EDA and ML - A Perfect Pair for Large-Scale Data Analysis
This short position paper presents our view of how exploratory data analysis techiniques and machine learning methods can be applied together for large-scale data analysis.
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This short position paper presents our view of how exploratory data analysis techiniques and machine learning methods can be applied together for large-scale data analysis.
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Large scale space programs analyze thousands of requirements while mitigating safety, performance, schedule, and cost risks. These efforts involve a variety of roles with interdependent use cases and goals. For example, study managers and facilitators identify ground-rules and assumptions for a collection of studies required for a program or project milestone. Task leaders derive product requirements from the ground rules and assumptions and describe activities to produce needed analytical products. Disciplined specialists produce the specified products and load results into a file management system. Organizational and project managers provide the personnel and funds to conduct the tasks. Each role has responsibilities to establish information linkages and provide status reports to management. Projects conduct design and analysis cycles to refine designs to meet the requirements and implement risk mitigation plans. At the program level, integrated design and analysis cycles studies are conducted to eliminate every 'to-be-determined' and develop plans to mitigate every risk. At the agency level, strategic studies analyze different approaches to exploration architectures and campaigns. This paper describes a web-accessible database developed by NASA to coordinate and manage tasks at three organizational levels. Other topics in this paper cover integration technologies and techniques for process modeling and enterprise architectures.
Here, the present paper addresses important fundamental issues of inter-phase heat transfer and energy coupling in turbulent dispersed multiphase flows through scaling analysis. In typical point-particle or two-fluid approaches, the fluid motion and convective heat transfer at the particle scale are not resolved and the momentum and energy coupling between fluid and particles are provided by proper closure models. By examining the kinetic energy transfer due to the coupling forces from the macroscale to microscale fluid motion, closure models are obtained for the contributions of the coupling forces to the energy coupling. Due to the inviscid origin of the added-mass force, its contribution to the microscale kinetic energy does not contribute to dissipative transfer to fluid internal energy as was done by the quasi-steady force. Time scale analysis shows that when the particle is larger than a critical diameter, the diffusive-unsteady kernel decays at a time scale that is smaller than the Kolmogorov time scale. As a result, the computationally costly Basset-like integral form of diffusive-unsteady heat transfer can be simplified to a non-integral form. Conventionally, the fluid-to-particle volumetric heat capacity ratio is used to evaluate the relative importance of the unsteady heat transfer to the energy balance of the particles. Therefore, for gas-particle flows, where the fluid-to-particle volumetric heat capacity ratio is small, unsteady heat transfer is usually ignored. However, the present scaling analysis shows that for small fluid-to-particle volumetric heat capacity ratio, the importance of the unsteady heat transfer actually depends on the ratio between the particle size and the Kolmogorov scale. Furthermore, the particle mass loading multiplied by the heat capacity ratio is usually used to estimate the importance of the thermal two-way coupling effect. Through scaling argument, improved estimates are established for the energy coupling parameters of each energy exchange mechanism between the phases.
Thermal and compositional inhomogeneities may significantly affect outcomes in a variety of fundamental and practical systems, including shock tubes, rapid compression machines, and combustion engines. Even in idealized configurations, homogenous reactive mixtures can never be completely uniform. This can lead to premature ignition, undesired flame propagation, or even detonation. Such perturbations to reacting flows have been recently analyzed by the authors of this work using a novel scaling analysis. That analysis identified three competing processes that affect the influence of an inhomogeneity on the globally observed ignition. First, diffusive quenching of a locally perturbed hot-spot can compete with premature ignition of this reaction center. Next, the ignited flame may extinguish due to high stretch rate. Finally, compressive heating of the end-gas due to flame propagation can compete with homogeneous ignition of the unperturbed bulk gas. In the present work, this scaling analysis is applied to ignition delay measurements reported in the literature. In these experiments, ignition often occurs earlier than expected due to inhomogeneities in the mixture. The previously-developed framework is utilized to infer a representative hot-spot geometry which could lead to the observed early ignition, demonstrating the validity of the scaling analysis.
