Provenance-and machine learning-based recommendation of parameter values in scientific workflows

Scientific Workflows (SWfs) have revolutionized how scientists in various domains of science conduct their experiments. The management of SWfs is performed by complex tools that provide support for workflow composition, monitoring, execution, capturing, and storage of the data generated during execution. In some cases, they also provide components to ease the visualization and analysis of the generated data. During the workflow’s composition phase, programs must be selected to perform the activities defined in the workflow specification. These programs often require additional parameters that serve to adjust the program’s behavior according to the experiment’s goals. Consequently, workflows commonly have many parameters to be manually configured, encompassing even more than one hundred in many cases. Wrongly parameters’ values choosing can lead to crash workflows executions or provide undesired results. As the execution of data- and compute-intensive workflows is commonly performed in a high-performance computing environment e.g., (a cluster, a supercomputer, or a public cloud), an unsuccessful execution configures a waste of time and resources. In this article, we present FReeP—Feature Recommender from Preferences, a parameter value recommendation method that is designed to suggest values for workflow parameters, taking into account past user preferences. FReeP is based on Machine Learning techniques, particularly in Preference Learning. FReeP is composed of three algorithms, where two of them aim at recommending the value for one parameter at a time, and the third makes recommendations for n parameters at once. The experimental results obtained with provenance data from two broadly used workflows showed FReeP usefulness in the recommendation of values for one parameter. Furthermore, the results indicate the potential of FReeP to recommend values for n parameters in scientific workflows.

Introduction to the ringing effect in satellite hyperspectral atmospheric spectrometry

Atmospheric remote spectrometry from space has become in the last 20 years a key component of the Earth monitoring system: their large coverage and deci-kelvin stability have demonstrated their usefulness for weather prediction, atmospheric composition monitoring as well as climate monitoring. It is thus critical to investigate the possible sources of errors associated to this technique. One of them is the so-called "ringing error" that appears in Fourier transform spectrometers when the instrument transmission varies at the scale of the spectral resolution. This paper exposes the theoretical basis of this particular type of radiometric uncertainty. Its sensitivity to instrumental parameters as well as the impact on the radiometrically calibrated measurements is assessed in the context of atmospheric infrared sounding using Fourier transform spectrometers. It is shown that this error is an intrinsic feature of such instruments that could safely be ignored in early-generation instruments but will have to be taken into account in the new generation ones as it can yield a significant degradation of the radiometric error budget.

ON THE SNOW COVER CHEMICAL COMPOSITION IN THE BOLSHEKHETSIR NATURE RESERVE (KHABAROVSK TERRITORY)

This paper presents the results of the snow cover chemical composition monitoring at the Bolshekhetsir nature reserve, conducted in March 2017, in terms of pH, specific conductivity, salinity, major ions, biogenic and suspended substances.

SNOWPACK MACROCOMPONENTS COMPOSITION MONITORING IN THE VICINITY OF TPP-5 OF NOVOSIBIRSK CITY

The results of experimental investigation of suspended substances and ionic composition of snowpack in the vicinity of Novosibirsk thermal power plant (TPP) 5 in 2018/19 winter season are presented. The correlation relationship analysis between impurity components was done. On the basis of light impurity sedimentation reconstruction model and the snowpack monitoring data, there was done the numerical restoring the concentration fields.

Gas phase methanol synthesis with Raman spectroscopy for gas composition monitoring

Applicability of Raman spectroscopy for time-resolved gas composition monitoring during direct methanol synthesis via carbon dioxide hydrogenation is investigated.