کارابرن عزیز، مقالات isi بالاترین کیفیت ترجمه را دارند، ترجمه آنها کامل و دقیق می باشد (محتوای جداول و شکل های نیز ترجمه شده اند) و از بهترین مجلات isi انتخاب گردیده اند. همچنین تمامی ترجمه ها دارای ضمانت کیفیت بوده و در صورت عدم رضایت کاربر مبلغ عینا عودت داده خواهد شد.
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BDEv 3:0: Energy efficiency and microarchitectural characterization of Big Data processing frameworks
BDEv 3:0: کارایی انرژی و خصوصیات میکروارساختاری چارچوب پردازش داده های بزرگ-2018
As the size of Big Data workloads keeps increasing, the evaluation of distributed frameworks becomes a crucial task in order to identify potential performance bottlenecks that may delay the processing of large datasets. While most of the existing works generally focus only on execution time and resource utilization, analyzing other important metrics is key to fully understanding the behavior of these frameworks. For example, microarchitecture-level events can bring meaningful insights to characterize the interaction between frameworks and hardware. Moreover, energy consumption is also gaining increasing attention as systems scale to thousands of cores. This work discusses the current state of the art in evaluating distributed processing frameworks, while extending our Big Data Evaluator tool (BDEv) to extract energy efficiency and microarchitecture-level metrics from the execution of representative Big Data workloads. An experimental evaluation using BDEv demonstrates its usefulness to bring meaningful information from popular frameworks such as Hadoop, Spark and Flink.
Keywords: Big Data processing, performance evaluation, energy efficiency, microarchitectural characterization
IOT and big data based cooperative logistical delivery scheduling method and cloud robot system
اینترنت اشیا و داده های بزرگ مبتنی بر همکاری لایسنسسی برنامه ریزی تحویل و سیستم ربات ابر-2018
Many studies have been done for logistics delivery scheduling technologies, but the cooperating and relaying of resources in the process of logistics delivery remains elusive. We proposed IOT and big data based cooperative logistical delivery scheduling method and cloud robot system, After obtaining the big data of logistics delivery resources and requirements from logistics delivery companies through the IOT and/or Internet, establishing the map of logistics delivery routes based on the big data of logistics delivery resources, the logistics delivery route corresponding to the logistics delivery requirements is selected from the map of logistics delivery routes by using the shortest route algorithm of the graph theory, and then the logistics delivery resources corresponding to the logistics delivery route are scheduled to the corresponding logistics delivery requirements, which can greatly improve the cooperative scheduling of logistics delivery resources among different logistics delivery companies to enhance the level of logistics delivery resources utilization, reduce the logistics delivery logistics delivery costs, and improve customer experience.
Keywords: logistical delivery, cooperative scheduling, IOT, big data, cloud robot
Operational safety: The hidden cost of supply-demand mismatch in fashion and textiles related manufacturers
ایمنی عملیاتی: هزینه مخفی عدم هماهنگی عرضخه - تقاضا در سازنده های مربوط به مد و منسوجات-2018
Inventory management is a focus for operations management scholars and operations managers. Previous literature mainly investigated the relations between firms inventory and financial performance. However, the relation between firms inventory and non-financial performance (e.g., social outcome) is less clear. This study takes a fresh perspective to examine the impacts of supply-demand mismatch on firms safety performance. Based on a sample set from fashion and textiles related manufacturers, the analysis suggests that supply-demand mismatch (measured by inventory volatility) associates with a higher likelihood of safety incidents. The impact is more salient where the firms are operating in complex (labour intensive) and tightly coupled (high production capacity utilization) environments. This study provides significant contributions to the inventory management literature, occupational health and safety management literature and operational managers.
