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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Data Mining Strategies for Real-Time Control in New York City
استراتژی داده کاوی برای کنترل زمان واقعی در شهر نیویورک-2105
The Data Mining System (DMS) at New York City Department of Transportation (NYCDOT) mainly consists of four database systems for traffic and pedestrian/bicycle volumes, crash data, and signal timing plans as well as the Midtown in Motion (MIM) systems which are used as part of the NYCDOT Intelligent Transportation System (ITS) infrastructure. These database and control systems are operated by different units at NYCDOT as an independent database or operation system. New York City experiences heavy traffic volumes, pedestrians and cyclists in each Central Business District (CBD) area and along key arterial systems. There are consistent and urgent needs in New York City for real-time control to improve mobility and safety for all users of the street networks, and to provide a timely response and management of random incidents. Therefore, it is necessary to develop an integrated DMS for effective real-time control and active transportation management (ATM) in New York City. This paper will present new strategies for New York City suggesting the development of efficient and cost-effective DMS, involving: 1) use of new technology applications such as tablets and smartphone with Global Positioning System (GPS) and wireless communication features for data collection and reduction; 2) interface development among existing database and control systems; and 3) integrated DMS deployment with macroscopic and mesoscopic simulation models in Manhattan. This study paper also suggests a complete data mining process for real-time control with traditional static data, current real timing data from loop detectors, microwave sensors, and video cameras, and new real-time data using the GPS data. GPS data, including using taxi and bus GPS information, and smartphone applications can be obtained in all weather conditions and during anytime of the day. GPS data and smartphone application in NYCDOT DMS is discussed herein as a new concept. © 2014 The Authors. Published by Elsevier B.V. Selection and peer-review under responsibility of Elhadi M. Shakshu Keywords: Data Mining System (DMS), New York City, real-time control, active transportation management (ATM), GPS data
Big data for internet of things: A survey
داده های بزرگ برای اینترنت اشیا: یک مرور-2018
With the rapid development of the Internet of Things (IoT), Big Data technolo gies have emerged as a critical data analytics tool to bring the knowledge within IoT infrastructures to better meet the purpose of the IoT systems and support critical decision making. Although the topic of Big Data analytics itself is ex tensively researched, the disparity between IoT domains (such as healthcare, energy, transportation and others) has isolated the evolution of Big Data ap proaches in each domain. Thus, the mutual understanding across IoT domains can possibly advance the evolution of Big Data research in IoT. In this work, we therefore conduct a survey on Big Data technologies in different IoT domains to facilitate and stimulate knowledge sharing across the IoT domains. Based on our review, this paper discusses the similarities and differences among Big Data technologies used in different IoT domains, suggests how certain Big Data technology used in one IoT domain can be re-used in another IoT domain, and develops a conceptual framework to outline the critical Big Data technologies across all the reviewed IoT domains.
Keywords: Big Data, data analytics, Internet of Things, healthcare, energy, transportation, building automation, Smart Cities
Emerging social media data on measuring urban park use
داده های جدید رسانه های اجتماعی در مورد اندازه گیری استفاده از پارک های شهری-2018
Green urban infrastructure, which serves the interests of both human and nature, are considered as essential assets of urban residents. However, measuring the use of green space has been problematic. Because, most previously used data on measuring green space use are self-reported or collected via social surveys, which not only include limited samples, but also are always subjective, costly, and laborious. This study integrates sensor and positioning technologies and measures the use of green space from an emerging big data per spective. The hourly real-time Tencent user density (RTUD) data from social media are used to analyze the time-spatial distribution of urban park users. RTUD data, park attributes, and surrounding landscape fea tures are incorporated into ArcGIS for spatial analysis. A group of linear regression models is constructed to determine factors that may be associated with the user density of urban parks. The total accumulated number of observed users is 3.25 million in 686 urban parks of Shenzhen in two typical sunny days – a work day and a rest day. Without costly and laborious field investigation, based on RTUD data, we conduct a city wide analysis regarding all parks. The proposed method is proved to be suitable for measuring urban green space use at city-level or even large scale. Results show that park user density is relatively high in well developed areas and community parks. Park attributes and surrounding landscape features are significantly associated to park use. The findings of this study can help policy makers optimize the construction and maintenance of urban parks.
Keywords: Green space ، RTUD data ، Shenzhen ، Time-spatial distribution، User density
The convergence of new computing paradigms and big data analytics methodologies for online social networks
همگرایی پارادایم های محاسباتی جدید و تجزیه و تحلیل داده های بزرگ روش ها برای شبکه های اجتماعی آنلاین-2018
Over past decade, the developments of Web 3.0, Web 4.0 and Science 2.0 have become critical network infrastructure and knowl edge platform for all socially organized participating entities (man, machine, group, and even brain-like computer) for exchanging, sharing, contributing a great amount of data, information, knowl edge. Meanwhile, the popularity of online social networks tools, platforms, applications and services spurs much more interactions and collaboration at larger scale than ever before. The better lever age of those social big data for improving social network services depends on new computing paradigms and analytics methodolo gies to a great extent, such as social-sensed multimedia computing, aware computing and situation analytics, and so on. In addition, various forms of attacks constantly occur, including identity theft, social fishing, impersonation attack, hijack, image retrieval and analysis, fake requests, and Sybil and other malicious software attacks . Malicious attacks also came from social bots . Resolv ing of all the challenging issue does need more effective and efficient computing and analysis methods.
