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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 |
مقاله انگلیسی |
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A rejection inference technique based on contrastive pessimistic likelihood estimation for P2P lending
یک روش رد استنباط برمبنای تخمین احتمال بدبینی مخالف برای وام دهی P2P-2018 The majority of current credit-scoring models are built solely on accepted samples and thus cause sample bias. Sample bias is particularly severe in the peer-to-peer (P2P) lending domain due to its comparatively high rejection rate. Reject inference solves sample bias by inferring the possible outcomes of rejected samples and incorporating them into credit score modeling. This study addresses the problem of reject inference in a specific P2P lending domain from the perspective of semi-supervised learning. A novel reject inference method (CPLE-LightGBM) is proposed by combining the contrastive pessimistic likelihood estimation framework and an advanced gradient boosting decision tree classifier (LightGBM). The performance of the proposed CPLE-LightGBM method is validated on multiple datasets, and results demonstrate the efficiency of our proposal. Analysis of the influence of rejection rate on predictive accuracy reveals the usefulness of sampling in rejected datasets.
keywords: Big data applications |Contrastive pessimistic likelihood |Credit scoring |Data analytics |Gradient boosting decision tree estimation |Machine learning |P2P lending |Reject inference |Semi-supervised learning |
مقاله انگلیسی |
3 |
On Traffic-Aware Partition and Aggregation in MapReduce for Big Data Applications
تجمیع و تقسیم بندی ترافیک آگاه در MapReduce برای کاربردهای داده های بزرگ-2016 The MapReduce programming model simplifies large-scale data processing on commodity cluster by exploiting parallel
map tasks and reduce tasks. Although many efforts have been made to improve the performance of MapReduce jobs, they ignore the
network traffic generated in the shuffle phase, which plays a critical role in performance enhancement. Traditionally, a hash function is
used to partition intermediate data among reduce tasks, which, however, is not traffic-efficient because network topology and data size
associated with each key are not taken into consideration. In this paper, we study to reduce network traffic cost for a MapReduce job by
designing a novel intermediate data partition scheme. Furthermore, we jointly consider the aggregator placement problem, where each
aggregator can reduce merged traffic from multiple map tasks. A decomposition-based distributed algorithm is proposed to deal with
the large-scale optimization problem for big data application and an online algorithm is also designed to adjust data partition and
aggregation in a dynamic manner. Finally, extensive simulation results demonstrate that our proposals can significantly reduce network
traffic cost under both offline and online cases.
Index Terms: MapReduce | partition | aggregation | big data | lagrangian decomposition |
مقاله انگلیسی |
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Big Data applications in real-time traffic operation and safety monitoring and improvement on urban expressways
کاربردهای داده های بزرگ در عمل و نظارت بر ایمنی ترافیک در زمان واقعی و بهبود در بزرگراه های شهری-2015 The advent of Big Data era has transformed the outlook of numerous fields in science and
engineering. The transportation arena also has great expectations of taking the advantage
of Big Data enabled by the popularization of Intelligent Transportation Systems (ITS). In
this study, the viability of a proactive real-time traffic monitoring strategy evaluating
operation and safety simultaneously was explored. The objective is to improve the system
performance of urban expressways by reducing congestion and crash risk. In particular,
Microwave Vehicle Detection System (MVDS) deployed on an expressway network in
Orlando was utilized to achieve the objectives. The system consisting of 275 detectors
covers 75 miles of the expressway network, with average spacing less than 1 mile.
Comprehensive traffic flow parameters per lane are continuously archived on one-minute
interval basis. The scale of the network, dense deployment of detection system, richness of
information and continuous collection turn MVDS as the ideal source of Big Data. It was
found that congestion on urban expressways was highly localized and time-specific. As
expected, the morning and evening peak hours were the most congested time periods.
The results of congestion evaluation encouraged real-time safety analysis to unveil the
effects of traffic dynamics on crash occurrence. Data mining (random forest) and
Bayesian inference techniques were implemented in real-time crash prediction models.
The identified effects, both indirect (peak hour, higher volume and lower speed upstream
of crash locations) and direct (higher congestion index downstream to crash locations)
congestion indicators confirmed the significant impact of congestion on rear-end crash
likelihood. As a response, reliability analysis was introduced to determine the appropriate
time to trigger safety warnings according to the congestion intensity. Findings of this paper
demonstrate the importance to jointly monitor and improve traffic operation and safety.
The Big Data generated by the ITS systems is worth further exploration to bring all their
full potential for more proactive traffic management.
Keywords:
Big Data
Real-time
Congestion
Safety
Urban expressway |
مقاله انگلیسی |
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FlexAnalytics: A Flexible Data Analytics Framework for Big Data Applications with I/O Performance Improvement
FlexAnalytics: چارچوب تجزیه و تحلیل انعطاف پذیر برای کاربردهای داده های بزرگ با بهبود کارایی I/O-2014 Increasingly larger scale applications are generating an unprecedented amount of data. However, the
increasing gap between computation and I/O capacity on High End Computing machines makes a severe
bottleneck for data analysis. Instead of moving data from its source to the output storage, in-situ analytics
processes output data while simulations are running. However, in-situ data analysis incurs much more
computing resource contentions with simulations. Such contentions severely damage the performance of
simulation on HPE. Since different data processing strategies have different impact on performance and
cost, there is a consequent need for flexibility in the location of data analytics. In this paper, we explore
and analyze several potential data-analytics placement strategies along the I/O path. To find out the best
strategy to reduce data movement in given situation, we propose a flexible data analytics (FlexAnalytics)
framework in this paper. Based on this framework, a FlexAnalytics prototype system is developed for
analytics placement. FlexAnalytics system enhances the scalability and flexibility of current I/O stack on
HEC platforms and is useful for data pre-processing, runtime data analysis and visualization, as well as
for large-scale data transfer. Two use cases – scientific data compression and remote visualization – have
been applied in the study to verify the performance of FlexAnalytics. Experimental results demonstrate
that FlexAnalytics framework increases data transition bandwidth and improves the application end-toend transfer performance.
Keywords:
I/O bottlenecks
In-situ analytics
Data preparation
Big data
High-end computing |
مقاله انگلیسی |