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نتیجه جستجو - Spark

تعداد مقالات یافته شده: 119
ردیف عنوان نوع
1 Gaining from disorder: Making the case for antifragility in purchasing and supply chain management
به دست آوردن اختلال: ایجاد شرایط ضد انعطاف پذیری در خرید و مدیریت زنجیره تامین-2021
The purchasing and supply chain management (P&SCM) discipline assumes that supply chains are fragile systems, hence taking a “negative” approach toward disorder. Building on Taleb’s concept of antifragility—the ability to gain from disorder rather than avoiding it—, we challenge this traditional assumption. The COVID-19 pandemic has revealed that some companies were indeed able to gain from disorder, whereas some of those that focused too much on robustness and resilience lost ground. Building robust and resilient supply chains may no longer be enough to thrive in today’s highly volatile business world. This article sparks a new debate by introducing antifragility to the P&SCM literature and provides new directions for future research.
Keywords: Antifragility | Antifragile supply chain | Supply chain disruption | Resilient supply chain | Purchasing | Robust supply chain | Supply chain management | COVID-19
مقاله انگلیسی
2 Computer vision in surgery
بینایی ماشین در جراحی-2021
The fields of computer vision (CV) and artificial intelligence (AI) have undergone rapid advancements in the past decade, many of which have been applied to the analysis of intraoperative video. These advances are driven by wide-spread application of deep learning, which leverages multiple layers of neural networks to teach computers complex tasks. Prior to these advances, applications of AI in the operating room were limited by our relative inability to train computers to accurately understand images with traditional machine learning (ML) techniques. The development and refining of deep neural networks that can now accurately identify objects in images and remember past surgical events has sparked a surge in the applications of CV to analyze intraoperative video and has allowed for the accurate identification of surgical phases (steps) and instruments across a variety of procedures. In some cases, CV can even identify operative phases with accuracy similar to surgeons. Future research will likely expand on this foundation of surgical knowledge using larger video datasets and improved algorithms with greater accuracy and interpretability to create clinically useful AI models that gain widespread adoption and augment the surgeon’s ability to provide safer care for patients everywhere.
مقاله انگلیسی
3 TITAN: A knowledge-based platform for Big Data workflow management
TITAN: یک پلت فرم مبتنی بر دانش برای مدیریت گردش کار داده های بزرگ-2021
Modern applications of Big Data are transcending from being scalable solutions of data processing and analysis, to now provide advanced functionalities with the ability to exploit and understand the underpinning knowledge. This change is promoting the development of tools in the intersection of data processing, data analysis, knowledge extraction and management. In this paper, we propose TITAN, a software platform for managing all the life cycle of science workflows from deployment to execution in the context of Big Data applications. This platform is characterised by a design and operation mode driven by semantics at different levels: data sources, problem domain and workflow components. The proposed platform is developed upon an ontological framework of meta-data consistently managing processes and models and taking advantage of domain knowledge. TITAN comprises a well-grounded stack of Big Data technologies including Apache Kafka for inter-component communication, Apache Avro for data serialisation and Apache Spark for data analytics. A series of use cases are conducted for validation, which comprises workflow composition and semantic meta-data management in academic and real-world fields of human activity recognition and land use monitoring from satellite images./
keywords: تجزیه و تحلیل داده های بزرگ | مفاهیم | استخراج دانش | Big Data analytics | Semantics | Knowledge extraction
مقاله انگلیسی
4 Laminar flame speeds of methane/air mixtures at engine conditions: Performance of different kinetic models and power-law correlations
سرعت شعله چند لایه مخلوط های متان / هوا در شرایط موتور: عملکرد مدل های مختلف جنبشی و همبستگی قدرت قانون-2020
The laminar flame speed is an important input in turbulent premixed combustion modelling of spark ignition engines. At engine-relevant temperatures and pressures, its measurement is challenging or not possible and thereby it is usually obtained from simulations based on chemical models or power-law correlations. This work aims to investigate the performance of different models and power-law correla- tions in terms of predicting laminar flame speeds of methane/air at engine conditions. The propagation of spherically expanding laminar flames in a closed chamber was simulated and laminar flame speeds were computed over a broad range of pressures (1-120 atm) and temperatures (30 0-110 0 K) for methane/air mixtures based on seven kinetic models. It was found that at engine conditions, there are notable dis- crepancies among the predictions. GRI Mech. 3.0 and USC Mech. II respectively predict the largest and smallest values at high pressure conditions. This was explained by the difference in CH 3 oxidation and recombination according to reaction pathway analysis. Additionally, laminar flame speeds of methane flames were experimentally determined under engine-relevant conditions. It was shown that the recently developed Foundational Fuel Chemistry Model Version 1.0 model predicts closely the data at high pres- sures and temperatures. Therefore, it was chosen as the reference model for the comparisons. Thirteen published power-law correlations for laminar flame speeds of CH 4 /air were implemented, and their per- formance in predicting the laminar flame speeds at engine conditions was investigated. Most of these correlations have been derived for a narrow range of temperatures and pressures, which are lower than those encountered in engines. A new power-law correlation was derived based on predictions by the Foundational Fuel Chemistry Model Version 1.0. This new correlation is expected to provide reliable pre- dictions at engine conditions for a stoichiometric methane/air mixture and thereby it is recommended to be used in modeling turbulent premixed combustion in spark-ignition engine simulations.