High temperature gas reactors (HTGR) are poised to spearhead the next generation of nuclear reactors, due to their passive safety characteristics and their applications flexibility. Able to provide base load power or process heat for industrial applications, HGTRs may be a critical component to reducing greenhouse gas emissions in the United States and abroad. However, increased interest and development has also highlighted areas requiring further investigation, one of which being the air-ingress accident. Air ingress may occur following a break in the coaxial cross duct and pressure vessel depressurization. The initial mode of ingress is hypothesized to be density driven cross flow as the cold air enters the lower plenum, at which point molecular diffusion and/or local buoyancy phenomena will dominate until a global natural circulation current is established. While computational simulations support this progression, little or no experimental data exists to validate and support this phenomenology. Therefore, it is necessary to generate such data; this research seeks to do so through the use of a sub-scale separate effects test facility - the Stratified Flow Separate Effects Test Facility (SFSETF). The goal of this facility is more specifically to focus on the transition to global natural circulation starting from air ingress after a double-ended guillotine break (DEGB) while neglecting depressurization. This paper will provide analyses performed in the design of this facility, including the geometric and hydraulic scaling analyses, safety analysis and instrumentation selection. (authors)
In this work, a scaling patch approach is used to investigate the proper scales in planar turbulent wakes. A proper scale for the mean axial flow is the well-known maximum velocity deficit U ref = U ∞ –U ctr , where U ∞ is the free stream velocity and U ctr is the mean axial velocity at the wake centerline. From an admissible scaling of the mean continuity equation, a proper scale for the mean transverse flow is found as V ref = (dδ/dx)U ref , where dδ/dx is the growth rate of the wake width. From an admissible scaling of the mean momentum equation, a proper scale for the kinematic Reynolds shear stress is found as R uv,ref = U ∞ V ref , which is a mixed scale of the free stream velocity and the mean transverse flow scale. Expressions are derived for the scaled mean transverse velocity and Reynolds shear stress in the far field of planar turbulent wakes. Using a Gaussian function for the mean axial velocity deficit, approximate functions for the scaled mean transverse velocity and Reynolds shear stress are developed and found to agree well with experimental and simulation data. This work reveals that the mean transverse flow, despite its small magnitude, plays an important role in the scaling and understanding of the planar turbulent wake.
Gene expression, signal transduction, protein/chemical interactions, biomedical literature cooccurrences, and other concepts are often captured in biological network representations where nodes represent a certain bioentity and edges the connections between them. While many tools to manipulate, visualize, and interactively explore such networks already exist, only few of them can scale up and follow today’s indisputable information growth. In this review, we shortly list a catalog of available network visualization tools and, from a user-experience point of view, we identify four candidate tools suitable for larger-scale network analysis, visualization, and exploration. Lastly, we comment on their strengths and their weaknesses and empirically discuss their scalability, user friendliness, and postvisualization capabilities.
Identification of chemically similar stars using elemental abundances is core to many pursuits within Galactic archeology. However, measuring the chemical likeness of stars using abundances directly is limited by systematic imprints of imperfect synthetic spectra in abundance derivation. We present a novel data-driven model that is capable of identifying chemically similar stars from spectra alone. We call this relevant scaled component analysis (RSCA). RSCA finds a mapping from stellar spectra to a representation that optimizes recovery of known open clusters. By design, RSCA amplifies factors of chemical abundance variation and minimizes those of nonchemical parameters, such as instrument systematics. The resultant representation of stellar spectra can therefore be used for precise measurements of chemical similarity between stars. We validate RSCA using 185 cluster stars in 22 open clusters in the Apache Point Observatory Galactic Evolution Experiment survey. We quantify our performance in measuring chemical similarity using a reference set of 151,145 field stars. We find that our representation identifies known stellar siblings more effectively than stellar-abundance measurements. Using RSCA, 1.8% of pairs of field stars are as similar as birth siblings, compared to 2.3% when using stellar-abundance labels. We find that almost all of the information within spectra leveraged by RSCA fits into a two-dimensional basis, which we link to [Fe/H] and α-element abundances. We conclude that chemical tagging of stars to their birth clusters remains prohibitive. However, using the spectra has noticeable gain, and our approach is poised to benefit from larger data sets and improved algorithm designs.
