دانلود و نمایش مقالات مرتبط با Web service::صفحه 1
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نتیجه جستجو - Web service

تعداد مقالات یافته شده: 47
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1 مروری بر تجمیع دستگاه های مدل سازی اطلاعات ساختمانی (BIM) و اینترنت اشیاء (IoT): وضعیت کنونی و روند آینده
سال انتشار: 2019 - تعداد صفحات فایل pdf انگلیسی: 13 - تعداد صفحات فایل doc فارسی: 56
تجمیع مدل سازی اطلاعات ساختمانی (BIM) با داده های زمان واقعی(بلادرنگ) دستگاه های اینترنت اشیاء (IoT)، نمونه قوی را برای بهبود ساخت وساز و بهره وری عملیاتی ارائه می دهد. اتصال جریان-های داده های زمان واقعی که بر گرفته از مجموعه هایی از شبکه های حسگرِ اینترنت اشیاء (که این جریان های داده ای، به سرعت در حال گسترش هستند) می باشند، با مدل های باکیفیت BIM، در کاربردهای متعددی قابل استفاده می باشد. با این حال، پژوهش در زمینه ی تجمیع BIM و IOT هنوز در مراحل اولیه ی خود قرار دارد و نیاز است تا وضعیت فعلی تجمیع دستگاه های BIM و IoT درک شود. این مقاله با هدف شناسایی زمینه های کاربردی نوظهور و شناسایی الگوهای طراحی رایج در رویکردی که مخالف با تجمیع دستگاه BIM-IoT می باشد، مرور جامعی در این زمینه انجام می دهد و به بررسی محدودیت های حاضر و پیش بینی مسیرهای تحقیقاتی آینده می پردازد. در این مقاله، در مجموع، 97 مقاله از 14 مجله مربوط به AEC و پایگاه داده های موجود در صنایع دیگر (در دهه گذشته)، مورد بررسی قرار گرفتند. چندین حوزه ی رایج در این زمینه تحت عناوین عملیات ساخت-وساز و نظارت، مدیریت ایمنی و بهداشت، لجستیک و مدیریت ساختمان، و مدیریت تسهیلات شناسایی شدند. نویسندگان، 5 روش تجمیع را همراه با ذکر توضیحات، نمونه ها و بحث های مربوط به آنها به طور خلاصه بیان کرده اند. این روش های تجمیع از ابزارهایی همچون واسط های برنامه نویسی BIM، پایگاه داده های رابطه ای، تبدیل داده های BIM به پایگاه داده های رابطه ای با استفاده از طرح داده های جدید، ایجاد زبان پرس وجوی جدید، فناوری های وب معنایی و رویکردهای ترکیبی، استفاده می کنند. براساس محدودیت های مشاهده شده، با تمرکز بر الگوهای معماری سرویس گرا (SOA) و راهبردهای مبتنی بر وب برای ادغام BIM و IoT، ایجاد استانداردهایی برای تجمیع و مدیریت اطلاعات، حل مسئله همکاری و محاسبات ابری، مسیرهای برجسته ای برای تحقیقات آینده پیشنهاد شده است.
کلمه های کلیدی: مدل سازی اطلاعات ساختمانی (BIM) | دستگاه اینترنت اشیاء (IoT) | حسگرها | ساختمان هوشمند | شهر هوشمند | محیط ساخته شده هوشمند | تجمیع.
مقاله ترجمه شده
2 A comparative analysis of machine learning models for quality pillar assessment of SaaS services by multi-class text classification of users’ reviews
تجزیه و تحلیل مقایسه ای مدل های یادگیری ماشین برای ارزیابی ستون کیفیت خدمات SaaS با طبقه بندی متن چند طبقه از نظرات کاربران-2019
Software as a Service (SaaS) has emerged as the most widely used of all the current software delivery models. With the growth of edge computing, as SaaS services increasingly become distributed, selecting the best SaaS provider from those available is challenging but it is of critical importance. In the recent past, well-known cloud service providers such as Amazon Web Services and Microsoft have developed frameworks and service quality pillars for cloud applications. However, there are currently no mechanisms for product users to know if and to what extent a service satisfies the defined service pillar. Having such information would enable users to form trustworthy associations in edge computing. In this paper, we address this drawback by adopting a systematic approach of analysing customer reviews related to SaaS products and ascertain to which service quality pillar they refer. We use eleven traditional machine learning classification approaches and a weighted voting ensemble of these classifiers to achieve this task and test the performance of each of them. Since the dataset is unbalanced in terms of sample distribution per class, we use 10-fold cross-validation on the training dataset to determine the best parameters for each machine learning algorithm to achieve optimal performance. Friedman test and Nemenyi’s post hoc test is applied to identify the significant difference among the classifiers performance during cross-validation. Based on the experimental results, a comparative analysis is conducted to identify the best performing machine learning classification model on the SaaS reviews. The results show that the performance of the logistic regression model has a higher performance among the individual classifiers and the weighted voting ensemble shows minimal improvement in overall performance.
