دانلود و نمایش مقالات مرتبط با Big data::صفحه 1
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نتیجه جستجو - Big data

تعداد مقالات یافته شده: 966
ردیف عنوان نوع
1 استفاده از رسانه های اجتماعی برای شناسایی جذابیت گردشگری در شش شهر ایتالیا
سال انتشار: 2019 - تعداد صفحات فایل pdf انگلیسی: 7 - تعداد صفحات فایل doc فارسی: 18
تکامل فناوری و گسترش شبکه های اجتماعی به افراد اجازه داده است که مقادیر زیادی داده را در هر روز تولید کنند. شبکه های اجتماعی کاربرانی را فارهم می کند که به اطلاعات دسترسی دارند. هدف این مقاله تعیین جذابیت های شهرهای مختلف گردشگری ازطریق بررسی رفتار کاربران در شبکه های اجتماعی می باشد. پایگاه داده ای شامل عکس های جغرافیایی واقع شده در شش شهر می باشد که به عنوان یک مرکز فرهنگی و هنری در ایتالیا عمل می کنند. عکس ها از فلیکر که یک بستر به اشتراک گذاری داده می باشد دانلود شدند. تحلیل داده ها با استفاده از دیدگاه مدلهای یادگیری ریاضی و ماشینی انجام شد. نتایج مطالعه ما نشانگر نقشه های شناسایی رفتار کاربران، گرایش سالانه به فعالیت تصویری در شهرها و تاکید بر سودمند بودن روش پیشنهادی می باشد که قادر به تامین اطلاعات مکانی و کاربری است. این مطالعه تاکید می کند که چگونه تحلیل داده های اجتماعی می تواند یک مدل پیشگویانه برای فرموله کردن طرح های گردشگری خلق کند. در انتها، راهبردهای عمومی بازاریابی گردشگری مورد بحث قرار می گیرند.
مقاله ترجمه شده
2 Unsupervised by any other name: Hidden layers of knowledge production in artificial intelligence on social media
بدون نظارت با هر نام دیگری: لایه های پنهان تولید دانش در هوش مصنوعی در رسانه های اجتماعی-2019
Artificial Intelligence (AI) in the form of different machine learning models is applied to Big Data as a way to turn data into valuable knowledge. The rhetoric is that ensuing predictions work well—with a high degree of autonomy and automation. We argue that we need to analyze the process of applying machine learning in depth and highlight at what point human knowledge production takes place in seemingly autonomous work. This article reintroduces classification theory as an important framework for understanding such seemingly invisible knowledge production in the machine learning development and design processes. We suggest a framework for studying such classification closely tied to different steps in the work process and exemplify the framework on two experiments with machine learning applied to Facebook data from one of our labs. By doing so we demonstrate ways in which classification and potential discrimination take place in even seemingly unsupervised and autonomous models. Moving away from concepts of non-supervision and autonomy enable us to understand the underlying classificatory dispositifs in the work process and that this form of analysis constitutes a first step towards governance of artificial intelligence.
Keywords: Artificial intelligence | machine learning | classification | social media| Facebook | discrimination | bias
مقاله انگلیسی
3 When data is capital: Datafication, accumulation, and extraction
وقتی داده سرمایه است: داده سازی، انباشت و استخراج-2019
The collection and circulation of data is now a central element of increasingly more sectors of contemporary capitalism. This article analyses data as a form of capital that is distinct from, but has its roots in, economic capital. Data collection is driven by the perpetual cycle of capital accumulation, which in turn drives capital to construct and rely upon a universe in which everything is made of data. The imperative to capture all data, from all sources, by any means possible influences many key decisions about business models, political governance, and technological development. This article argues that many common practices of data accumulation should actually be understood in terms of data extraction, wherein data is taken with little regard for consent and compensation. By understanding data as a form capital, we can better analyse the meaning, practices, and implications of datafication as a political economic regime.
Keywords: Big Data | digital capitalism | value | political economy | Marx | Bourdieu
مقاله انگلیسی
4 Conceptual frameworks for social and cultural Big Data analytics: Answering the epistemological challenge
چارچوب مفهومی برای تجزیه و تحلیل داده های بزرگ اجتماعی و فرهنگی : پاسخ به چالش معرفت شناختی-2019
This paper aims to contribute to the development of tools to support an analysis of Big Data as manifestations of social processes and human behaviour. Such a task demands both an understanding of the epistemological challenge posed by the Big Data phenomenon and a critical assessment of the offers and promises coming from the area of Big Data analytics. This paper draws upon the critical social and data scientists’ view on Big Data as an epistemological challenge that stems not only from the sheer volume of digital data but, predominantly, from the proliferation of the narrow-technological and the positivist views on data. Adoption of the social-scientific epistemological stance presupposes that digital data was conceptualised as manifestations of the social. In order to answer the epistemological challenge, social scientists need to extend the repertoire of social scientific theories and conceptual frameworks that may inform the analysis of the social in the age of Big Data. However, an ‘epistemological revolution’ discourse on Big Data may hinder the integration of the social scientific knowledge into the Big Data analytics.
