1 patients (n = 19) each received a monoleaflet valve; group 2 patients (n = 18) each received a bileaflet valve. Transthoracic echocardiography was performed at rest and after graded bicycle ergometry to assess prosthetic valve parameters, including mean and peak transvalvular gradient and effective orifice area (EOA). Results: Transprosthetic gradients (mean and peak) measured at rest, maximum exercise and 3- min recovery were related to indexed geometric orifice area (IGOA) by an exponential decay function, with no significant advantage for either valve design. However, in valve sizes ≤25 mm the bileaflet valves demonstrated lower gradients, both at rest and under exercise conditions (mean gradient during exercise, bileaflet versus monoleaflet 19.9 ± 7.2 mmHg versus 25.6 ± 6.3 mmHg, p = 0.01). Similarly, EOAs were larger in the bileaflet group when equivalent GOAs ≤2.5 cm2 were compared (EOA: bileaflet versus monoleaflet 1.51 ± 0.33 cm2 versus 1.14 ± 0.26 cm2, p = 0.018). The total work performed correlated with prosthesis diameter (r2 = 0.81, p = 0.037) and was not influenced by valve design. Conclusion: The hemodynamic performance of mechanical aortic valves, including transprosthetic gradient and maximum exercise work performed, related principally to the prosthesis physical size. However, within the smaller valve sizes, the bileaflet design appeared to offer hemodynamic advantages

Big Data Analytics for Earth Sciences: the EarthServer approach

Big Data Analytics is an emerging field since massive storage and computing capabilities have been made available by advanced e-infrastructures. Earth and Environmental sciences are likely to benefit from Big Data Analytics techniques supporting the processing of the large number of Earth Observation datasets currently acquired and generated through observations and simulations. However, Earth Science data and applications present specificities in terms of relevance of the geospatial information, wide heterogeneity of data models and formats, and complexity of processing. Therefore, Big Earth Data Analytics requires specifically tailored techniques and tools. The EarthServer Big Earth Data Analytics engine offers a solution for coverage-type datasets, built around a high performance array database technology, and the adoption and enhancement of standards for service interaction (OGC WCS and WCPS). The EarthServer solution, led by the collection of requirements from scientific communities and international initiatives, provides a holistic approach that ranges from query languages and scalability up to mobile access and visualization. The result is demonstrated and validated through the development of lighthouse applications in the Marine, Geology, Atmospheric, Planetary and Cryospheric science domains