We investigate the intermittent dynamics of momentum transport and its underlying time scales in the near-wall region of the neutrally stratified atmospheric boundary layer in the presence of a vegetation canopy. This is achieved through an empirical analysis of the persistence time scales (periods between successive zero-crossings) of momentum flux events, and their connection to the ejection–sweep cycle. Using high-frequency measurements from the GoAmazon campaign, spanning multiple heights within and above a dense canopy, the analysis suggests that, when the persistence time scales ( $t_p$ ) of momentum flux events from four different quadrants are separately normalized by $\varGamma _{w}$ (integral time scale of the vertical velocity), their distributions $P(t_p/\varGamma _{w})$ remain height-invariant. This result points to a persistent memory imposed by canopy-induced coherent structures, and to their role as an efficient momentum-transporting mechanism between the canopy airspace and the region immediately above. Moreover, $P(t_p/\varGamma _{w})$ exhibits a power-law scaling at times $t_{p}<\varGamma _{w}$ , with an exponential tail appearing for $t_{p} \geq \varGamma _{w}$ . By separating the flux events based on $t_p$ , we discover that around 80 % of the momentum is transported through the long-lived events ( $t_{p} \geq \varGamma _{w}$ ) at heights immediately above the canopy, while the short-lived ones ( $t_{p} < \varGamma _{w}$ ) only contribute marginally ( $\approx 20\,\%$ ). To explain the role of instantaneous flux amplitudes in momentum transport, we compare the measurements with newly developed surrogate data and establish that the range of time scales involved with amplitude variations in the fluxes tends to increase as one transitions from within to above the canopy.
Fish scale has the potential to be a rapid biomarker due to its structure and high possibility to come into contact with any pollutant in the aquatic environment. The scale structure consists of osteoblastic cells and other bone materials such as collagen where it is possible to form a molecular complex with heavy metals such as aluminum and iron. Hence, aluminum and iron in water could possibly destroy the scale material and marked as a scale deformation that quantitatively could be analyzed by comparing it to the normal scale structure. Water sampling and fish cage experiment were performed between June and July 2011 in Porong river which represented the water body that has high aluminum and iron contamination. The filtered water samples were preserved and extracted using the acid-mixture procedure prior to measurement of the aluminum and iron concentrations using Inductively Coupled Plasma Atomic Emission Spectroscopy (ICP-AES), while samples for total suspended solid (TSS) analysis were kept at 4 °C in cool-boxes. The scales were cleaned with sterile water, then dehydrated in 30, 50, 70, and 90% ethanol and dried on filter papers. They were then mounted on an aluminum stub and coated with gold in a sputter coater prior to Scanning Electron Microscope (SEM) observation. According to the SEM analysis, it was found that there were several deformations on the scale samples taken from sites that have high concentrations of aluminum and iron i.e. the increasing number of pits, deformation and decreasing number of spherules and ridges while the control scale exhibited the normal features. However, the site with higher TSS and pH indicated lower aluminum effect. A moderate correlation was found between the number of pits with aluminum (r=0.43) and iron (r=0.41) concentrations. Fish scale deformation using SEM analysis can potentially be a rapid biomarker in aquatic monitoring of aluminum and iron contamination. However, the measurement must be accompanied by pH and TSS observations.
