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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Applications of Big Social Media Data analysis: An Overview
برنامه های تحلیل داده های اجتماعی رسانه هابزرگ : یک مرور کلی-2018
Over the last few years, online communication has moved toward user-driven technologies, such as online social networks (OSNs), blogs, online virtual communities, and online sharing platforms. These social technologies have ushered in a revolution in user-generated data, online global communities, and rich human behavior-related content. Human-generated data and human mobility patterns have become important steps toward developing smart applications in many areas. Understanding human preferences is important to the development of smart applications and services to enable such applications to understand the thoughts and emotions of humans, and then act smartly based on learning from social media data. This paper discusses the role of social media data in comprehending online human data and in consequently different real applications of SM data for smart services are executed.
Keywords: Online social networks (OSN), social media (SM), big social data, machine learning, smart society
A novel adaptive e-learning model based on Big Data by using competence-based knowledge and social learner activities
یک مدل تطبیقی جدید یادگیری الکترونیکی مبتنی بر داده های بزرگ با استفاده ازدانش مبتنی بر شایستگی و فعالیت های یادگیرنده اجتماعی-2018
The e-learning paradigm is becoming one of the most important educational methods, which is a deci sive factor for learning and for making learning relevant. However, most existing e-learning platforms offer traditional e-learning system in order that learners access the same evaluation and learning con tent. In response, Big Data technology in the proposed adaptive e-learning model allowed to consider new approaches and new learning strategies. In this paper, we propose an adaptive e-learning model for providing the most suitable learning content for each learner. This model based on two levels of adaptive e-learning. The first level involves two steps: (1) determining the relevant future educational objectives through the adequate learner e-assessment method using MapReduce-based Genetic Algo rithm, (2) generating adaptive learning path for each learner using the MapReduce-based Ant Colony Optimization algorithm. In the second level, we propose MapReduce-based Social Networks Analysis for determining the learner motivation and social productivity in order to assign a specific learning rhythm to each learner. Finally, the experimental results show that the presented algorithms implemented on Big Data environment converge much better than those implementations with traditional concurrent works. Also, this work provides main benefit because it describes how Big Data technology transforms e-learning paradigm.
Keywords: Adaptive e-learning ، Big data ، MapReduce ، Genetic algorithm ، Personalized learning path ، Ant colony optimization algorithms ، Social networks analysis ، Motivation and productivity ، Learning content
رفع ابهام ازبیتکوین: پرده برداری از افسانه ارز دیجیتال
سال انتشار: 2018 - تعداد صفحات فایل pdf انگلیسی: 16 - تعداد صفحات فایل doc فارسی: 20
بیتکوین و سیستم معاملاتی عجیب و غیر متمرکز آن، در حال حاضر مورد علاقه معامله گران حرفه ای و خرده پای در جستجوی سود و مورد توجه اقتصاددانان و کارشناسان قانونی به دنبال قوانین احتمالی برای محدود کردن استفاده های غیر قانونی، است. ما به بررسی موفقیت غیر منتظره و مداوم بیتکوین از یک دیدگاه جامعه شناختی علاقه مند هستیم، که سوال اول در این راستا، سیستم مشروعیت غیر معمولی آن، که به جای اختیارات دولتی، بافن آوری بلاکچین پشتیبانی می شود، است. سپس به جمع آوری داده و المان هایی پرداختیم که تاریخچه بیتکوین را به عنوان یک رمزنگاری ارزی (ارز دیجیتال) بازسازی می کند که از داستان پر رمز و راز آن آغاز می شود که تولد آن را در برگرفته است. ما سپس، گسترش و توسعه آن را از طریق شبکه های اجتماعی و زبان کلمات، همراه با شیوع و توسعه عظیم ناگهانی آن پیگیری می کنیم و در آخر پیشنهاد می دهیم که مصرف کنندگان و قانون گزاران نهادی باید نسبت به ریسک های بیتکوین و قدرت ادعایی آن که مفهوم پول ما را به چالش کشیده است، هوشیار باشند.
کلمات کلیدی: بیتکوین | ارز دیجیتال | تکنولوژی بلاکچین (زنجیره بلوکی) | طرح های پونزی | اعتماد به پول
|مقاله ترجمه شده|
Tourists digital footprint in cities: Comparing Big Data sources
ردیابی دیجیتال گردشگران در شهرها: مقایسه منابع داده های بزرگ -2018
There is little knowledge available on the spatial behaviour of urban tourists, and yet tourists generate an enormous quantity of data when they visit cities. These data sources can be used to track their presence through their activities. The aim of this paper is to analyse the digital footprint of urban tourists through Big Data. Unlike other papers that use a single data source, this article examines three sources of data to reflect different tourism activities in cities: Panoramio (sightseeing), Foursquare (consumption), and Twitter (being connected-accommodation). The results show that the data from the three activities are partly spatially redundant and partly complementary, and allow the characterisation of multifunction tourist spaces and spaces specialising in one or various activities. The main conclusion is that it is not sufficient to use one data source to analyse the presence of tourists in cities; several must be used in a complementary manner.
Keywords: Urban tourism ، Big Data ، Photo-sharing services ، Social networks ، Spatial analysis ، GIS
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.