Keywords: Social and cultural Big Data analytics | social science | computational science | epistemological challenge | social media
مقاله انگلیسی
5 Medical research, Big Data and the need for privacy by design
تحقیقات پزشکی، داده های بزرگ و نیاز به حریم خصوصی توسط طراحی-2019
Medical research data is sensitive personal data that needs to be protected from unauthorized access and unintentional disclosure. In a research setting, sharing of (big) data within the scientific community is necessary in order to make progress and maximize scientific benefits derived from valuable and costly data. At the same time, convincingly protecting the privacy of people (patients) participating in medical research is a prerequisite for maintaining trust and willingness to share. In this commentary, we will address this issue and the pitfalls involved in the context of the PEP project1 that provides the infrastructure for the Personalized Parkinson’s Project,2 a large cohort study on Parkinson’s disease from Radboud University Medical Center (Radboudumc), in cooperation with Verily life Sciences, an Alphabet subsidiary.
Keywords:Big Data | GDPR compliance | informed consent | medical cohort study | polymorphic encryption | privacy by design
مقاله انگلیسی
6 The optical unconscious of Big Data: Datafication of vision and care for unknown futures
ناخودآگاه نوری از داده های بزرگ: به دست آوردن بینایی و مراقبت از آگاهی های ناشناخته-2019
Ever since Big Data became a mot du jour across social fields, optical metaphors such as the microscope began to surface in popular discourse to describe and qualify its epistemological impact. While the persistence of optics seems to be at odds with the datafication of vision, this article suggests that the optical metaphor offers an opportunity to reflect about the material consequences of the modes of seeing and knowing that currently shape datafied worlds. Drawing on feminist new materialism, the article investigates the optical metaphor as a material-discursive practice that actively constitutes the world, as metaphors imply modes of thinking, knowing and doing that have material enactions. Expanding visual culture theories, the notion of ‘optical unconscious’ is taken up to discuss the tensions between displacement and persistence of optics within datafied worlds, that is, how optical vision is displaced but also mobilised and repurposed by data-driven knowledge. In dialogue with feminist science and technology studies and speculative ethics, I suggest that the datafication of vision offers a chance to reconceptualize the sense of sight towards a sensorial engagement with Big Data premised on responsibility, care, and an ethics of unknowability. Within this framework, vision may be conceived differently, perhaps not only as enhancement and control, but as generator of new possibilities. Ultimately, the article proposes that the visual theories after which Big Data is being imagined matter not only for our understanding of Big Data’s epistemic potential, but also for the possibility of shaping emerging data worlds.
Keywords: Optical unconscious | datafication of vision | speculative ethics | care | feminist materialism | metaphors
مقاله انگلیسی
7 Big Data, precision medicine and private insurance: A delicate balancing act
داده های بزرگ، پزشکی دقیق و بیمه خصوصی: یک موازنه ظریف-2019
In this paper, we discuss how access to health-related data by private insurers, other than affecting the interests of prospective policy-holders, can also influence their propensity to make personal data available for research purposes. We take the case of national precision medicine initiatives as an illustrative example of this possible tendency. Precision medicine pools together unprecedented amounts of genetic as well as phenotypic data. The possibility that private insurers could claim access to such rapidly accumulating biomedical Big Data or to health-related information derived from it would discourage people from enrolling in precision medicine studies. Should that be the case, the economic value of personal data for the insurance industry would end up affecting the public value of data as a scientific resource. In what follows we articulate three principles – trustworthiness, openness and evidence – to address this problem and tame its potentially harmful effects on the development of precision medicine and, more generally, on the advancement of medical science.