Keywords: SaaS | Quality pillars | User reviews | Text classification | Machine learning approaches
مقاله انگلیسی
3 یک چارچوب عامل گرا برای سیستم های نظیر به-نظیر باز بر روی بلاکچین و IPFS
سال انتشار: 2018 - تعداد صفحات فایل pdf انگلیسی: 6 - تعداد صفحات فایل doc فارسی: 11
چکیده: در سال های اخیر نگرانی های فزاینده در مورد سرویس های ابری متمرکز(به عنوان مثال: محرمانگی، حاکمیت، نظارت، امنیت) ضرورت استفاده از فناوری های توزیع شده از قبیل بلاکچین یا IPFS را ایجاد نموده است. این اختراعات دارای مشکلات فنی هستند که تا بروز آنها حل نشده بودند. مدل های فعلی نظیر-به-نظیر نیاز به یک بازبینی برای پوشش طیفی از سیستم های بالقوه دارد تا بتواند به عنوان یک سیستم نظیر-به-نظیر پیاده سازی شود. این مقاله یک چارچوب برای ساختن این سیستم ها ارائه می دهد. این چارچوب یک روش عامل-گرا در یک محیط باز ارائه می دهد که عاملها تنها به قسمتی از داده های سیستم دسترسی دارند. این راهکار دسترسی به داده، اکتشاف داده و اعتماد داده را با متعاملین مختلف را پوشش می دهد. علاوه بر این، این چارچوب یک معماری توزیع شده برای سیستم های باز ارائه می کند که توصیه هایی برای تصمیم گیری در حالت های فناوری بلاکچین و ... فراهم آورده است.
مقاله ترجمه شده
4 OCPMDM: Online computation platform for materials data mining
OCPMDM: پلت فرم محاسبات آنلاین برای داده کاوی مواد-2018
With the rapid development of the Materials Genome Initiative (MGI), scientists and engineers are confronted with the need to conduct sophisticated data analytics in modeling the behaviors of materials. Nowadays, it is inconvenient for material researchers to carry out materials data mining work without an efficient platform for materials machine learning. So, it is meaningful to develop an online platform for material researchers in urgent need of using machine learning techniques by themselves. The typical case study is given to demonstrate the applications of the online computation platform for material data mining (OCPMDM) in our lab: The quantitative structure property relationship (QSPR) model for rapid prediction of Curie temperature of perovskite material can be applied to screen out perovskite candidates with higher Curie temperature than those of training dataset collected from references, efficiently. Material data mining tasks can be implemented via the OCPMDM, which provides powerful tools for material researchers in machine learning-assisted materials design and optimization.
Keywords: Machine learning ، Data mining ، Materials design ، MGI ، Web service
مقاله انگلیسی
5 AWESoME: Big Data for Automatic Web Service Management in SDN
AWESoME: داده های بزرگ برای مدیریت و سرویس های خودکار در SDN-2018
Software defined network (SDN) has enabled consistent and programmable management in computer networks. However, the explosion of cloud services and content delivery networks (CDNs)—coupled with the momentum of encryption—challenges the simple per-flow management and calls for a more comprehensive approach for managing Web traffic. We propose a new approach based on a “per service” management concept, which allows to identify and prioritize all traffic of important Web services, while segregating others, even if they are running on the same cloud platform, or served by the same CDN. We design and evaluate AWESoME, automatic Web service manager, a novel SDN application to address the above problem. On the one hand, it leverages big data algorithms to automatically build models describing the traffic of thousands of Web services. On the other hand, it uses the models to install rules in SDN switches to steer all flows related to the originating services. Using traffic traces from volunteers and operational networks, we provide extensive experimental results to show that AWESoME associates flows to the corresponding Web service in real-time and with high accuracy. AWESoME introduces a negligible load on the SDN controller and installs a limited number of rules on switches, hence scaling well in realistic deployments. Finally, for easy reproducibility, we release ground truth traces and scripts implementing AWESoME core components.
Index Terms: Computer network management, software defined networking, machine learning
مقاله انگلیسی
6 Water utility decision support through the semantic web of things
پشتیبانی تصمیم گیری ابزار آب از طریق وب معنایی اشیا-2018
Urban environments are urgently required to become smarter. However, building advanced applications on the Internet of Things requires seamless interoperability. This paper proposes a water knowledge management platform which extends the Internet of Things towards a Semantic Web of Things, by leveraging the semantic web to address the heterogeneity of web resources. Proof of concept is demonstrated through a decision support tool which leverages both the data-driven and knowledge based programming interfaces of the platform. The solution is grounded in a comprehensive ontology and rule base developed with industry experts. This is instantiated from GIS, sensor, and EPANET data for a Welsh pilot. The web service provides dis coverability, context, and meaning for the sensor readings stored in a scalable database. An interface displays sensor data and fault inference notifications, leveraging the complementary nature of serving coherent lower and higher-order knowledge.
