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
Cost optimization for deadline-aware scheduling of big-data processing jobs on clouds
بهینه سازی هزینه برای زمانبندی دقیق پردازش داده های بزرگ کارها در ابرها-2018
Cloud computing has been widely regarded as a capable solution for big data processing. Nowadays cloud service providers usually offer users virtual machines with various combinations of configurations and prices. As this new service scheme emerges, the problem of choosing the cost-minimized combination under a deadline constraint is becoming more complex for users. The complexity of determining the cost minimized combination may be resulted from different causes: the characteristics of user applications, and providers’ setting on the configurations and pricing of virtual machine. In this paper, we proposed a variety of algorithms to help the users to schedule their big data processing workflow applications on clouds so that the cost can be minimized and the deadline constraints can be satisfied. The proposed algorithms were evaluated by extensive simulation experiments with diverse experimental settings.
Keywords: Big-data ، Scheduling ، Cost-efficient ، Cloud computing
Fault-diagnosis for reciprocating compressors using big data and machine learning
تشخیص گسل برای کمپرسورهای مجاور با استفاده از داده های بزرگ و یادگیری ماشین-2018
Reciprocating compressors are widely used in petroleum industry. A small fault in recipro cating compressor may cause serious issues in operation. Traditional regular maintenance and fault diagnosis solutions cannot efficiently detect potential faults in reciprocating com pressors. This paper proposes a fault-diagnosis system for reciprocating compressors. It applies machine-learning techniques to data analysis and fault diagnosis. The raw data is denoised first. Then the denoised data is sparse coded to train a dictionary. Based on the learned dictionary, potential faults are finally recognized and classified by support vector machine (SVM). The system is evaluated by using 5-year operation data collected from an offshore oil corporation in a cloud environment. The collected data is evenly divided into two halves. One half is used for training, and the other half is used for testing. The results demonstrate that the proposed system can efficiently diagnose potential faults in com pressors with more than 80% accuracy, which represents a better result than the current practice.
Keywords: Reciprocating compressor، Big data ، Cloud computing ، Deep learning ، RPCA ، SVM
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
SLA based healthcare big data analysis and computing in cloud network
تحلیل داده های بزرگ سلامت مبتنی بر sla و محاسبات در شبکه های ابری-2018
Large volume of multi-structured and low-latency patient data are generated in healthcare services, which is challenging task to process and analyze within the Service Level Agreement (SLA). In this paper, a Parallel Semi-Naive Bayes (PSNB) based probabilistic method is used to process the healthcare big data in cloud for future health condition prediction. In order to improve the accuracy of PSNB method, a Modified Conjunctive Attribute (MCA) algorithm is proposed for reducing the dimension. Emergency condition of the patient is considered by setting a global priority among the patients and an Optimal Data Distribution (ODD) algorithm is proposed to position both batch and streaming patient data into the Spark nodes. Further, a Dynamic Job Scheduling (DJS) algorithm is designed to schedule the jobs efficiently to the most suitable nodes for processing the data taking SLA into account. Our proposed PSNB algorithm provides better accuracy of 87.8% for both batch and streaming data, which is 12.8% higher than the original Naive Bayes (NB) algorithm and can conveniently be employed in various patient monitoring applications.
Keywords: Big Data ، cloud computing ،healthcare, spark
Explaining resistance to system usage in the PharmaCloud: A view of the dual-factor model
توضیح دادن مقاومت دربرابر استفاده از سیستم در ابر دارویی: یک مرور روی مدل دو عاملی-2018
Use of the PharmaCloud can improve the quality of healthcare, but improvements are likely to be thwarted if physicians resist using the system. This study uses the dual-factor model to explain physicians’ resistance behaviors to system usage. The results of a field survey conducted in Taiwan showed that physicians’ resistance to using the PharmaCloud stemmed from regret avoidance, inertia, perceived value, and perceived threat. These results also indicate that system, information, and service qualities are the key determinants of the behavioral intention to use. This research advances the theoretical understanding of user acceptance and resistance to technology post-implementation and offers practical implications.
keywords: Pharma Cloud| Drug safety| User resistance| System use| Status quo bias
یک پروژه گردآوری شده و کد منبع باز برای تولید شبیه سازهای مدلسازی مبتنی بر وب جنگل
سال انتشار: 2018 - تعداد صفحات فایل pdf انگلیسی: 5 - تعداد صفحات فایل doc فارسی: 18
مدیریت پایدار جنگل نیازمند سیستمهای پشتیبانی از تصمیم جهت بررسی طرح های امکانپذیر و پیش بینی پیامدهای این تصمیمات می باشد. مدلسازهای جنگل نوعا" سیستمهای پیچیده ای از معادلات را برای پیش بینی رفتار جنگل ها تولید می کنند که استفاده از مدلهای جنگل را برای کاربران نهایی به صورت کلی دشوار می کنند و بر انتقال دانش و فناوری اثر می گذارند. برای غلبه بر این دشواری ها و تسهیل استفاده عملی از آنها، مدلها می توانند به صورت نرم افزار جهت تولید شبیه سازهای جنگلی سازگار با کاربران یکپارچه سازی شوند. در این مقاله ما ForestMTIS را معرفی و توصیف می کنیم، یک پروژه محاسبه ابری تالیف شده و منبع – باز قابل ویرایش برای تولید شبیه سازهای جنگل که برای مدلهای آماری، غیرفضایی، جبری، غیرمتراکم، تک گونه و برجسته برای رشد و بهره تولید شده است. ما استفاده از ForestMTIS را برمبنای توسعه FlorNExT®، اولین برنامه کاربردی آن، برمبنای یک دیدگاه مشارکتی برای ساخت مدل رشد و بهره و مدیریت پایدار جنگل و قابل دسترسی برای تعداد زیادی از کاربران در شمال پرتغال نشان می دهیم.
