2,020 research outputs found

    A Tale of Two Data-Intensive Paradigms: Applications, Abstractions, and Architectures

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    Scientific problems that depend on processing large amounts of data require overcoming challenges in multiple areas: managing large-scale data distribution, co-placement and scheduling of data with compute resources, and storing and transferring large volumes of data. We analyze the ecosystems of the two prominent paradigms for data-intensive applications, hereafter referred to as the high-performance computing and the Apache-Hadoop paradigm. We propose a basis, common terminology and functional factors upon which to analyze the two approaches of both paradigms. We discuss the concept of "Big Data Ogres" and their facets as means of understanding and characterizing the most common application workloads found across the two paradigms. We then discuss the salient features of the two paradigms, and compare and contrast the two approaches. Specifically, we examine common implementation/approaches of these paradigms, shed light upon the reasons for their current "architecture" and discuss some typical workloads that utilize them. In spite of the significant software distinctions, we believe there is architectural similarity. We discuss the potential integration of different implementations, across the different levels and components. Our comparison progresses from a fully qualitative examination of the two paradigms, to a semi-quantitative methodology. We use a simple and broadly used Ogre (K-means clustering), characterize its performance on a range of representative platforms, covering several implementations from both paradigms. Our experiments provide an insight into the relative strengths of the two paradigms. We propose that the set of Ogres will serve as a benchmark to evaluate the two paradigms along different dimensions.Comment: 8 pages, 2 figure

    Development of framework for sustainable Lean implementation: An ISM approach

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    The survival of any organization depends upon its competitive edge. Even though Lean is one of the most powerful quality improvement methodologies, nearly twothirds of the Lean implementations results in failures and less than one-fifth of those implemented have sustained results. One of the most significant tasks of top management is to identify, understand and deploy the significant Lean practices like quality circle, Kanban, Just-in-time purchasing, etc. The term 'bundle' is used to make groupsm of inter-related and internally consistent Lean practices. Eight significant Lean practice bundles have been identified based on literature reviewed and opinion of the experts. The order of execution of Lean practice bundles is very important. Lean practitioners must be able to understand the interrelationship between these practice bundles. The objective of this paper is to develop framework for sustainable Lean implementation using interpretive structural modelling approach

    Seasonal variations of abundance and live/dead compositions of copepods inMersin Bay, northeastern Levantine Sea (eastern Mediterranean)

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    The seasonality of abundance and live/dead compositions of copepods was studied in the northeastern Mediterranean Sea. Zooplankton, chlorophyll-a, and PO4 sampling was performed on a monthly basis from March 2006 to February 2007 at both a coastal station and an open water station. At the coastal station, high phytoplankton biomass was driven by PO4 input from the Lamas River. On annual average, copepod abundance was 53,075 and 140,227 ind. m(-2) at the coastal and open water stations, respectively. The most common copepod taxa were Oithona similis, Euterpina acutifrons, Labidocera spp., Oncaea media, and Temora spp. at the coastal station, and Oncaea media, Labidocera spp., Lucicutia spp., Farranula spp., Oithona similis, and Microsetella spp. at the open water station. At the coastal station, dead copepods did not exceed 7% of the population; on annual average, 2.6% of the copepods were dead. At the open water station, on average 10.6% of the copepod population appeared dead; the percentages of dead copepods reached 29.5% in April and 21.7% in May 2006, suggesting that the copepod community suffered higher nonpredatory mortality at the open water station than at the coastal station, especially in the spring

    Elimination of the reaction rate 'scale effect': application of the Lagrangian reactive particle-tracking method to simulate mixing-limited, field-scale biodegradation at the Schoolcraft (MI, USA) site

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    This is the peer reviewed version of the following article: [Ding, D., Benson, D. A., Fernàndez‐Garcia, D., Henri, C. V., Hyndman, D. W., Phanikumar, M. S., & Bolster, D. (2017). Elimination of the reaction rate “scale effect”: Application of the Lagrangian reactive particle‐tracking method to simulate mixing‐limited, field‐scale biodegradation at the Schoolcraft (MI, USA) site. Water Resources Research, 53, 10,411–10,432. https://doi.org/10.1002/2017WR021103], which has been published in final form at https://doi.org/10.1002/2017WR021103. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.Measured (or empirically fitted) reaction rates at groundwater remediation sites are typically much lower than those found in the same material at the batch or laboratory scale. The reduced rates are commonly attributed to poorer mixing at the larger scales. A variety of methods have been proposed to account for this scaling effect in reactive transport. In this study, we use the Lagrangian particle-tracking and reaction (PTR) method to simulate a field bioremediation experiment at the Schoolcraft, MI site. A denitrifying bacterium, Pseudomonas Stutzeri strain KC (KC), was injected to the aquifer, along with sufficient substrate, to degrade the contaminant, carbon tetrachloride (CT), under anaerobic conditions. The PTR method simulates chemical reactions through probabilistic rules of particle collisions, interactions, and transformations to address the scale effect (lower apparent reaction rates for each level of upscaling, from batch to column to field scale). In contrast to a prior Eulerian reaction model, the PTR method is able to match the field-scale experiment using the rate coefficients obtained from batch experiments.Peer ReviewedPostprint (author's final draft

    The gliding phase in swimming: the effect of water depth

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    Aiming to achieve higher performances, swimmers should maximize each component of swimming races. During starts and turns, the gliding phase represents a determinant part of these race components. Thus, the depth position allowing minimizing the hydrodynamic drag force represents an important concern in swimming research. The aim of this study was to analyse the effect of depth on drag during the underwater gliding, using computational fluid dynamic
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