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Potentials, trends, and prospects in edge technologies: Fog, cloudlet, mobile edge, and micro data centers
پتانسیل ها، گرایشات و چشم اندازها در روشهای لبه ای: مراکز داده ای مات، تکه ابر، لبه ای سیار و میکرو-2018
Advancements in smart devices, wearable gadgets, sensors, and communication paradigm have enabled the vision of smart cities, pervasive healthcare, augmented reality and interactive multimedia, Internet of Every Thing (IoE), and cognitive assistance, to name a few. All of these visions have one thing in common, i.e., delay sensitivity and instant response. Various new technologies designed to work at the edge of the network, such as fog computing, cloudlets, mobile edge computing, and micro data centers have emerged in the near past. We use the name ``edge computing for this set of emerging technologies. Edge computing is a promising paradigm to offer the required computation and storage resources with minimal delays because of ``being near to the users or terminal devices. Edge computing aims to bring cloud resources and services at the edge of the network, as a middle layer between end user and cloud data centers, to offer prompt service response with minimal delay. Two major aims of edge computing can be denoted as: (a) minimize response delay by servicing the users’ request at the network edge instead of servicing it at far located cloud data centers, and (b) minimize downward and upward traffic volumes in the network core. Minimization of network core traffic inherently brings energy efficiency and data cost reductions. Downward network traffic can be minimized by servicing set of users at network edge instead of service providers data centers (e.g., multimedia and shared data) Content Delivery Networks (CDNs), and upward traffic can be minimized by processing and filtering raw data (e.g., sensors monitored data) and uploading the processed information to cloud. This survey presents a detailed overview of potentials, trends, and challenges of edge computing. The survey illustrates a list of most significant applications and potentials in the area of edge computing. State of the art literature on edge computing domain is included in the survey to guide readers towards the current trends and future opportunities in the area of edge computing.
keywords: Edge computing| Fog computing| Internet of Things
Big data for internet of things: A survey
داده های بزرگ برای اینترنت اشیا: یک مرور-2018
With the rapid development of the Internet of Things (IoT), Big Data technolo gies have emerged as a critical data analytics tool to bring the knowledge within IoT infrastructures to better meet the purpose of the IoT systems and support critical decision making. Although the topic of Big Data analytics itself is ex tensively researched, the disparity between IoT domains (such as healthcare, energy, transportation and others) has isolated the evolution of Big Data ap proaches in each domain. Thus, the mutual understanding across IoT domains can possibly advance the evolution of Big Data research in IoT. In this work, we therefore conduct a survey on Big Data technologies in different IoT domains to facilitate and stimulate knowledge sharing across the IoT domains. Based on our review, this paper discusses the similarities and differences among Big Data technologies used in different IoT domains, suggests how certain Big Data technology used in one IoT domain can be re-used in another IoT domain, and develops a conceptual framework to outline the critical Big Data technologies across all the reviewed IoT domains.
Keywords: Big Data, data analytics, Internet of Things, healthcare, energy, transportation, building automation, Smart Cities
From big data to smart energy services: An application for intelligent energy management
از داده های بزرگ به سرویس های هوشمند انرژی: یک برنامه کاربردی برای مدیریت انرژی هوشمند-2018
Big data is an ascendant technological concepts and includes smart energy services, such as intelligent energy management, energy consumption prediction and exploitation of Internet of Things (IoT) solutions. As a result, big data technologies will have a significant impact in the energy sector. This paper proposes a high level architecture of a big data platform that can support the creation, development, maintenance and exploitation of smart energy services through the utilisation of cross-domain data. The proposed platform enables the simplification of the procedure followed for the information gathering by multiple sources, turning into actionable recommendations and meaningful operational insights for city authorities and local administrations, energy managers and consultants, energy service companies, utilities and energy providers. Α web-based Decision Support System (DSS) has been developed according to the proposed architecture, exploiting multi-sourced data within a smart city context towards the creation of energy management action plans. The pilot application of the developed DSS in three European cities is presented and discussed. This “data-driven” DSS can support energy managers and city authorities for managing their building facilities’ energy performance.
Keywords: Big Data; Decision Support System; Energy Services; Intelligent Management; Smart Cities.
