دانلود و نمایش مقالات مرتبط با داده های بزرگ::صفحه 1
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نتیجه جستجو - داده های بزرگ

تعداد مقالات یافته شده: 879
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 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
3 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
4 Differential Privacy Preserving of Training Model in Wireless Big Data with Edge Computing
حفظ حریم خصوصی دیفرانسیل حفظ مدل آموزش در داده های بزرگ بی سیم با محاسبات لبه-2018
With the popularity of smart devices and the widespread use of machine learning methods, smart edges have become the mainstream of dealing with wireless big data. When smart edges use machine learning models to analyze wireless big data, nevertheless, some models may unintentionally store a small portion of the training data with sensitive records. Thus, intruders can expose sensitive information by careful analysis of this model. To solve this privacy issue, in this paper, we propose and implement a machine learning strategy for smart edges using differential privacy. We focus our attention on privacy protection in training datasets in wireless big data scenario. Moreover, we guarantee privacy protection by adding Laplace mechanisms, and design two different algorithms Output Perturbation (OPP) and Objective Perturbation (OJP), which satisfy differential privacy. In addition, we consider the privacy preserving issues presented in the existing literatures for differential privacy in the correlated datasets, and further provided differential privacy preserving methods for correlated datasets, guaranteeing privacy by theoretical deduction. Finally, we implement the experiments on the TensorFlow, and evaluate our strategy on four datasets, i.e., MNIST, SVHN, CIFAR-10 and STL-10. The experiment results show that our methods can efficiently protect the privacy of training datasets and guarantee the accuracy on benchmark datasets.
Index Terms: Wireless Big Data, Smart Edges, Differential Privacy, Training Data Privacy, Machine Learning, Correlated Datasets, Laplacian Mechanism, TensorFlow
مقاله انگلیسی
5 A new architecture of Internet of Things and big data ecosystem for secured smart healthcare monitoring and alerting system
معماری جدید اینترنت اشیاء و اکوسیستم داده های بزرگ برای نظارت بر سیستم مراقبت سلامت هوشمند و سیستم هشدار دهنده امن-2018
Wearable medical devices with sensor continuously generate enormous data which is often called as big data mixed with structured and unstructured data. Due to the complexity of the data, it is difficult to process and analyze the big data for finding valuable information that can be useful in decision making. On the other hand, data security is a key requirement in healthcare big data system. In order to overcome this issue, this paper proposes a new architecture for the implementation of IoT to store and process scalable sensor data (big data) for health care applications. The Proposed architecture consists of two main sub architectures, namely, Meta Fog-Redirection (MF-R) and Grouping and Choosing (GC) architecture. MF-R architecture uses big data technologies such as Apache Pig and Apache HBase for collection and storage of the sensor data (big data) generated from different sensor devices. The proposed GC architecture is used for securing integration of fog computing with cloud computing. This architecture also uses key management service and data categorization function (Sensitive, Critical and Normal) for providing security services. The framework also uses MapReduce based prediction model to predict the heart diseases. Performance evaluation parameters such as throughput, sensitivity, accuracy, and f-measure are calculated to prove the efficiency of the proposed architecture as well as the prediction model.
Keywords: Wireless sensor networks ، Internet of Things ، Big data analytics ، Cloud computing and health car
مقاله انگلیسی
6 Role of Big Data and Machine Learning in Diagnostic Decision Support in Radiology
نقش داده های بزرگ و یادگیری ماشین در پشتیبانی از تصمیم گیری تشخیصی در رادیولوژی-2018
The field of diagnostic decision support in radiology is undergoing rapid transformation with the availability of large amounts of patient data and the development of new artificial intelligence methods of machine learning such as deep learning. They hold the promise of providing imaging specialists with tools for improving the accuracy and efficiency of diagnosis and treatment. In this article, we will describe the growth of this field for radiology and outline general trends highlighting progress in the field of diagnostic decision support from the early days of rule-based expert systems to cognitive assistants of the modern era.
