با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد).
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Optimising virtual networks over time by using Windows Multiplicative DEA model
بهینه سازی شبکه های مجازی در طول زمان با استفاده از مدل تحلیل پوششی داده ها ویندوز ضربی-2019 Recently, the prediction of the most efficient configuration of a vast set of devices used for mounting an optimised cloud computing services and virtual networks environments have attracted growing atten- tion. This paper proposes a paradigm shift in modelling transmission control protocol (TCP) behaviour over time in virtual networks by using data envelopment analysis (DEA) models. Firstly, it proves that self-similarity with long-range dependency is presented differently in every network device. This study implements a novel fractal dimension concept on virtual networks for prediction, where this key in- dex informs if the transport layer forwards services with smooth or jagged behaviour over time. Another substantial contribution is proving that virtual network devices have a distinct fractal memory, TCP band- width performance, and fractal dimension over time, presenting themselves as important factor for fore- casting of spatiotemporal data. Thus, a continuous stepwise fractal performance evaluation framework methodology is developed as an expert system for virtual network assessment and performs a fractal analysis as a knowledge representation. In addition, due to the limitations of classical DEA models, the windows multiplicative data envelopment analysis (WMDEA) model is used to dynamically assess the fractal time series from virtual network hypervisors. For knowledge acquisition, 50 different virtual net- work hypervisors were appraised as decision-making units (DMU). Finally, this expert system also acts as a math hypervisor capable of determining the correct fractal pattern to follow when delivering TCP services in an optimised virtual network. Keywords: Cloud computing | Windows multiplicative data envelopment | analysis | Fractal expert system | Virtual Networks | Network Optimisation | Stepwise Performance Evaluation |
مقاله انگلیسی |
2 |
ViSiBiD: A learning model for early discovery and real-time prediction of severe clinical events using vital signs as big data
ViSiBiD: مدل های یادگیری برای کشف زودرس و پیش بینی زمان واقعی از حوادث بالینی شدید با استفاده از علائم حیاتی به عنوان داده های بزرگ-2017 The advance in wearable and wireless sensors technology have made it possible to monitor multiple vital
signs (e.g. heart rate, blood pressure) of a patient anytime, anywhere. Vital signs are an essential part
of daily monitoring and disease prevention. When multiple vital sign data from many patients are accumulated for a long period they evolve into big data. The objective of this study is to build a prognostic
model, ViSiBiD, that can accurately identify dangerous clinical events of a home-monitoring patient in
advance using knowledge learned from the patterns of multiple vital signs from a large number of similar patients. We developed an innovative technique that amalgamates existing data mining methods with
smartly extracted features from vital sign correlations, and demonstrated its effectiveness on cloud platforms through comparative evaluations that showed its potential to become a new tool for predictive
healthcare. Four clinical events are identified from 4893 patient records in publicly available databases
where six bio-signals deviate from normality and different features are extracted prior to 1–2 h from 10
to 30 min observed data of those events. Known data mining algorithms along with some MapReduce
implementations have been used for learning on a cloud platform. The best accuracy (95.85%) was obtained through a Random Forest classifier using all features. The encouraging learning performance using
hybrid feature space proves the existence of discriminatory patterns in vital sign big data can identify
severe clinical danger well ahead of time.
Keywords: Big data | Vital sign | Cloud computing | Correlations | Knowledge discovery | Data mining |
مقاله انگلیسی |
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MERRA Analytic Services: Meeting the Big Data challenges of climate science through cloud-enabled Climate Analytics-as-a-Service
خدمات تحلیلی MERRA: برآورده کردن چالش های داده های بزرگ علمی آب و هوایی از طریق تجزیه و تحلیل آب و هوا به عنوان یک سرویس یکپارچه شده توسط ابر-2017 Climate science is a Big Data domain that is experiencing unprecedented growth. In our efforts to address the Big
Data challenges of climate science, we are moving toward a notion of Climate Analytics-as-a-Service (CAaaS). We
focus on analytics, because it is the knowledge gained from our interactions with Big Data that ultimately produce
societal benefits. We focus on CAaaS because we believe it provides a useful way of thinking about the problem: a
specialization of the concept of business process-as-a-service, which is an evolving extension of IaaS, PaaS, and
SaaS enabled by Cloud Computing. Within this framework, Cloud Computing plays an important role; however,
we see it as only one element in a constellation of capabilities that are essential to delivering climate analytics as a
service. These elements are essential because in the aggregate they lead to generativity, a capacity for self
assembly that we feel is the key to solving many of the Big Data challenges in this domain. MERRA Analytic Ser
vices (MERRA/AS) is an example of cloud-enabled CAaaS built on this principle. MERRA/AS enables MapReduce
analytics over NASA’s Modern-Era Retrospective Analysis for Research and Applications (MERRA) data collection.
The MERRA reanalysis integrates observational data with numerical models to produce a global temporally and
spatially consistent synthesis of 26 key climate variables. It represents a type of data product that is of growing
importance to scientists doing climate change research and a wide range of decision support applications.
MERRA/AS brings together the following generative elements in a full, end-to-end demonstration of CAaaS capa
bilities: (1) high-performance, data proximal analytics, (2) scalable data management, (3) software appliance
virtualization, (4) adaptive analytics, and (5) a domain-harmonized API. The effectiveness of MERRA/AS has
been demonstrated in several applications. In our experience, Cloud Computing lowers the barriers and risk to
organizational change, fosters innovation and experimentation, facilitates technology transfer, and provides
the agility required to meet our customers’ increasing and changing needs. Cloud Computing is providing a
new tier in the data services stack that helps connect earthbound, enterprise-level data and computational re
sources to new customers and new mobility-driven applications and modes of work. For climate science,
Cloud Computing’s capacity to engage communities in the construction of new capabilities is perhaps the most
important link between Cloud Computing and Big Data.
Keywords:MapReduce|Hadoop|Data analytics|Data services|Cloud Computing|Generativity|iRODS|MERRA|ESGF|BAER |
مقاله انگلیسی |
4 |
تحقیق بر روی الگوریتم بهینهسازی پایگاه داده با مقیاس بزرگ تحت محیط محاسات ابری و اینترنت اشیا
سال انتشار: 2015 - تعداد صفحات فایل pdf انگلیسی: 5 - تعداد صفحات فایل doc فارسی: 10 در این مقاله، ما تحقیقی بر روی الگوریتم بهینهسازی پایگاه داده با مقیاس بزرگ تحت محیط محاسات ابری و اینترنت اشیا ارائه میدهیم. بر اساس فناوری محاسات ابری و مقدار بزرگ دادهها، پایگاه داده برای بسیاری از جنبههای تعدیل و بهینهسازی به منظور بهبود هر چه بیشتر بهرهوری عملکرد سیستم پایگاه داده و کاهش مصرف منبع عملکرد سیستم، با افزایش چشمگیر بهرهوری به این مقدار بزرگ داده پاسخ میدهد. تعدیل و بهینهسازی پایگاهدادهی چند سطحی، شامل بهینهسازی زمان اجرای پایگاه داده، بهینهسازی پارامتر پایگاه داده و کاربرد بهینهسازی مربوط به سه سطح میباشد. تحلیل عددی، عملیبودن و اثربخشی رویکرد پیشنهادی ما را اثبات میکند.
کلمات کلیدی: c. |
مقاله ترجمه شده |