Graph grammars according to the type of input and manipulated data: A survey
گرامر نمودار با توجه به نوع ورودی و دستکاری شده است داده ها: یک مرور-2018
Graph grammars which generate graphs are a generalization of Chomsky grammars that generate strings. During the last decades there has been a remarkable development of graph grammars. Due to their wide diversity of applications, graph grammars have received a particular attention from many scientists and researchers. There has been applications of graph grammars in several areas such as pattern recognition, data base systems, biological developments in organisms, semantics of programming languages, compiler construction, software development environments, etc. In the literature, in some surveys, graph grammars have been studied and classified according to some criteria such as: parallel or sequential applicability of rules, embedding mechanism, type of generated graphs, etc. In addition to this, as data play an important role more and more in different domains, we survey in this paper the vast field of graph grammars by classifying them according to three criteria: the number of manipulated data (single or multiple types), the nature of data (structured or unstructured), and finally the kind of data (images, graphs, patterns, etc.). In particular, we consider that a graph grammar is well defined by five components instead of four, namely: type of generated graphs (TG), a start graph (Z), a set of production rules (P), a set of additional specifications of the rules (A), and the criterion that we additionally consider which is the type of input and manipulated data (TD). This proposed formalism, especially with the added fifth component, may serve to overcome some issues related to Big Data and Cloud Computing domains.
Keywords: Graph grammar ، Type of input and manipulated data ، Type of generated graph ، Big Data ، Cloud computing ، Application
An experimental survey on big data frameworks
یک بررسی تجربی در چارچوب داده های بزرگ-2018
Recently, increasingly large amounts of data are generated from a variety of sources. Existing data pro cessing technologies are not suitable to cope with the huge amounts of generated data. Yet, many research works focus on Big Data, a buzzword referring to the processing of massive volumes of (unstructured) data. Recently proposed frameworks for Big Data applications help to store, analyze and process the data. In this paper, we discuss the challenges of Big Data and we survey existing Big Data frameworks. We also present an experimental evaluation and a comparative study of the most popular Big Data frameworks with several representative batch and iterative workloads. This survey is concluded with a presentation of best practices related to the use of studied frameworks in several application domains such as machine learning, graph processing and real-world applications.
Keywords: Big data ، MapReduce ، Hadoop ، HDFS ، Spark ، Flink ، Storm ،Samza ، Batch/stream processing
Parent-child interaction therapy as a prevention model for childhood obesity: A novel application for high-risk families
درمان تعامل والدین - بچه به عنوان یک مدل جلوگیری برای چاقی بچه: یک کاربرد نوین برای خانواده های پرخطر-2018
Childhood obesity is a formidable public health issue in the United States. Although childhood obesity risk is complex and influenced by multiple systems and individual domains, there is increasing appreciation for the impact of the family environment generally, and parent-child interactions specifically, on childrens levels of risk. Longitudinal research has identified parenting style and quality of parent-child interactions as important targets for reducing child obesity risk. Although, obesity prevention programs have attempted to change general parenting practices to prevent obesity (Haines et al., 2016; Harvey-Berino & Rourke, 2003; Østbye et al., 2012), no prevention efforts, to date, have attempted to change the parent-child relationship to reduce young childrens obesity risk. In this paper, we describe the rationale for and development of an innovative prevention program: Parent-Child Interaction Therapy-Health (PCIT-Health). First, we review the risk factors for the onset of obesity during childhood and assess current approaches to preventing child obesity, including limitations. Next, we articulate the theoretical links and empirical evidence that make PCIT a logical model to reduce the risk for childhood obesity. Finally, we describe the adaptation of the standard PCIT model into the PCIT-Health model and conclude with next steps for evaluating the adaptation.
keywords: Childhood obesity |Low-income |Parent-child interaction therapy |Parent-child relationship |Parenting |Self-regulation
A new technique ensuring privacy in big data: K-anonymity without prior value of the threshold k
یک تکنیک جدید مطمعن حریم خصوصی در داده های بزرگ: K-anonymity بدون مقدار قبلی آستانه k-2018
Big data has become omnipresent and crucial for many application domains. Big data makes reference to the explosive quantity of data generated in today’s society that might contain personally identifiable information (PII). That’s why the challenge from the point of view of data privacy is one of the major hurdles for the application of big data. In that situation, several techniques were exposed in order to ensure privacy in big data including generalization, randomization and cryptographic techniques as well. It is well known that there exist two main types of attributes in the literature, quasi identifier and sensitive attributes. In this paper, we are going to focus on quasi identifier attributes. Over the years, k-anonymity has been treated with great interest as an anonymization technique ensuring privacy in big data when we are dealing with quasi identifier attributes. Despite the fact that many algorithms of k-anonymity have been proposed, most of them admit that the threshold k of k-anonymity has to be known before anonymizing the data set. Here, a novel way in applying k-anonymity for quasi identifier attributes is presented. It’s a new algorithm called “k-anonymity without prior value of the threshold k”. Our proposed algorithm was experimentally evaluated using a test table of quasi identifier attributes. Furthermore, we highlight all the steps of our proposed algorithm with detailed comments.