Key Words: Diagnostic decision support, artificial intelligence, deep learning, machine learning, cognitive assistants, medical image analysis, knowledge and reasoning
مقاله انگلیسی
7 A Review of Policies concerning development of Big Data Industry in Pakistan
بررسی سیاست های مربوط به توسعه صنعت داده های بزرگ در پاکستان-2018
This In the present globalized smart ecosystem, various suggestions of using data as a new tool for the development of the economy are still going on to be presented. Hence, developed countries are trying to pursue different policy measures to develop the big data industry, including promoting big data R&D sector and investment in human resources to retain the pace of this global trend. The government of Pakistan has supported liberal policies to activate the IT its applications such as big data, Internet of Things (IOT) and electronic government (e-government). We used the Analytic Network Process (ANP) model to prioritize policy measures and find out its implications for Pakistan. This study will convey an important lesson for developing countries and particularly for South Asian countries to establish policies for developing big data as a new tool for economic growth in the context of smart ecosystem environment.
Keywords: Big data; Internet of things; IOT; Analytic network process; Ecosystem
مقاله انگلیسی
8 Scheduling workflows with privacy protection constraints for big data applications on cloud
جریان های برنامه ریزی شده با محدودیت های حفاظت از حریم خصوصی برای برنامه های داده بزرگ در ابر-2018
Nowadays, business or scientific processes with massive big data in Cyber-Physical-Social environments are springing up in cloud. Cloud customers’ private information stored in cloud may be easily exposed and lead to serious privacy leakage issues in Cyber-Physical-Social environments. To avoid such issues, cloud customers’ privacy or sensitive data may be restricted to being processed by some specific trusted cloud data centers. Therefore, a new problem is how to schedule workflow with such data privacy protection constraints, while minimizing both execution time and monetary cost for big data applications on cloud. In this paper, we model such problem as a multi-objective optimization problem and propose a Multi Objective Privacy-Aware workflow scheduling algorithm, named MOPA. It can provide cloud customers with a set of Pareto tradeoff solutions. The problem-specific encoding and population initialization are proposed in this algorithm. The experimental results show that our algorithm can obtain higher quality solutions when compared with other ones.
Keywords: Privacy protection ، Workflow scheduling ، Cloud ، Big data ، Multi-objective optimization
مقاله انگلیسی
9 Bridging data-capacity gap in big data storage
شکاف ظرفیت داده ها در ذخیره سازی داده های بزرگ-2018
Big data is aggressive in its production, and with the merger of Cloud computing and IoT, the huge volumes of data generated are increasingly challenging the storage capacity of data centres. This has led to a growing data-capacity gap in big data storage. Unfortunately, the limitations faced by current storage technologies have severely handicapped their potential to meet the storage demand of big data. Consequently, storage technologies with higher storage density, throughput and lifetime have been researched to overcome this gap. In this paper, we first introduce the working principles of three such emerging storage technologies, and justify their inclusion in the study based on the tremendous advances received by them in the recent past. These storage technologies include Optical data storage, DNA data storage & Holographic data storage. We then evaluate the recent advances received in storage density, throughput and lifetime of these emerging storage technologies, and compare them with the trends and advances in prevailing storage technologies. We finally discuss the implications of their adoption, evaluate their prospects, and highlight the challenges faced by them to bridge the data-capacity gap in big data storage.
Keywords: Big data ، Data-capacity gap ، Optical storage ، DNA storage ، Holographic storage ، Magnetic storage
مقاله انگلیسی
10 Intelligent and effective informatic deconvolution of “Big Data” and its future impact on the quantitative nature of neurodegenerative disease therapy
هوش انعطاف پذیر و موثر اطلاعاتی "داده های بزرگ" و تأثیر آن بر ماهیت کمی در درمان بیماری های نورودنژراتیک-2018
Biomedical data sets are becoming increasingly larger and a plethora of high-dimensionality data sets (“Big Data”) are now freely accessible for neurodegenerative diseases, such as Alzheimer’s disease. It is thus important that new informatic analysis platforms are developed that allow the organization and interrogation of Big Data resources into a rational and actionable mechanism for advanced therapeutic development. This will entail the generation of systems and tools that allow the cross-platform correlation between data sets of distinct types, for example, transcriptomic, pro teomic, and metabolomic. Here, we provide a comprehensive overview of the latest strategies, including latent semantic analytics, topological data investigation, and deep learning techniques that will drive the future development of diagnostic and therapeutic applications for Alzheimer’s dis ease. We contend that diverse informatic “Big Data” platforms should be synergistically designed with more advanced chemical/drug and cellular/tissue-based phenotypic analytical predictive models to assist in either de novo drug design or effective drug repurposing.
Keywords: Big data; Informatics; High-dimensionality; Alzheimer’s disease; Aging; Molecular signature; Transcriptomics; Metabolomics; Proteomics; Genomics
مقاله انگلیسی
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