Customer Relationship Management and Big Data Enabled: Personalization & Customization of Services
مدیریت ارتباط با مشتری و داده های بزرگ فعال: شخصی سازی و سفارشی سازی خدمات-2018
The emergence of big data brings a new wave of Customer Relationship Management (CRM)’s strategies in supporting personalization and customization of sales, services and customer services. CRM needs big data for better customers experiences especially personalization and customization of services. Big data is a popular term used to describe data that is volume, velocity, variety, veracity, and value of data both structured and unstructured. Big data requires new tools and techniques to capture, store and analyse it and is used to improve decision making for enhancing customer management. The aim of the research is to examine big data for CRM’s scenario. The method of collection of data for this study was literature review and thematic analysis from recent studies. The study reveals that CRM with big data has enabled business to become more aggressive in term of marketing strategy like push notification through smartphone to their potential target audiences.
Key words: Big Data, Data Analytics, CRM, Web 2.0, Social Networks
The power of a thumbs-up: Will e-commerce switch to social commerce?
توان یک توافق: آیا تجارت الکترونیک قابل تبدیل به تجارت اجتماعی است؟-2018
By taking advantage of social networking capabilities, social commerce provides features that encourage customers to share their personal experiences. The popularity of online social networks has driven the purchase decisions of buyers on social commerce sites, but few studies have explored why consumers switch between e-commerce (product-centered) and social (social-centered) commerce sites. In applying the push–pull–mooring model, the objective of this study was to gain an understanding of specifically how push, pull, and mooring factors shape their switching intentions. The findings revealed that push effect, in terms of low transaction efficiency, drives customers away from e-commerce sites, whereas the pull effects, including social presence, social support, social benefit, and self-presentation, attract customers to social commerce sites. Moreover, mooring effects, including conformity and personal experience, strengthened consumers’ behavior in switching between e-commerce and social commerce sites. Besides, conformity was also found to moderate the influences of social presence, social support, social benefit, and efficiency on switching intention, whereas personal experience moderated the effects of social benefit, self-presentation, and efficiency on switching intention. Such an understanding assists online retailers in understanding online shoppers’ switching behaviors, and thus turning social interactions into profits and sales.
keywords: Switching intention| Push–pull–mooring framework| Social commerce|E-commerce
CEO network centrality and bond ratings
مرکزیت شبکه CEO و رتبه بندی های باندی-2018
This study examines the impact of Chief Executive Officer (CEO) network centrality on bond ratings at the firm level. Using multiple dimensions of social connectedness, we find a significant positive relation between CEO network centrality and bond ratings, suggesting that firms with better connected CEOs are more likely to receive high bond ratings. Our results still hold after a battery of additional tests. We also find that firms with better connected CEOs experience lower cost of debt. Overall, our study supports the notion in social science research that well-connected individuals can bring benefits to their firms.
keywords: CEO network centrality| Bond credit rating| Social networks
Parallel algorithms for flexible pattern matching on big graphs
الگوریتم های موازی برای تطبیق الگوی انعطاف پذیر در نمودارهای بزرگ-2018
Strong simulation is a state-of-the-art approximate scheme in graph pattern matching. This scheme always finds high-quality results compared to other schemes. However, as the Web and social networks are increasingly used in human lives, the scale of the data grows ex tremely large. As a result, such big graphs are often stored in the distributed environment, in order to be managed efficiently. Unfortunately, current distributed algorithm for strong simulation is not efficient and cannot be applied to real applications. In this paper, we propose efficient parallel algorithms for strong simulation in the distributed setting. The contribution includes (1) We convert the calculation of strong simulation to calculating a relative small set of partial results for each partition of pattern suitable for distributed system. (2) We develop a method to reduce data shipment and time complexity of local computation in the distributed setting. (3) We split the distributed calculation of strong simulation into an offline redistribution algorithm and an online matching algorithm. The major data shipment is involved in the offline algorithm, while the online algorithm is highly parallel and much more efficient than current algorithms. (4) By experiments on both real and synthetic data, we verify the efficiency of our distributed algorithms and the effectiveness of our scheme without large intermediate results.
Keywords: Graph query ، Graph simulation ، Distributed algorithms ، Strong simulation
The role of location and social strength for friendship prediction in location-based social networks
نقش مکان و قدرت اجتماعی برای پیش بینی دوستی در شبکه های اجتماعی مبتنی بر مکان-2018
Recent advances in data mining and machine learning techniques are focused on exploiting location data. These advances, combined with the increased availability of location-acquisition technology, have encouraged social networking services to offer to their users different ways to share their location information. These social networks, called location-based social networks (LBSNs), have attracted millions of users and the attention of the research community. One fundamental task in the LBSN context is the friendship prediction due to its role in different applications such as recommendation systems. In the literature exists a variety of friendship prediction methods for LBSNs, but most of them give more importance to the location information of users and disregard the strength of relationships existing between these users. The contributions of this article are threefold, we: 1) carried out a comprehensive survey of methods for friendship prediction in LBSNs and proposed a taxonomy to organize the existing methods; 2) put forward a proposal of five new methods addressing gaps identified in our survey while striving to find a balance between optimizing computational resources and improving the predictive power; and 3) used a comprehensive evaluation to quantify the prediction abilities of ten current methods and our five proposals and selected the top-5 friendship prediction methods for LBSNs. We thus present a general panorama of friendship prediction task in the LBSN domain with balanced depth so as to facilitate research and real-world application design regarding this important issue.
keywords: Location-based social networks| Link prediction| Friendship recommendation| Human mobility| User behavior