Keywords: Precision medicine | Big Data | information asymmetry | ethics | insurance | adverse selection
مقاله انگلیسی
8 Mapping the political landscape of Persian Twitter: The case of 2013 presidential election
نقشه چشم انداز سیاسی توییتر فارسی: مورد انتخابات ریاست جمهوری سال 2013-2019
The fallacy of premature designations such as ‘‘Iran’s Twitter Revolution’’ can be attributed to the empirical gap in our knowledge about such sociotechnical phenomena in non-Western societies. To fill this gap, we need in-depth analyses of social media use in those contexts and to create detailed maps of online public environments in such societies. This paper aims to present such cartography of the political landscape of Persian Twitter by studying the case of Iran’s 2013 presidential election. The objective of this study is twofold: first, to fill the empirical gap in our knowledge about Twitter use in Iran, and second, to develop computational methods for studying Persian Twitter (e.g., effective methods for analyzing Persian text) and identify the best methods for addressing different issues (e.g., topic detection and sentiment analysis). During Iran’s 2013 presidential election, three million tweets were collected and analyzed using social network analysis and machine learning. The findings provide a more nuanced view of the political landscape of Persian Twitter and identify patterns in accordance with or in contrast to those identified in the English-speaking Twittersphere around the 2013 presidential election. Persian Twitter was dominated by micro-celebrities, whereas institutional elites dominated English discourse about Iran on Twitter. The results also illustrate that Persian Twitter in 2013 was predominantly in favor of reformists. Finally, this study demonstrates that sentiment analysis toward political name entities can be used efficiently for mapping the political landscape of conversation on Twitter.
Keywords: Twitter | Iran | social network analysis | political landscape | computational methods | Big Data
مقاله انگلیسی
9 بازدیدهای آنلاین: تفاوت ها ازنظر وسیله بازدید
سال انتشار: 2019 - تعداد صفحات فایل pdf انگلیسی: 4 - تعداد صفحات فایل doc فارسی: 12
این مطالعه نقش ایفا شده توسط وسیله بازدید (موبایل یا رایانه) را دررفتار بازدید آنلاین از سایت های مسافرتی بررسی می کند. ما بیش از 2/1 میلیون بازدید آنلاین از سایت Booking.com را تحلیل می کنیم و وجود و ویژگی های متمایز بازدیدهای آنلاین صورت گرفته توسط وسایل موبایلی را آشکار می سازیم. یافته های ما بیان می کنند که 1) سهم بازدیدهای آنلاین صورت گرفته توسط موبایل با گذشت زمان با نرخ بسیار بالایی افزایش یافته است (بالاتر از نرخ رشد بازدیدهای صورت گرفته توسط رایانه)؛ 2) یک تفاوت سیستماتیک و ازنظر آماری معنادار بین ویژگی ها و توزیع های بازدیدهای آنلاین صورت گرفته توسط وسایل موبایلی دربرابر بازدیدهای آنلاین صورت گرفته توسط رایانه ها وجود دارد. ما میزان آگاهی از نقش ایفا شده توسط وسایل بازدید آنلاین از سایت های مسافرتی را بالا می بریم و دلالت های موجود برای تحقیقات آتی را ارائه می دهیم.
مقاله ترجمه شده
10 Toward modeling and optimization of features selection in Big Data based social Internet of Things
به سوی مدل سازی و بهینه سازی انتخاب ویژگی ها در داده های بزرگ مبتنی بر اینترنت اشیا اجتماعی-2018
The growing gap between users and the Big Data analytics requires innovative tools that address the challenges faced by big data volume, variety, and velocity. Therefore, it becomes computationally inefficient to analyze and select features from such massive volume of data. Moreover, advancements in the field of Big Data application and data science poses additional challenges, where a selection of appropriate features and High-Performance Computing (HPC) solution has become a key issue and has attracted attention in recent years. Therefore, keeping in view the needs above, there is a requirement for a system that can efficiently select features and analyze a stream of Big Data within their requirements. Hence, this paper presents a system architecture that selects features by using Artificial Bee Colony (ABC). Moreover, a Kalman filter is used in Hadoop ecosystem that is used for removal of noise. Furthermore, traditional MapReduce with ABC is used that enhance the processing efficiency. Moreover, a complete four-tier architecture is also proposed that efficiently aggregate the data, eliminate unnecessary data, and analyze the data by the proposed Hadoop-based ABC algorithm. To check the efficiency of the proposed algorithms exploited in the proposed system architecture, we have implemented our proposed system using Hadoop and MapReduce with the ABC algorithm. ABC algorithm is used to select features, whereas, MapReduce is supported by a parallel algorithm that efficiently processes a huge volume of data sets. The system is implemented using MapReduce tool at the top of the Hadoop parallel nodes with near real time. Moreover, the proposed system is compared with Swarm approaches and is evaluated regarding efficiency, accuracy and throughput by using ten different data sets. The results show that the proposed system is more scalable and efficient in selecting features.
Keywords: SIoT ، Big Data ، ABC algorithm، Feature selection
مقاله انگلیسی
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