keywords: Inventory management |Occupational health and safety |Empirical study |Fashion and textiles
Capacity investment under uncertainty: The effect of volume flexibility
سرمایه گذاری روی ظرفیت تحت عدم قطعیت: تاثیر انعطاف پذیری حجم-2018
Real option theory is a central tool in todays investment theory as it integrates uncertainty and managerial flexibility in the analysis and valuation of investment projects. This paper studies the optimal time and size of investment for a monopolistic firm under demand uncertainty and volume flexibility. In our modeling framework, demand is random and the firm first decides the optimal time and size of the production process. After entry, the firm adjusts continuously production volume to match the observed demand. Volume flexibility comes at a cost which depends on both the current output and the established capacity. We study two different models of volume flexibility: Downside volume flexibility allows the firms to produce any quantity below the installed capacity; Upside volume flexibility allows to expand production above the firms capacity size. In both cases, the option to temporary suspend production is not given a priori, but it is part of the firms optimal choice. With this feature, the model provides conclusions that contrast some of the most recent theoretical findings on the same subject. We find that an increase of the degree of downside volume flexibility makes the firm willing to invest earlier in a larger plant. We also show that downside volume flexibility reduces the utilization rates, especially in highly uncertain markets. Upside volume flexibility has the joint effect of reducing the size of the investment and the investment threshold at which the firm installs capacity. The utilization rates are significantly higher compared to the case of downside volume flexibility only, and there is an increasing relationship between increased upside flexibility and utilization rates.
keywords: Real options |Capacity investments |Volume flexibility
Scalable system scheduling for HPC and big data
برنامه ریزی مقیاس پذیر برای HPC و داده های بزرگ-2018
In the rapidly expanding field of parallel processing, job schedulers are the ‘‘operating systems’’ of modern big data architectures and supercomputing systems. Job schedulers allocate computing resources and control the execution of processes on those resources. Historically, job schedulers were the domain of supercomputers, and job schedulers were designed to run massive, long-running computations over days and weeks. More recently, big data workloads have created a need for a new class of computations consisting of many short computations taking seconds or minutes that process enormous quantities of data. For both supercomputers and big data systems, the efficiency of the job scheduler represents a fundamental limit on the efficiency of the system. Detailed measurement and modeling of the perfor mance of schedulers are critical for maximizing the performance of a large-scale computing system. This paper presents a detailed feature analysis of 15 supercomputing and big data schedulers. For big data workloads, the scheduler latency is the most important performance characteristic of the scheduler. A theoretical model of the latency of these schedulers is developed and used to design experiments targeted at measuring scheduler latency. Detailed benchmarking of four of the most popular schedulers (Slurm, Son of Grid Engine, Mesos, and Hadoop YARN) is conducted. The theoretical model is compared with data and demonstrates that scheduler performance can be characterized by two key parameters: the marginal latency of the scheduler ts and a nonlinear exponent αs. For all four schedulers, the utilization of the computing system decreases to <10% for computations lasting only a few seconds. Multi-level schedulers (such as LLMapReduce) that transparently aggregate short computations can improve utilization for these short computations to >90% for all four of the schedulers that were tested.
Keywords: Scheduler ، Resource manager ، Job scheduler ، High performance computing ، Data analytics
Big data-informed energy efficiency assessment of China industry sectors based on K-means clustering
ارزیابی کارآیی انرژی ارزیابی انرژی در بخش های صنعتی چین بر اساس الگوریتم K-means خوشه بندی-2018
The regional energy management body has a large amount of regional industrial companies’ energy consumption data. It can evaluate the energy utilization of listed regional industrial companies based on the total data and, then, find the key points for understanding the resources usage patterns, identifying the problematic companies, and establishing good energy consumption practices. This paper reviews the research progress on big data analysis and industrial energy efficiency evaluation and focuses on the energy efficiency evaluation methods based on energy consumption process analysis and big data mining approach. Based on K-means and multi-dimensional association rules algorithm, to analyze the charac teristics of regional energy consumption in different industries and companies, we cluster single industry in K-means and finding their levels of water and energy consumption. This classification provided us a reference point to identify the industries and companies to focus on and locate the bad consumption practices and environmental performance. Then, multi-dimensional association rules are used to find the correlation of processes, companies and energy efficiency to guide the energy conservation in regional energy monitor. The output of our research is a working Big Data analytics platform and the results generated from advance analytics techniques applied specifically to solve regional energy efficiency problems.