Keywords: k-anonymity; quasi identifier attributes; big data; anonymization; privacy
“Hands on” versus “empty”: Supervision experiences of frontline child welfare workers
"دست های پر" دربرابر" خالی": تجربیات نظارتی کارگران کودک طلایه دار-2018
Quality supervision positively relates to frontline child welfare worker job satisfaction; worker empowerment and self-efficacy; the quality of client outcomes; and worker retention. Despite the importance of supervisory experiences, few studies describe workers perceptions of their relationships and experiences with their supervisors. The study applied the tenet of self-perpetuating, reinforcing relationships within the social exchange theory to understand newly-hired workers experiences of supervision. We used inductive, thematic analysis to examine interview data focused on workers transitions from training to casework including their supervision experiences. The qualitative subsample (N = 38) was drawn from the Florida Study of Professionals for Safe Families (FSPSF), a statewide sample of recently-hired frontline child welfare workers. Approximately one half of workers considered their current supervisory experiences as “hands on” and cooperative while the remaining half, conversely, described them as “empty” and detached. Findings reflect interactions in four domains: supervisor availability and approachability; consistency of provided information; level of micromanagement; and level of support. Workers, regardless of their experiences, expected supervisors to be available, knowledgeable, micromanagers, and supportive. Congruent with self-perpetuating, reinforcing relationships, almost universally, workers with cooperative experiences had their expectations met in each domain while those with detached experiences struggled in each area. Findings yield implications for training to guide relationships between supervisors and newly-hired workers: provide “hands on” supervisors and “check in” with newly-hired workers; provide micromanagement, including periodic accompaniment on home visits; provide an agency-approved checklist to guide workers through case processes; and support workers holistically.
keywords: Child welfare workers |Supervision |Training |Preparation |Workplace support
A framework for Big Data driven product traceability system
یک چارچوب برای سیستم ردیابی محصول بر اساس داده های بزرگ-2018
In recent years, the safety of consumer goods such as food products, drugs, etc. has become one of the major research challenge. This was due to the several food and drugs scandals and incidents that occurs during the 21st century (horse meat, mad cow disease, counterfeit drucs, etc.). To respond to this problem, multiple sectors of industry are becoming more client-oriented and needs faster response to deal with such issues. Product Traceability Systems (PTS) can ensure product authenticity over its whole lifecycle, thereby reducing the potential for bad publicity, minimising recall costs and stoping the distribution of unsafe products. However, current traceability systems are not adapted for all industries, they still deal with specific domain segment (food industry). This paper presents a comparative study between several works done on product traceability and proposes based on this study a standardized traceability system architecture based on Big Data technology.
Index Terms : Traceability system, Big Data, QR-Code, Information sharing
کنترل و تثبیت چندگانه عصبی
سال انتشار: 2018 - تعداد صفحات فایل pdf انگلیسی: 8 - تعداد صفحات فایل doc فارسی: 23
در این مقاله، ما یک استراتژی کنترل و تثبیت کننده چند عاملی عصبی برای سیستم های غیر خطی و ناپایدار ارائه می دهیم. این روش استراتژیک کنترل ، به ویژه زمانی که سیستم رفتارها و یا نقاط تعادل مختلفی دارد و زمانی که امیدواریم کل پروسه را به حالت مطلوب اطمینان ثبات منتقل کنیم، کارآمد است. استراتژی کنترل مورد نظر بر روی یک سیستم بی ثبات غیر خطی دارای دو نقطه تعادل بکار گرفته شده است. نشان داده شده است که استفاده از استراتژی کنترل و تثبیت کننده چند عاملی عصبی دامنه ثبات سیستم را نسبت به زمانی که از یک استراتژی کنترل عصبی تک استفاده می کنیم، بیشترافزایش می دهد.
کلمات کلیدی: رفتارها | متغیرهای حالت | کنترل کننده های عصبی چندگانه | ثبات
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