A course on big data analytics
دوره ای در تجزیه و تحلیل داده های بزرگ-2018
This report details a course on big data analytics designed for undergraduate junior and senior computer science students. The course is heavily focused on projects and writing code for big data processing. It is designed to help students learn parallel and distributed computing frameworks and techniques commonly used in industry. The curriculum includes a progression of projects requiring increasingly sophisticated big data processing ranging from data preprocessing with Linux tools, distributed pro cessing with Hadoop MapReduce and Spark, and database queries with Hive and Google’s BigQuery. We discuss hardware infrastructure and experimentally evaluate the cost/benefit of an on-premise server versus Amazon’s Elastic MapReduce. Finally, we showcase outcomes of our course in terms of student engagement and anonymous student feedback.
Keywords: Curriculum ، Undergraduate education ، Big data ،Cloud computing
Human-intelligence workflow management for the big data of augmented reality on cloud infrastructure
مدیریت داده های انسانی هوشمند برای داده های بزرگ واقعیت افزوده بر زیر ساخت های ابر-2018
Human-intelligence workflow management (HIWM) is proposed as a means of dynamically distributing and processing storage work and calculating operations for fast augmented reality (AR) service provision on diverse smart mobile devices based on human behavior to apply the next generation web environ ments. In HIWM, pre-processing is performed to minimize service response time according to the def inition of metadata and user requests for AR services. Basically, to process big data for AR services, a dynamic job distribution scheme is proposed based on the computing capacity of desktops constituting the cloud infrastructures. For final AR services by HIWM, the results of the evaluation of the performance of HIWM in relation to big data processing time are presented. The results show that processing time is 40.56% less than that of the existed methods in proportion to AR service requests.
Keywords: Human-intelligence workflow management ، Big data ، Cloud infrastructure
Train Delay Prediction Systems: A Big Data Analytics Perspective
سیستم پیش بینی تأخیر قطار: چشم انداز تحلیل داده های بزرگ-2018
Current train delay prediction systems do not take advantage of state-of-the-art tools and techniques for handling and extracting useful and actionable information from the large amount of historical train movements data collected by the railway information systems. Instead, they rely on static rules built by experts of the railway infrastructure based on classical univariate statistic. The purpose of this paper is to build a data-driven Train Delay Prediction System (TDPS) for large-scale railway networks which exploits the most recent big data technologies, learning algorithms, and statistical tools. In particular, we propose a fast learning algorithm for Shallow and Deep Extreme Learning Machines that fully exploits the recent in-memory large-scale data processing technologies for predicting train delays. Proposal has been compared with the current state-of-the-art TDPSs. Results on real world data coming from the Italian railway network show that our proposal is able to improve over the current state-of-the-art TDPSs.
Keywords: Railway network ، Train Delay Prediction systems ، Big data analytics ، Extreme learning machines ، Shallow architecture ، Deep architecture
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
How information technology influences opportunity exploration and exploitation firm’s capabilities
چگونه فناوری اطلاعات بر قابلیت هاییک شرکت در اکتشاف و استخراج فرصت اثر می گذارد-2018
Understanding how and why some firms have proficiency in exploring and exploiting opportunities is a cutting-edge research problem. Our central thesis is that information technology (IT) performs a key role in firms’ opportunity exploration and exploitation. We test the proposed theory using partial least squares path modeling on a combination of survey and secondary data from 203 Spanish firms. We find that: (1) IT infrastructure provides the foundation to build business experimentation and the flexibility to sense and explore business opportunities; and (2) IT-enabled business flexibility helps firms to develop the operational proficiency to exploit opportunities and increase their performance.
keywords: IT infrastructure|Business flexibility| Exploration and exploitation| Business opportunities| Business value of IT
A multi-layered performance analysis for cloud-based topic detection and tracking in Big Data applications
تجزیه و تحلیل عملکرد چند لایه برای تشخیص و ردیابی موضوع مبتنی بر ابر در برنامه های داده های بزرگ -2018
In the era of the Internet of Things and social media; communities, governments, and corporations are increasingly eager to exploit new technological innovations in order to track and keep up to date with important new events. Examples of such events include the news, health related incidents, and other major occurrences such as earthquakes and landslides. This area of research commonly referred to as Topic Detection and Tracking (TDT) is proving to be an important component of the current generation of Internet-based applications, where it is of critical importance to have early detection and timely response to important incidents such as those mentioned above. The advent of Big data though beneficial to TDT applications also brings about the enormous challenge of dealing with data variety, velocity and volume (3Vs). A promising solution is to employ Cloud Computing, which enables users to access powerful and scalable computational and storage resources in a ‘‘pay-as-you-go’’ fashion. However, the efficient use of Cloud resources to boost the performance of mission critical applications employing TDT is still an open topic that has not been fully and effectively investigated. An important prerequisite is to build a performance analysis capable of capturing and explaining specific factors (for example; CPU, Memory, I/O, Network, Cloud Platform Service, and Workload) that influence the performances of TDT applications in the cloud. Within this paper, our main contribution, is that we present a multi-layered performance analysis for big data TDT applications deployed in a cloud environment. Our analysis captures factors that have an important effect on the performance of TDT applications. The novelty of our work is that it is a first kind of vertical analysis on infrastructure, platform and software layers. We identify key parameters and metrics in each cloud layer (including Infrastructure, Software, and Platform layers), and establish the dependencies between these metrics across the layers. We demonstrate the effectiveness of the proposed analysis via experimental evaluations using real-world datasets obtained from Twitter.
Keywords: Cloud-based TDT ، Big Data ، Performance analysis ، Cloud computing