Keywords: Big-data ، Energy efficiency assessment ، K-means ، Multi-dimension association rules
رابطه بین رفتار جستجوی اطلاعات و رفتار نوآورانه در دانشجویان پرستاری چینی
سال انتشار: 2018 - تعداد صفحات فایل pdf انگلیسی: 5 - تعداد صفحات فایل doc فارسی: 19
زمینه: در اقتصاد مبتنی بر اطلاعات، سواد اطلاعاتی پایه و اساس سواد علمی شده است و پایه ای را برای رشد نوآورانه فراهم می کند. بررسی رابطه بین رفتارهای جستجوی اطلاعات و رفتارهای نوآورانه دانشجویان پرستاری می تواند به رهنمود توسعه آموزش و تربیت سواد اطلاعاتی برای دانشجویان پرستاری کمک کند. با این حال رابطه بین رفتار جستجوی اطلاعات و رفتار نوآورانه در دانشجویان پرستاری توجه اندکی را به خود معطوف کرده است.
هدف: هدف این مطالعه بررسی رابطه بین رفتار جستجوی اطلاعات و رفتار نوآورانه دانشجویان پرستاری است.
روشها: دانشجویان پرستاری در استان هونان چین با یک مقیاس رفتار جستجوی اطلاعات و مقیاس رفتار نوآورانه ارزیابی شدند.
نتایج: درکل 1247 دانشجوی پرستاری در تحلیل نهایی شامل شدند. نتایج نشان داد که هم رفتار جستجوی اطلاعات و هم رفتار نوآورانه به صورت قابل توجهی در دانشجویان دوره لیسانس نسبت به دانش آموزان دبیرستانی پرستاری (0.01 > P) و در فوق لیسانس ها نسبت به لیسانس ها (0.01 > P) بهتر بود. سطح کلی رفتار جستجوی اطلاعات در دانشجویان پرستاری رابطه مثبتی با رفتار نوآورانه داشت (0.63 = r، 0.01 > P) و 7 بُعد رفتار جستجوی اطلاعات نیز با رفتار نوآورانه در درجات مختلفی همبسته بودند. به علاوه، استفاده از اطلاعات اثبات شد که قوی ترین پیش بینی کننده رفتار نوآورانه است.
نتیجه گیری: رفتار جستجوی اطلاعات رابطه مثبتی با رفتار نوآورانه دربین دانجشویان پرستاری داشت. یک نیاز به یکپارچه سازی آموزش سواد اطلاعاتی با دوره های بازیابی اطلاعات به ویژه در جنبه های استفاده از اطلاعات، بازیابی و بررسی آنها وجود دارد.
کلیدواژه ها: رفتار جستجوی اطلاعات | رفتار نوآوری | تحلیل همبستگی | دانشجویان پرستاری | سواد اطلاعاتی
|مقاله ترجمه شده|
Big data analytics for wireless and wired network design: A survey
تجزیه و تحلیل داده های بزرگ برای طراحی شبکه بی سیم و سیمی: یک مرور-2018
Currently, the world is witnessing a mounting avalanche of data due to the increasing number of mobile network subscribers, Internet websites, and online services. This trend is continuing to develop in a quick and diverse manner in the form of big data. Big data analytics can process large amounts of raw data and extract useful, smaller-sized information, which can be used by different parties to make reliable decisions. In this paper, we conduct a survey on the role that big data analytics can play in the design of data communication networks. Integrating the latest advances that employ big data analytics with the net works’ control/traffic layers might be the best way to build robust data communication networks with refined performance and intelligent features. First, the survey starts with the introduction of the big data basic concepts, framework, and characteristics. Second, we illustrate the main network design cycle em ploying big data analytics. This cycle represents the umbrella concept that unifies the surveyed topics. Third, there is a detailed review of the current academic and industrial efforts toward network design using big data analytics. Forth, we identify the challenges confronting the utilization of big data analytics in network design. Finally, we highlight several future research directions. To the best of our knowledge, this is the first survey that addresses the use of big data analytics techniques for the design of a broad range of networks.