Abstract The climatological mean barotropic vorticity budget is analyzed to investigate the relative importance of surface wind stress, topography, planetary vorticity advection, and nonlinear advection in dynamical balances in a global ocean simulation. In addition to a pronounced regional variability in vorticity balances, the relative magnitudes of vorticity budget terms strongly depend on the length‐scale of interest. To carry out a length‐scale dependent vorticity analysis in different ocean basins, vorticity budget terms are spatially coarse‐grained. At length‐scales greater than 1,000 km, the dynamics closely follow the Topographic‐Sverdrup balance in which bottom pressure torque, surface wind stress curl and planetary vorticity advection terms are in balance. In contrast, when including all length‐scales resolved by the model, bottom pressure torque and nonlinear advection terms dominate the vorticity budget (Topographic‐Nonlinear balance), which suggests a prominent role of oceanic eddies, which are of km in size, and the associated bottom pressure anomalies in local vorticity balances at length‐scales smaller than 1,000 km. Overall, there is a transition from the Topographic‐Nonlinear regime at scales smaller than 1,000 km to the Topographic‐Sverdrup regime at length‐scales greater than 1,000 km. These dynamical balances hold across all ocean basins; however, interpretations of the dominant vorticity balances depend on the level of spatial filtering or the effective model resolution. On the other hand, the contribution of bottom and lateral friction terms in the barotropic vorticity budget remains small and is significant only near sea‐land boundaries, where bottom stress and horizontal viscous friction generally peak.
As leadership computing facilities grow their storage capacity into the multi- petabyte range, the number of files and directories leap into the scale of billions. A complete profiling of such a parallel file system in a production environment presents a unique challenge. On one hand, the time, resources, and negative performance impact on production users can make regular profiling difficult. On the other hand, the result of such profiling can yield much needed understanding of the file system's general characteristics, as well as provide insight to how users write and access their data on a grand scale. This paper presents a lightweight and scalable profiling solution that can efficiently walk, analyze, and profile multi-petabyte parallel file systems. This tool has been deployed and is in regular use on very large-scale production parallel file systems at both Oak Ridge National Lab's Oak Ridge Leadership Facility (OLCF) and Lawrence Livermore National Lab's Livermore Computing (LC) facilities. We present the results of our initial analysis on the data collected from these two large-scale production systems, organized into three use cases: (1) file system snapshot and composition, (2) striping pattern analysis for Lustre, and (3) simulated storage capacity utilization in preparation for future file systems. Our analysis shows that on the OLCF file system, over 96% of user files exhibit the default stripe width, potentially limiting performance on large files by underutilizing storage servers and disks. Our simulated block analysis quantitatively shows the space overhead when doing a forklift system migration. It also reveals that due to the difference in system compositions (OLCF vs. LC), we can achieve better performance and space trade-offs by employing different native file system block sizes.
The large-scale analysis task of deciphering gravitational wave signals in the LISA data stream will be difficult, requiring a large amount of computational resources and extensive development of computational methods. Its high dimensionality, multiple model types, and complicated noise profile require a global fit to all parameters and input models simultaneously. In this work, we detail our global fit algorithm, called “Erebor,” designed to accomplish this challenging task. It is capable of analysing current state-of-the-art datasets and then growing into the future as more pieces of the pipeline are completed and added. We describe our pipeline strategy, the algorithmic setup, and the results from our analysis of the LDC2A Sangria dataset, which contains Massive Black Hole Binaries, compact Galactic Binaries, and a parameterized noise spectrum whose parameters are unknown to the user. The Erebor algorithm includes three unique and very useful contributions: GPU acceleration for enhanced computational efficiency; ensemble MCMC sampling with multiple MCMC walkers per temperature for better mixing and parallelized sample creation; and special online updates to reversible-jump (or trans-dimensional) sampling distributions to ensure sampler mixing and accurate initial estimates for detectable sources in the data. We recover posterior distributions for all 15 (6) of the injected MBHBs in the LDC2A training (hidden) dataset. We catalog ∼12000 Galactic Binaries (∼8000 as high confidence detections) for both the training and hidden datasets. All of the sources and their posterior distributions are provided in publicly available catalogs.
