In-Mapper combiner based MapReduce algorithm for processing of big climate data
الگوریتم MapReduce مبتنی بر ترکیب Mapper در پردازش داده های آب و هوایی بزرگ -2018
Big data refers to a collection of massive volume of data that cannot be processed by conventional data processing tools and technologies. In recent years, the data production sources are enlarged noticeably, such as high-end streaming devices, wireless sensor networks, satellite, wearable Internet of Things (IoT) devices. These data generation sources generate a massive volume of data in a continuous manner. The large volume of climate data is collected from the IoT weather sensor devices and NCEP. In this paper, the big data processing framework is proposed to integrate climate and health data and to find the correlation between the climate parameters and incidence of dengue. This framework is demonstrated with the help of MapReduce programming model, Hive, HBase and ArcGIS in a Hadoop Distributed File System (HDFS) environment. The following weather parameters such as minimum temperature, maximum temperature, wind, precipitation, solar and relative humidity are collected for the study are Tamil Nadu with the help of IoT weather sensor devices and NCEP. Proposed framework focuses only on climate data for 32 districts of Tamil Nadu where each district contains 1,57,680 rows and so there are 50,45,760 rows in total. Batch view precomputation for the monthly mean of various climate parameters would require 50,45,760 rows. Hence, this would create more latency in query processing. In order to overcome this issue, batch views can precompute for a smaller number of records and involve more computation to be done at query time. The In-Mapper based MapReduce framework is used to compute the monthly mean of climate parameter for each latitude and longitude. The experimental results prove the effectiveness of the response time for the In-Mapper based combiner algorithm is less when compared with the existing MapReduce algorithm.
Keywords: Big data ، Internet of Things ، Weather sensor devices ، MapReduce programming ،Model ، Hadoop distributed file system
Big Data Analytics in Industrial IoT Using a Concentric Computing Model
تجزیه و تحلیل داده های بزرگ در اینترنت اشیا صنعتی با استفاده از یک مدل محاسباتی مرکزی-2018
The unprecedented proliferation of miniaturized sensors and intelligent communication, computing, and control technologies have paved the way for the development of the Industrial Internet of Things. The IIoT incorporates machine learning and massively parallel distributed systems such as clouds, clusters, and grids for big data storage, processing, and analytics. In IIoT, end devices continuously generate and transmit data streams, resulting in increased network traffic between device-cloud communication. Moreover, it increases in-network data transmissions. requiring additional efforts for big data processing, management, and analytics. To cope with these engendered issues, this article first introduces a novel concentric computing model (CCM) paradigm composed of sensing systems, outer and inner gateway processors, and central processors (outer and inner) for the deployment of big data analytics applications in IIoT. Second, we investigate, highlight, and report recent research efforts directed at the IIoT paradigm with respect to big data analytics. Third, we identify and discuss indispensable challenges that remain to be addressed for employing CCM in the IIoT paradigm. Lastly, we provide several future research directions (e.g., real-time data analytics, data integration, transmission of meaningful data, edge analytics, real-time fusion of streaming data, and security and privacy).
Keywords: Big Data, data analysis, Internet of Things,learning (artificial intelligence)
A Big Data Analytics Architecture for the Internet of Small Things
معماری تحلیل داده های بزرگ برای اینترنت اشیا کوچک-2018
The SK Telecom Company of South Korea recently introduced the concept of IoST to its business model. The company deployed IoST, which constantly generates data via the LoRa wireless platform. The increase in data rates generated by IoST is escalating exponentially. After attempting to analyze and store the massive volume of IoST data using existing tools and technologies, the South Korean company realized the shortcomings immediately. The current article addresses some of the issues and presents a big data analytics architecture for its IoST. A system developed using the proposed architecture will be able to analyze and store IoST data efficiently while enabling better decisions. The proposed architecture is composed of four layers, namely the small things layer, infrastructure layer, platform layer, and application layer. Finally, a detailed analysis of a big data implementation of the IoST used to track humidity and temperature via Hadoop is presented as a proof of concept.