Keywords: Big data analytics , Network design , Self-optimization , Self-configuration , Self-healing network
The IoT for smart sustainable cities of the future: An analytical framework for sensor-based big data applications for environmental sustainability
اینترنت اشیا برای شهرهای پایدار هوشمند از آینده: یک چارچوب تحلیلی برای کاربردهای داده های بزرگ مبتنی بر حسگر برای سازگاری با محیط زیست-2018
The Internet of Things (IoT) is one of the key components of the ICT infrastructure of smart sustainable cities as an emerging urban development approach due to its great potential to advance environmental sustainability. As one of the prevalent ICT visions or computing paradigms, the IoT is associated with big data analytics, which is clearly on a penetrative path across many urban domains for optimizing energy efficiency and mitigating en vironmental effects. This pertains mainly to the effective utilization of natural resources, the intelligent man agement of infrastructures and facilities, and the enhanced delivery of services in support of the environment. As such, the IoT and related big data applications can play a key role in catalyzing and improving the process of environmentally sustainable development. However, topical studies tend to deal largely with the IoT and related big data applications in connection with economic growth and the quality of life in the realm of smart cities, and largely ignore their role in improving environmental sustainability in the context of smart sustainable cities of the future. In addition, several advanced technologies are being used in smart cities without making any con tribution to environmental sustainability, and the strategies through which sustainable cities can be achieved fall short in considering advanced technologies. Therefore, the aim of this paper is to review and synthesize the relevant literature with the objective of identifying and discussing the state-of-the-art sensor-based big data applications enabled by the IoT for environmental sustainability and related data processing platforms and computing models in the context of smart sustainable cities of the future. Also, this paper identifies the key challenges pertaining to the IoT and big data analytics, as well as discusses some of the associated open issues. Furthermore, it explores the opportunity of augmenting the informational landscape of smart sustainable cities with big data applications to achieve the required level of environmental sustainability. In doing so, it proposes a framework which brings together a large number of previous studies on smart cities and sustainable cities, including research directed at a more conceptual, analytical, and overarching level, as well as research on specific technologies and their novel applications. The goal of this study suits a mix of two research approaches: topical literature review and thematic analysis. In terms of originality, no study has been conducted on the IoT and related big data applications in the context of smart sustainable cities, and this paper provides a basis for urban researchers to draw on this analytical framework in future research. The proposed framework, which can be replicated, tested, and evaluated in empirical research, will add additional depth to studies in the field of smart sustainable cities. This paper serves to inform urban planners, scholars, ICT experts, and other city sta keholders about the environmental benefits that can be gained from implementing smart sustainable city in itiatives and projects on the basis of the IoT and related big data applications.
Keywords: Smart sustainable cities , The IoT , Big data analytics , Sensor technology , Data processing platforms , Environmental sustainability , Big data applications , Cloud computing , Fog/edge computing
A disease diagnosis and treatment recommendation system based on big data mining and cloud computing
سیستم تشخیص بیماری و درمان مبتنی بر کاوش داده های بزرگ و محاسبات ابری-2018
It is crucial to provide compatible treatment schemes for a disease according to various symptoms at different stages. However, most classification methods might be ineffective in accurately classifying a disease that holds the characteristics of multiple treatment stages, various symptoms, and multi-pathogenesis. Moreover, there are limited exchanges and co operative actions in disease diagnoses and treatments between different departments and hospitals. Thus, when new diseases occur with atypical symptoms, inexperienced doctors might have difficulty in identifying them promptly and accurately. Therefore, to maximize the utilization of the advanced medical technology of developed hospitals and the rich medical knowledge of experienced doctors, a Disease Diagnosis and Treatment Recommen dation System (DDTRS) is proposed in this paper. First, to effectively identify disease symp toms more accurately, a Density-Peaked Clustering Analysis (DPCA) algorithm is introduced for disease-symptom clustering. In addition, association analyses on Disease-Diagnosis (D D) rules and Disease-Treatment (D-T) rules are conducted by the Apriori algorithm sep arately. The appropriate diagnosis and treatment schemes are recommended for patients and inexperienced doctors, even if they are in a limited therapeutic environment. More over, to reach the goals of high performance and low latency response, we implement a parallel solution for DDTRS using the Apache Spark cloud platform. Extensive experi mental results demonstrate that the proposed DDTRS realizes disease-symptom clustering effectively and derives disease treatment recommendations intelligently and accurately.
Keywords: Big data mining ، Cloud computing ، Disease diagnosis and treatment ، Recommendation system