Keywords: Laminar flame speed | engine conditions | methane | power-law correlation | propagating spherical flame
مقاله انگلیسی
5 How does liability affect prices? Railroad sparks and timber
بدهی چگونه بر قیمت ها تأثیر می گذارد؟ جرقه و چوب راه آهن-2020
This paper analyzes how judicially-determined liability assignments affect valuations and prices. On two occasions in 2007, a railway company caused a fire to break out in the State of Washington. The two fires burned down some of the neighboring properties’ timber. These two incidents led to two companion court cases that made it all the way to the Washington Supreme Court. The court rulings, both made on May 31, 2012, held that the railway company was not liable for timber damages under Washington’s timber trespass statute, despite having acted negligently. As a consequence of these decisions, economic theory predicts a decrease in the value of timber in those areas associated with higher risk of fire, and an increase in the value of Washington railway companies. Using a triple difference model and an event study, we test and find evidence supporting this prediction.
Keywords: Liability | Property rights | Law and economics | Event study
مقاله انگلیسی
6 Pivot-based approximate k-NN similarity joins for big high-dimensional data
پیوندهای شباهت تقریبی k-NN مبتنی بر محوری برای داده های بزرگ با ابعاد بزرگ-2020
Given an appropriate similarity model, the k-nearest neighbor similarity join represents a useful yet costly operator for data mining, data analysis and data exploration applications. The time to evaluate the operator depends on the size of datasets, data distribution and the dimensionality of data representations. For vast volumes of high-dimensional data, only distributed and approximate approaches make the joins practically feasible. In this paper, we investigate and evaluate the performance of multiple MapReduce-based approximate k-NN similarity join approaches on two leading Big Data systems Apache Hadoop and Spark. Focusing on the metric space approach relying on reference dataset objects (pivots), this paper investigates distributed similarity join techniques with and without approximation guarantees and also proposes high-dimensional extensions to previously proposed algorithms. The paper describes the design guidelines, algorithmic details, and key theoretical underpinnings of the compared approaches and also presents the empirical performance evaluation, approximation precision, and scalability properties of the implemented algorithms. Moreover, the Spark source code of all these algorithms has been made publicly available. Key findings of the experimental analysis are that randomly initialized pivot-based methods perform well with big highdimensional data and that, in general, the selection of the best algorithm depends on the desired levels of approximation guarantee, precision and execution time.
Keywords: Hadoop | Spark | MapReduce | k-NN | Approximate similarity join | High-dimensional data
مقاله انگلیسی
7 ساخت و جامعه - یک دانشجوی مقدماتی دوره مهندسی با مشارکت ساخت و علوم اجتماعی
سال انتشار: 2020 - تعداد صفحات فایل pdf انگلیسی: 10 - تعداد صفحات فایل doc فارسی: 38
برنامه درسی و آموزش مقدماتی دانشجوی دوره اول مهندسی در زمینه تولید و جامعه ارائه شده است. این دوره برای استفاده از پهپاد کوادکوپتر به عنوان نمونه برای آموزش دانش در فرایندها و سیستم های تولید ، نشان دادن تأثیرات بالقوه ساخت به جامعه و تحریک یادگیری در نوآوری ، کار گروهی و ارتباطات در یک محیط مهندسی دنیای واقعی وهمچنین برای یادگیری و نحوه نتیجه گیری بهتر دانش آموزان طراحی شده است. این دوره بر اساس دو پروژه تیمی ساخته شده است. دانشجویان مونتاژ یک هواپیمای بدون سرنشین پیشرفته ، DJI Spark را تمرین و زمان بندی کردند و یک ارائه پروژه میان مدت و گزارش برنامه خود را برای راه اندازی کارخانه مونتاژ در میشیگان برای این پهپاد ایجاد کردند. دانش آموزان طراحی خط مونتاژ ، تعادل زمان چرخه ، مطالعه زمان ، اتوماسیون ، رباتیک ، ارگونومی را انجام دادند. پروژه نهایی ، طراحی و ساخت پیوست به یک هواپیمای بدون سرنشین DJI F330 ، برای مأموریتی است که به نفع جامعه خواهد بود. دانش آموزان جنبه های اجتماعی ساخت ، طراحی به کمک رایانه ، ساخت مواد افزودنی ، مهارت های ساخت و فرایندهای ساخت را فرا گرفتند. هر تیم یک نیاز اجتماعی را شناسایی کرده ، ضمیمه ای را برای پهپاد DJI F330 ، با مهندسان باتجربه طراحی کرده ، قطعات ساخته شده ، پیوست را مونتاژ کرده و آنها را در پروازهای آزمایشی پهپاد ارزیابی کرده است. این دوره با همکاری نزدیک با یک کالج جامعه محلی برای به اشتراک گذاشتن سخنرانی و مواد آزمایشگاهی و همچنین آموزش در ساخت برای استفاده از همان گروه فارغ التحصیلان دبیرستان اجرا شد. این رویکرد علوم اجتماعی یکپارچه ، تولید ، و ارتباطات فنی برای آموزش ساخت با استفاده از هواپیماهای بدون سرنشین و ارتباط ساخت و جامعه نشان داده شده است که برای یک دوره مقدماتی مهندسی کارآمد است. دانشجویان مهندسی دوره اول اغلب در ترم اول خود تغییرات و فشار فوق العاده ای را تجربه می کنند. جلسه سخنرانی ، آزمایشگاه و بحث برای ارتباطات برای کمک به انتقال دانش آموزان در ترم اول مطالعه مهندسی تنظیم شد.