Keywords: Water management ، Decision support tool ، Interoperability ، Big data ، Ontology ، Semantic web ، Internet of things ، Smart water networks
مقاله انگلیسی
7 Ontology-Based Web Service Architecture for Retail Supply Chain Management
هستی شناسی مبتنی بر معماری-وب سرویس برای مدیریت زنجیره تامین خرده فروشی-2018
Service-oriented computing (SOC) technologies provide numerous opportunities and value-added service capabilities that global retail business requires to remain competitive in the market. Initiative to semantic web service provision is playing a crucial role to realize the possibility of heterogenous information systems integration in supply chain. The ability to dynamically discover and invoke a web service is an important aspects of semantic web service-based architecture. An essential part of the service discovery process is the ontology-based semantic web service matchmaking algorithm. This paper presents the key features of an improved matchmaking algorithm to calculate the similarity between concepts on ontology for semantic web service. The matchmaking is taking place in the context of OWL-S (Ontology Web Language for Services) based retail sales management. The paper describes the Semantic Web Service Architecture-II (SWSA-II), which uses a hybrid knowledge-based system; and it consists of Structural Case-Based Reasoning (S-CBR), Rule-Based Reasoning (RBR), and an ontological concept similarity assessment algorithm. Finally, a business scenario is used to demonstrate the functionality of the algorithm.
Keywords: Retail Supply Chain; Service Oriented Computing; Semantic Web Services; Case-Based Reasoning; Rule-Based Reasoning; Ontology Matchmaking Algorithm
مقاله انگلیسی
8 A security authorization scheme for smart home Internet of Things devices
یک طرح مجوز امنیتی برای دستگاه های خانه هوشمند و اینترنت اشیاء-2017
The Internet of Things (IoT) is becoming an important factor in many areas of our society. IoT brings intel ligence to critical aspects like transportation, industry, payments, health and many others. The interaction between embedded devices and Cloud based web services is a common scenario of IoT deployment. From the security point of view, both users and smart devices must establish a secure communication channel and have a form of digital identity. Most of the times, the usage of IoT devices requires an already existing infrastructure which cannot be controlled by the device owner, for instance in a smart home. This scenario requires a security stack suitable for heterogeneous devices which can be integrated in already existing operating systems or IoT frameworks. This paper proposes a lightweight authorization stack for smart home IoT applications, where a Cloud-connected device relays input commands to a user’s smart-phone for authorization. This architecture is user-device centric and addresses security issues in the context of an untrusted Cloud platform.
Keywords: IoT | Security | Identity | Embedded | Cloud computing | Smart home
مقاله انگلیسی
9 Measuring web service security in the era of Internet of Things
اندازه گیری امنیت سرویس وب در عصر اینترنت اشیاء -2017
Technologies such as Internet of Things allow small devices to offer web-based services in an open and dynamic networking environments on a massive scale. End users or service consumers face a hard decision over which service to choose among the available ones, as security holds a key in the decision making process. In this paper a base linguistic evalua tion set is designed, based on which all the other fuzzy term sets that used for describing security attributes are uniformed and integrated for calculating an overall security value of the services. This work, to the best of our knowledge, is the first practical solution to offer direct comparisons and rankings of network services based on multiple security at tributes such as confidentiality, availability, privacy and accountability. We analysed four major cloud service platforms to illustrate the proposed approach.
Keywords: Web service | Security measurement and evaluation | Quantitative service security | Service level agreement | Linguistic evaluation | Multiple attribute decision making
مقاله انگلیسی
10 CloudIntell: An intelligent malware detection system
CloudIntell: یک سیستم تشخیص بدافزار هوشمند-2017
Enterprises and individual users heavily rely on the abilities of antiviruses and other security mechanisms. However, the methodologies used by such software are not enough to detect and prevent most of the malicious activities and also consume a huge amount of resources of the host machine for their regular operations. In this paper, we propose a combination of machine learning techniques applied on a rich set of features extracted from a large dataset of benign and malicious files through a bespoke feature extraction tool. We extracted a rich set of features from each file and applied support vector machine, decision tree, and boosting on decision tree to get the highest possible detection rate. We also introduce a cloud-based scalable architecture hosted on Amazon web services to cater the needs of detection methodology. We tested our methodology against different scenarios and generated high achieving results with lowest energy consumption of the host machine.
Keywords: Malware analysis | Machine learning | Cloud | Decision tree | Boosting | SVM | Security | Malware detection | Portable executable | AWS
مقاله انگلیسی
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