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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.
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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.
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مقاله انگلیسی |