Keywords: Big Data, data analysis, Internet of Things, parallel programming
Wireless Big Data: Technologies and Applications
داده های بزرگ بی سیم: فن آوری ها و برنامه های کاربردی-2018
The thirteen papers in this special section focus on the topic of wireless Big Data applications. Powered by advanced analytics methods, big data has emerged as a promising paradigm to handle voluminous and complex data. Recently, to cope with the emerging fifth generation (5G) and Internet of Things (IoT), wireless big data affords us an unprecedented opportunity to obtain an in-depth understanding of wireless things and facilitate data-driven approaches for network optimization and operation. The papers in this section aim to tackle the challenges and consolidate timely theory and applications of wireless big data.
Keywords: Special issues and sections,Big Data,Internet of Things,Wireless communication,5G mobile communication
The impact of China’s 2016 Cyber Security Law on foreign technology firms, and on China’s big data and Smart City dreams
تأثیر قانون امنیت سایبری 2016 چین در مورد شرکت های فن آوری خارجی، و داده های بزرگ چین و رویاهای شهرهای هوشمند-2018
Chinese officials are increasingly turning to a policy known as Informatisation, connecting industry online, to utilise technology to improve efficiency and tackle economic develop mental problems in China. However, various recent laws have made foreign technology firms uneasy about perceptions of Rule of Law in China. Will these new laws, under China’s stated policy of “Network Sovereignty” (“网络主权” “wangluo zhuquan”) affect China’s ability to attract foreign technology firms, talent and importantly technology transfers? Will they slow China’s technology and Smart City drive? This paper focuses on the question of whether interna tional fears of China’s new Cyber Security Law are justified. In Parts I and II, the paper analyses why China needs a cyber security regime. In Parts III and IV it examines the law itself.
Keywords: China ، Big data ، The Internet of Things ، Smart Cities ، Network Sovereignty ، Rule of Law ، Cyber Security Laws
Vehicular Content Delivery: A Big Data Perspective
تحویل محتویات خودرو: چشم انداز داده های بزرگ-2018
The appearance of the Internet of Vehicles enables comfort driving experiences and content- rich multimedia services for in-vehicle users. The vehicular network provides specific scenario- centric content delivery services involving data of vehicle status, user behaviors, and environmental features. In this article, we focus on vehicular content delivery from a big data perspective. After a comprehensive review of state-of-the-art works, we elaborate the potential value of big data in vehicular information and content services by introducing several typical application scenarios. According to the data characteristics, we classify the vehicular data into three categories, that is, location-centric, user-centric, and vehicle-centric, and then illustrate an implementation of big data collection and analysis. A real-world big data application in social-based vehicular networks is presented, and simulation results show that the big-data-enabled content delivery strategy can obtain a performance gain of user satisfaction with the delivered contents compared to the case without consideration of social big data. Finally, we conclude the article with several future research topics
Keywords: Big Data, data analysis,Internet, Internet of Things,mobile radio, multimedia communication,social networking (online)
Data-driven smart manufacturing
تولید هوشمند مبتنی بر داده ها-2018
The advances in the internet technology, internet of things, cloud computing, big data, and artificial intelligence have profoundly impacted manufacturing. The volume of data collected in manufacturing is growing. Big data offers a tremendous opportunity in the transformation of today’s manufacturing paradigm to smart manufacturing. Big data empowers companies to adopt data-driven strategies to become more competitive. In this paper, the role of big data in supporting smart manufacturing is dis cussed. A historical perspective to data lifecycle in manufacturing is overviewed. The big data perspective is supported by a conceptual framework proposed in the paper. Typical application scenarios of the proposed framework are outlined.
