SMART: An Application Framework for Real Time Big Data Analysis on Heterogeneous Cloud Environments

International audienceThe amount of data that human activities generate poses a challenge to current computer systems. Big data processing techniques are evolving to address this challenge, with analysis increasingly being performed using cloud-based systems. Emerging services, however, require additional enhancements in order to ensure their applicability to highly dynamic and heterogeneous environments and facilitate their use by Small & Medium-sized Enterprises (SMEs). Observing this landscape in emerging computing system development, this work presents Small & Medium-sized Enterprise Data Analytic in Real Time (SMART) for addressing some of the issues in providing compute service solutions for SMEs. SMART offers a framework for efficient development of Big Data analysis services suitable to small and medium-sized organizations, considering very heterogeneous data sources, from wireless sensor networks to data warehouses, focusing on service composability for a number of domains. This paper presents the basis of this proposal and preliminary results on exploring application deployment on hybrid infrastructure

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Strategies for Big Data Analytics through Lambda Architectures in Volatile Environments

International audienceExpectations regarding the future growth of Internet of Things (IoT)-related technologies are high. These expectations require the realization of a sustainable general purpose application framework that is capable to handle these kinds of environments with their complexity in terms of heterogeneity and volatility. The paradigm of the Lambda architecture features key characteristics (such as robustness, fault tolerance, scalability, generalization, extensibility, ad-hoc queries, minimal maintenance, and low-latency reads and updates) to cope with this complexity. The paper at hand suggest a basic set of strategies to handle the arising challenges regarding the volatility, heterogeneity, and desired low latency execution by reducing the overall system timing (scheduling, execution, monitoring, and faults recovery) as well as possible faults (churn, no answers to executions). The proposed strategies make use of services such as migration, replication, MapReduce simulation, and combined processing methods (batch- and streaming-based). Via these services, a distribution of tasks for the best balance of computational resources is achieved, while monitoring and management can be performed asynchronously in the background. %An application of batch and stream-based methods are proposed to reduce the latency

Entropy to Mitigate Non-IID Data Problem on Federated Learning for the Edge Intelligence Environment

Machine Learning (ML) algorithms process input data making it possible to recognize and extract patterns from a large data volume. Likewise, Internet of Things (IoT) devices provide knowledge in a Federated Learning (FL) environment, sharing parameters without compromising their raw data. However, FL suffers from non-independent and identically distributed (non-iid) data, which means it is heterogeneous data and has biased input data, such as in smartphone data sources. This bias causes low convergence for ML algorithms and high energy and bandwidth consumption. This work proposes a method that mitigates non-iid data through a FedAvg-BE algorithm that provides Federated Learning with the border entropy evaluation to select quality data from each device by cross-device in a non-iid data environment. Extensive experiments were performed using publicly available datasets to train deep neural networks. The experiment result evaluation demonstrates that execution time saves up to 22% for the MNIST dataset and 26% for the CIFAR-10 dataset, with the proposed model in Federated Learning settings. The results demonstrate the feasibility of the proposed model to mitigate non-iid data impact

Multilayered artifacts in the pre-Columbian metallurgy from the North of Peru

Three types of alloys were recognized when analyzing pre-Columbian artifacts from the North of Peru: gold, silver, and copper alloys; gilded copper and silver; silvered copper; tumbaga, i.e., copper or silver enriched on gold at the surface by depletion gilding. In this paper, a method is described to differentiate gold alloys from gilded copper and from copper-gold tumbaga, and silver alloys from silvered copper and copper-silver tumbaga. This method is based on the use of energy-dispersive X-ray fluorescence, i.e., on a sophisticated analysis of XRF-spectra carrying out an accurate determination of Cu(K (alpha) /K (beta) ), Ag(K (alpha) /K (beta) ), Au(L (alpha) /L (beta) ), and Au-L (alpha) /Cu-K (alpha) or Ag-K (alpha) /Cu-K (alpha) ratios. That implies a dedicated software for the quantitative determination of the area of X-ray peaks. This method was first checked by a relevant number of standard samples and then it was applied to pre-Columbian alloys from the North of Peru

Geoeconomic variations in epidemiology, ventilation management, and outcomes in invasively ventilated intensive care unit patients without acute respiratory distress syndrome: a pooled analysis of four observational studies

Background: Geoeconomic variations in epidemiology, the practice of ventilation, and outcome in invasively ventilated intensive care unit (ICU) patients without acute respiratory distress syndrome (ARDS) remain unexplored. In this analysis we aim to address these gaps using individual patient data of four large observational studies. Methods: In this pooled analysis we harmonised individual patient data from the ERICC, LUNG SAFE, PRoVENT, and PRoVENT-iMiC prospective observational studies, which were conducted from June, 2011, to December, 2018, in 534 ICUs in 54 countries. We used the 2016 World Bank classification to define two geoeconomic regions: middle-income countries (MICs) and high-income countries (HICs). ARDS was defined according to the Berlin criteria. Descriptive statistics were used to compare patients in MICs versus HICs. The primary outcome was the use of low tidal volume ventilation (LTVV) for the first 3 days of mechanical ventilation. Secondary outcomes were key ventilation parameters (tidal volume size, positive end-expiratory pressure, fraction of inspired oxygen, peak pressure, plateau pressure, driving pressure, and respiratory rate), patient characteristics, the risk for and actual development of acute respiratory distress syndrome after the first day of ventilation, duration of ventilation, ICU length of stay, and ICU mortality. Findings: Of the 7608 patients included in the original studies, this analysis included 3852 patients without ARDS, of whom 2345 were from MICs and 1507 were from HICs. Patients in MICs were younger, shorter and with a slightly lower body-mass index, more often had diabetes and active cancer, but less often chronic obstructive pulmonary disease and heart failure than patients from HICs. Sequential organ failure assessment scores were similar in MICs and HICs. Use of LTVV in MICs and HICs was comparable (42·4% vs 44·2%; absolute difference -1·69 [-9·58 to 6·11] p=0·67; data available in 3174 [82%] of 3852 patients). The median applied positive end expiratory pressure was lower in MICs than in HICs (5 [IQR 5-8] vs 6 [5-8] cm H2O; p=0·0011). ICU mortality was higher in MICs than in HICs (30·5% vs 19·9%; p=0·0004; adjusted effect 16·41% [95% CI 9·52-23·52]; p<0·0001) and was inversely associated with gross domestic product (adjusted odds ratio for a US$10 000 increase per capita 0·80 [95% CI 0·75-0·86]; p<0·0001). Interpretation: Despite similar disease severity and ventilation management, ICU mortality in patients without ARDS is higher in MICs than in HICs, with a strong association with country-level economic status