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نتیجه جستجو - Big Data Algorithm

تعداد مقالات یافته شده: 6
ردیف عنوان نوع
1 Multi-Granular Electricity Consumer Load Profiling for Smart Homes using a Scalable Big Data Algorithm
تولید گرانول الکتریکی مصرف کننده بار برای ساخت خانه های هوشمند با استفاده از یک الگوریتم داده های بزرگ مقیاس پذیر-2018
With rising electricity prices, there is a need to give consumers greater control over their energy consumption. It is anticipated that such informed consumers in control of their consumption patterns will contribute to reduced energy usage and thus a sustainable environment. Smart meter technology in smart homes provides real-time information to customers through devices such as in-home displays and web portals, and provide half-hourly consumption data to electricity distributors and retailers. Such data enables the profiling of consumers making it possible to understand different life styles and electricity usage behaviours to provide customised electricity billing. To obtain the anticipated benefit from such highly granular and high frequency data, it is essential to have big data technologies which can process such volumes of data in near real time. The research described in this paper focus on addressing the key requirements of large volume data processing and making use of the highly granular nature of the data. Adapting a new scalable algorithm introduced by the authors for big data processing, this work demonstrates the practicality of processing large volumes of data at multiple levels of granularity. The faster processing capacity makes it possible to continuously analyse consumption data at frequent intervals as they are collected and at a highly granular level thus providing a practical solution as a smart home application. The advantages of the technique is demonstrated using electricity consumption data for 10,000 households for a year from an Australian electricity retailer.
مقاله انگلیسی
2 MRQAR: a generic MapReduce framework to discover Quantitative Association Rules in Big Data problems
MRQAR: یک چارچوب کلی MapReduce برای کشف قوانین کمی در مشکلات داده های بزرگ-2018
Many algorithms have emerged to address the discovery of quantitative association rules from datasets in the last years. However, this task is becoming a challenge because the processing power of most existing techniques is not enough to handle the large amount of data generated nowadays. These vast amounts of data are known as Big Data. A number of previous studies have been focused on mining boolean or nominal association rules from Big Data problems, nevertheless, the data in real-world applications usually consist of quantitative values and designing data mining algorithms able to extract quantitative association rules presents a challenge to workers in this research field. In spite of the fact that we can find classical methods to discover boolean or nominal association rules in the most well-known repositories of Big Data algorithms, such repositories do not provide methods to discover quantitative association rules. Indeed, no methodologies have been proposed in the literature without prior discretization in Big Data. Hence, this work proposes MRQAR, a new generic parallel framework to discover quantitative association rules in large amounts of data, designed following the MapReduce paradigm using Apache Spark. MRQAR performs an incremental learning able to run any sequential quantitative association rule algorithm in Big Data problems without needing to redesign such algorithms. As a case study, we have integrated the multiobjective evolutionary algorithm MOPNAR into MRQAR to validate the generic MapReduce framework proposed in this work. The results obtained in the experimental study performed on five Big Data problems prove the capability of MRQAR to obtain reduced set of high quality rules in reasonable time.
Keywords: Quantitative association rules , multiobjective evolutionary algorithms , Big Data, MapReduce, Spark
مقاله انگلیسی
3 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
4 Clustering Enabled Wireless Channel Modeling Using Big Data Algorithms
مدل سازی خوشه بندی کانال بی سیم فعال با استفاده از الگوریتم های داده های بزرگ-2018
Recently, rapid growth in data services has ushered in the so-called big data era, and data mining and analysis techniques have been widely adopted to extract value from data for different applications. Channel modeling also benefits in this era, in particular by exploiting algorithmic techniques developed for big data applications. In this article, the challenges and opportunities in clustering-enabled wireless channel modeling are discussed in this context. First, some well known clustering techniques, which are potentially capable of enabling clustered channel modeling, are presented. Next, the motivation of cluster-based channel modeling is presented. The typical concepts of clusters used in channel models are summarized, and the state-of-the-art clustering and tracking algorithms are reviewed and compared. Finally, several promising research problems for channel clustering are highlighted.
Keywords: Clustering algorithms, Channel models, Partitioning algorithms, Wireless communication,Data models,Delays, Algorithm design and analysis
مقاله انگلیسی
5 Can Sensors Collect Big Data? An Energy Efficient Big Data Gathering Algorithm for WSN
آیا حسگرها می توانند اطلاعات جمع آوری کنند؟ الگوریتم جمع آوری داده های انرژی برای شبکه های حسگر بی سیم-2017
Recently, incredible growth in communication technology has given rise to the hot topic, Big Data. Distributed wireless sensor networks (WSNs) are the key provider of Big Data and can generate a significant amount of data. Various technical challenges exist in gathering the real time data. Energy efficient routing algorithms can overcome these challenges. The signal transmission features have been obtained by analyzing the experiments. According to these experiments, an energy efficient Big Data algorithm (BDEG) for WSN is proposed for real time data collection. Clustering communication is established on the basis of RSSI and residual energy of sensor nodes. Experimental simulations show that BDEG is stable in terms of network lifetime and data transmission time because of load balancing scheme. The effectiveness of the proposed scheme is verified through numerical results obtained in MATLAB.
Keywords: Big Data | BDEG algorithm | RSSI | WSNs | Cluster ing | Network Lifetime.
مقاله انگلیسی
6 Next-generation sequencing: big data meets high performance computing
توالی نسل بعدی: داده های بزرگ همراه با محاسبات با کارایی بالا-2017
The progress of next-generation sequencing has a major impact on medical and genomic research. This high-throughput technology can now produce billions of short DNA or RNA fragments in excess of a few terabytes of data in a single run. This leads to massive datasets used by a wide range of applications including personalized cancer treatment and precision medicine. In addition to the hugely increased throughput, the cost of using high-throughput technologies has been dramatically decreasing. A low sequencing cost of around US$1000 per genome has now rendered large population-scale projects feasible. However, to make effective use of the produced data, the design of big data algorithms and their efficient implementation on modern high performance computing systems is required.
مقاله انگلیسی
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