Keywords: Big data ، Smart manufacturing ، Manufacturing data ، Data lifecycle
ادغام ERP کلان داده و تجزیه و تحلیل کسب و کار (BA)
سال انتشار: 2018 - تعداد صفحات فایل pdf انگلیسی: 10 - تعداد صفحات فایل doc فارسی: 22
پیشرفت های فن آوری در محاسبات ابری، سیستم های کلان داده ، پایگاه داده بدون SQL، سیستم های شناختی، یادگیری عمیق و سایر تکنیک های هوش مصنوعی باعث یکپارچگی داده های تراکنشی ERP سنتی و جریان کلان داده از سیستم های مختلف رسانه های اجتماعی و اینترنت اشیا (IOTs) به سیستم تجزیه و تحلیل یکپارچه نه تنها قابل اجرا، بلکه اجتناب ناپذیر هستند. دو مرحله برای این ادغام برجسته شده است. نخست اینکه ERP کلان داده باعث ادغام داده های تراکنش های سنتی ERP و کلان داده ها شده است و دوم، باعث ادغام ERP کلان داده ها با فرآیند تجزیه و تحلیل کسب و کار (BA) شده است. همانطور که پیاده کنندگان سیستم ERP و کاربران BA با چالش های مختلف مواجه هستند، مدیران مسئول ادغام ERP-BA کلان داده نیز به طور جدی به چالش کشیده می شوند. برای کمک به آنها برای مقابله با این چالش ها، ما مدل SIST (هماهنگی استراتژیک، ادغام سرمایه فکری و اجتماعی و ادغام فناوری) را توسعه می دهیم و پیشنهاد می کنیم که این ادغام یک پروژۀ تکاملی با سطوح مختلف بلوغ برای عملکرد های مختلف کسب و کار است که احتمالا منجر به پایداری مزایای رقابتی می شود.
کلمات کلیدی: تجزیه و تحلیل کسب و کار | ERP | داده های بزرگ | مدل بلوغ | چشم انداز پورتفولیو | مزایای رقابتی پایدار
|مقاله ترجمه شده|
Big Data Reduction for a Smart Citys Critica Infrastructural Health Monitoring
کاهش داده های بزرگ برای شهرهای هوشمند بحرانی نظارت بر زیرساخت بهداشت-2018
Critical infrastructure monitoring is one of the most important applications of a smart city. The objective is to monitor the integrity of the struc tures (e.g., buildings, bridges) and detect and pinpoint the locations of possible events (e.g., damages, cracks). Regarding today’s complex structures, collecting data using wireless sen sor data over extensive vertical lengths creates enormous challenges. With a direct BS deploy ment, a big amount of data will accumulate to be relayed to the BS. As a result, traditional models and schemes developed for health monitoring are largely challenged by low-cost, quality-guaran teed, and real-time event monitoring. In this arti cle, we propose BigReduce, a cloud based health monitoring application with an IoT framework that could cover most of the key infrastructures of a smart city under an umbrella and provide event monitoring. To reduce the burden of big data processing at the BS and enhance the quality of event detection, we integrate real-time data pro cessing and intelligent decision making capabili ties with BigReduce. Particularly, we provide two innovative schemes for health event monitoring so that an IoT sensor can use them locally; one is a big data reduction scheme, and the other is a decision making scheme. We believe that BigRe duce will result in a remarkable performance in terms of data reduction, energy cost reduction, and the quality of monitoring.
Keywords: Big Data, cloud computing, condition monitoring, critical infrastructures, data reduction, decision making, Internet of Things, public dministration,smart cities, wireless sensor networks
Systematic survey of big data and data mining in internet of things
بررسی سیستماتیک داده های بزرگ و داده کاوی در اینترنت اشیا-2018
In recent years, the Internet of Things (IoT) has emerged as a new opportunity. Thus, all devices such as smartphones, transportation facilities, public services, and home appliances are used as data creator devices. All the electronic devices around us help our daily life. Devices such as wrist watches, emergency alarms, and garage doors and home appliances such as refrigerators, microwaves, air conditioning, and water heaters are connected to an IoT network and controlled remotely. Methods such as big data and data mining can be used to improve the efficiency of IoT and storage challenges of a large data volume and the transmission, analysis, and processing of the data volume on the IoT. The aim of this study is to investigate the research done on IoT using big data as well as data mining methods to identify subjects that must be emphasized more in current and future research paths. This article tries to achieve the goal by following the conference and journal articles published on IoT-big data and also IoT-data mining areas between 2010 and August 2017. In order to examine these articles, the combination of Systematic Mapping and literature review was used to create an intended review article. In this research, 44 articles were studied. These articles are divided into three categories: Architecture & Platform, framework, and application. In this research, a summary of the methods used in the area of IoT-big data and IoT-data mining is presented in three categories to provide a starting point for researchers in the future.
Keywords: Internet of things ، Systematic survey ، Big data ، Data mining