کلیدواژه ها: شبیه ساز جنگل | ASP.Net | محاسبات ابری | نرم افزار به عنوان یک خدمات | انتقال دانش
|مقاله ترجمه شده|
Enhancing water system models by integrating big data
افزایش مدل های سیستم آب با ادغام داده های بزرگ-2018
The past quarter century has witnessed development of advanced modeling approaches, such as stochastic and agent-based modeling, to sustainably manage water systems in the presence of deep uncertainty and complexity. However, all too often data inputs for these powerful models are sparse and outdated, yielding unreliable results. Advancements in sensor and communication technologies have allowed for the ubiquitous deployment of sen sors in water resources systems and beyond, providing high-frequency data. Processing the large amount of heterogeneous data collected is non-trivial and exceeds the capacity of traditional data warehousing and pro cessing approaches. In the past decade, significant advances have been made in the storage, distribution, querying, and analysis of big data. Many tools have been developed by computer and data scientists to facilitate the manipulation of large datasets and create pipelines to transmit the data from data warehouses to compu tational analytic tools. A generic framework is presented to complete the data cycle for a water system. The data cycle presents an approach for integrating high-frequency data into existing water-related models and analyses, while highlighting some of the more helpful data management tools. The data tools are helpful to make sus tainable decisions, which satisfy the objectives of a society. Data analytics distribution tool Spark is introduced through the illustrative application of coupling high-frequency demand metering data with a water distribution model. By updating the model in near real-time, the analysis is more accurate and can expose serious mis interpretations.
Keywords: Water systems , Modeling , Big data , Automation , Hadoop , Apache Spark , Cloud computing
Managing big RDF data in clouds: Challenges, opportunities, and solutions
مدیریت داده های RDF بزرگ در ابرها: چالش ها، فرصت ها و راه حل ها-2018
The expansion of the services of the Semantic Web and the evolution of cloud computing technologies have significantly enhanced the capability of preserving and publishing information in standard open web formats, such that data can be both human-readable and machine-processable. This situation meets the challenge in the current big data era to effectively store, retrieve, and analyze resource description framework (RDF) data in swarms. This paper presents an overview of the existing challenges, evolving opportunities, and current de velopments towards managing big RDF data in clouds and provides guidance and substantial lessons learned from research in big data management. In particular, it highlights the basic principles of RDF data management, which allow researchers to know the most recent stage in developing RDF graphs and its achievement. Additionally, the research provides comparative studies among current storage systems and query processing approaches in understanding their efficiency. The paper also provides a vision for long-term future research directions by providing highlights on future challenges and opportunities in RDF domain.
Keywords: Semantic Web , Cloud computing , RDF graphs , Linked data , Big data
The IoT for smart sustainable cities of the future: An analytical framework for sensor-based big data applications for environmental sustainability
اینترنت اشیا برای شهرهای پایدار هوشمند از آینده: یک چارچوب تحلیلی برای کاربردهای داده های بزرگ مبتنی بر حسگر برای سازگاری با محیط زیست-2018
The Internet of Things (IoT) is one of the key components of the ICT infrastructure of smart sustainable cities as an emerging urban development approach due to its great potential to advance environmental sustainability. As one of the prevalent ICT visions or computing paradigms, the IoT is associated with big data analytics, which is clearly on a penetrative path across many urban domains for optimizing energy efficiency and mitigating en vironmental effects. This pertains mainly to the effective utilization of natural resources, the intelligent man agement of infrastructures and facilities, and the enhanced delivery of services in support of the environment. As such, the IoT and related big data applications can play a key role in catalyzing and improving the process of environmentally sustainable development. However, topical studies tend to deal largely with the IoT and related big data applications in connection with economic growth and the quality of life in the realm of smart cities, and largely ignore their role in improving environmental sustainability in the context of smart sustainable cities of the future. In addition, several advanced technologies are being used in smart cities without making any con tribution to environmental sustainability, and the strategies through which sustainable cities can be achieved fall short in considering advanced technologies. Therefore, the aim of this paper is to review and synthesize the relevant literature with the objective of identifying and discussing the state-of-the-art sensor-based big data applications enabled by the IoT for environmental sustainability and related data processing platforms and computing models in the context of smart sustainable cities of the future. Also, this paper identifies the key challenges pertaining to the IoT and big data analytics, as well as discusses some of the associated open issues. Furthermore, it explores the opportunity of augmenting the informational landscape of smart sustainable cities with big data applications to achieve the required level of environmental sustainability. In doing so, it proposes a framework which brings together a large number of previous studies on smart cities and sustainable cities, including research directed at a more conceptual, analytical, and overarching level, as well as research on specific technologies and their novel applications. The goal of this study suits a mix of two research approaches: topical literature review and thematic analysis. In terms of originality, no study has been conducted on the IoT and related big data applications in the context of smart sustainable cities, and this paper provides a basis for urban researchers to draw on this analytical framework in future research. The proposed framework, which can be replicated, tested, and evaluated in empirical research, will add additional depth to studies in the field of smart sustainable cities. This paper serves to inform urban planners, scholars, ICT experts, and other city sta keholders about the environmental benefits that can be gained from implementing smart sustainable city in itiatives and projects on the basis of the IoT and related big data applications.
Keywords: Smart sustainable cities , The IoT , Big data analytics , Sensor technology , Data processing platforms , Environmental sustainability , Big data applications , Cloud computing , Fog/edge computing