کلمات کلیدی: آموزش تولید | علوم اجتماعی | آموزش مبتنی بر نوآوری
مقاله ترجمه شده
8 DQPFS: Distributed quadratic programming based feature selection for big data
DQPFS: انتخاب ویژگی های مبتنی بر برنامه نویسی درجه دوم برای داده های بزرگ-2020
With the advent of the Big data, the scalability of the machine learning algorithms has become more crucial than ever before. Furthermore, Feature selection as an essential preprocessing technique can improve the performance of the learning algorithms in confront with large-scale dataset by removing the irrelevant and redundant features. Owing to the lack of scalability, most of the classical feature selection algorithms are not so proper to deal with the voluminous data in the Big Data era. QPFS is a traditional feature weighting algorithm that has been used in lots of feature selection applications. By inspiring the classical QPFS, in this paper, a scalable algorithm called DQPFS is proposed based on the novel Apache Spark cluster computing model. The experimental study is performed on three big datasets that have a large number of instances and features at the same time. Then some assessment criteria such as accuracy, execution time, speed-up and scale-out are figured. Moreover, to study more deeply, the results of the proposed algorithm are compared with the classical version QPFS and the DiRelief, a distributed feature selection algorithm proposed recently. The empirical results illustrate that proposed method has (a) better scale-out than DiRelief, (b) significantly lower execution time than DiRelief, (c) lower execution time than QPFS, (d) better accuracy of the Naïve Bayes classifier in two of three datasets than DiRelief.
Keywords: Big data | Apache Spark | Feature selection | Feature ranking | Quadratic programming
مقاله انگلیسی
9 Editorial: Enhancing the exploration and communication of quantitative entrepreneurship research
تحریریه: تقویت اکتشاف و ارتباط تحقیق کمی کارآفرینی-2020
The purpose of this editorial is to discuss ways to enhance exploratory quantitative studies in entrepreneurship. We use examples from entrepreneurship research and other scientific fields to illustrate the advantages of graphical data display for both exploratory purposes and post hoc tests. We provide suggestions for authors, reviewers, and editors on ways to enhance the transparency, accuracy, and pedagogical presentation of quantitative data in papers with the explicit purpose of illuminating emerging and important entrepreneurship phenomena. Our hope is that we spark a conversation among entrepreneurship scholars about the state of our empirical work and the possibilities that lie ahead to enhance exploratory entrepreneurship research.
Keywords: Research design | Publishing in JBV | Exploratory research
مقاله انگلیسی
10 A fully scalable big data framework for Botnet detection based on network traffic analysis
چارچوب داده های بزرگ کاملاً مقیاس پذیر برای تشخیص Botnet مبتنی بر آنالیز ترافیک شبکه-2020
Many traditional Botnet detection methods have trouble scaling up to meet the needs of multi-Gbps networks. This scalability challenge is not just limited to bottlenecks in the detection process, but across all individual components of the Botnet detection system in- cluding data gathering, storage, feature extraction, and analysis. In this paper, we propose a fully scalable big data framework that enables scaling for each individual component of Botnet detection. Our framework can be used with any Botnet detection method - includ- ing statistical methods, machine learning methods, and graph-based methods. Our experi- mental results show that the proposed framework successfully scales in live tests on a real network with 5Gbps of traffic throughput and 50 millions IP addresses visits. In addition, our run time scales logarithmically with respect to the volume of the input for example, when the scale of the input data multiplies by 4 ×, the total run time increases by only 31%. This is significant improvement compared to schemes such as Botcluster in which run time increases by 86% under similar scale condition.
Keywords: Botnet detection | Big data | Hadoop | Spark | Machine learning | Scalability
مقاله انگلیسی
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