The large-scale analysis task of deciphering gravitational-wave signals in the LISA data stream will be difficult, requiring a large amount of computational resources and extensive development of computational methods. Its high dimensionality, multiple model types, and complicated noise profile require a global fit to all parameters and input models simultaneously. In this work, we detail our global fit algorithm, called “Erebor,” designed to accomplish this challenging task. It is capable of analyzing current state-of-the-art datasets and then growing into the future as more pieces of the pipeline are completed and added. We describe our pipeline strategy, the algorithmic setup, and the results from our analysis of the LDC2A Sangria dataset, which contains massive black hole binaries, compact galactic binaries, and a parametrized noise spectrum whose parameters are unknown to the user. The Erebor algorithm includes three unique and very useful contributions: GPU acceleration for enhanced computational efficiency; ensemble Markov Chain Monte Carlo (MCMC) sampling with multiple MCMC walkers per temperature for better mixing and parallelized sample creation; and special online updates to reversible-jump (or transdimensional) sampling distributions to ensure sampler mixing and accurate initial estimates for detectable sources in the data.We recover posterior distributions for all 15 (6) of the injected massive black hole binaries (MBHB) in the LDC2A training (hidden) dataset. We catalog ∼12000 galactic binaries (∼8000 as high confidence detections) for both the training and hidden datasets. All of the sources and their posterior distributions are provided in publicly available catalogs.
Daylighting in buildings has come under increased scorn in the wake of ubiquitous solid-state lighting (SSL), as installed lighting power densities in buildings have been reduced to the point where designers and owners question the benefits of properly-designed daylighting and electric lighting control. At the same time the very process of demonstrating well-executed daylighting to satisfy new building rating systems has become burdensome. This paper demonstrates the utility of illuminance as an input to simple, existing qualitative and quantitative metrics for daylighting performance in buildings, at scale. This assertion is supported by sensitivity analysis of a large-scale daylighting simulation dataset. Uses of this data for quantifying non-energy benefits (NEBs) of daylighting are also explored. We do not eschew the use of newer, more detailed daylight metrics in design, but we do encourage the use of simple daylight metrics for a broader range of new and retrofit building projects toward a net-zero future.
In an attempt to attain building-specific characteristics for urban-scale building energy models, county-specific tax assessors’ data is often an initial data source. This data source can contain valuable information such as year built, area, height, HVAC type, and roof/wall descriptions.We will show examples of 2,000 fields from Hamilton County in Tennessee with examples of many fields which are not relevant to urban-scale building energy modeling, are incorrect compared to other data sources, and highlight some lessons learned working with such a data source.There are currently 3,142 counties in the United States, each with their own data format, field definitions, and data access policy. As urban-scale involves city-scale analysis potentially covering multiple counties and matures toward state- or nation-scale analysis, county-by-county approaches are not scalable. While there are efforts to unify these datasets, there is an increasing proliferation of data and algorithms that can cover wider areas and provide more accurate inputs for urban-scale models. This paper summarizes computer vision of imagery, cartographic layers, building type assessment, and model generation used to achieve scalable detection and analysis of buildings.
Ranges of validity for the continuum-beam model, the length-scale effects and continuum assumptions are analyzed in the framework of scaling analysis of NT structure. Two coupled criteria for the applicability of the continuum model are presented. Scaling analysis of NT buckling and geometric parameters (e.g., diameter and length) is carried out to determine the key non-dimensional parameters that control the buckling strains and modes of NT buckling. A model applicability map, which represents two classes of NTs, is constructed in the space of non-dimensional parameters. In an analogy with continuum mechanics, a mechanical law of geometric similitude is presented for two classes of beam-like NTs having different geometries. Expressions for the critical buckling loads and strains are tailored for the distinct groups of NTs and compared with the data provided by the molecular dynamics simulations. Implications for molecular dynamics simulations and the NT-based scanning probes are discussed.