Big data driven Hierarchical Digital Twin Predictive Remanufacturing paradigm: Architecture, control mechanism, application scenario and benefits
داده های بزرگ مبتنی بر الگوی پیش بینی تولید دوقلوهای پیش بینی شده دوقلو سلسله مراتبی: معماری ، مکانیسم کنترل ، سناریوی برنامه و مزایا-2020
Remanufacturing is deemed to be an effective method for recycling resources, achieving sustainable production. However, little importance of remanufacturing has been attached in PLM. Surely, there are many problems in implementation of the remanufacturing strategy, such as inability to effectively reduce uncertainty, lack of product multi-life-cycle remanufacturing process tracking management, lack of smart enabling technology application in the full lifecycle that focusing on multi-life-cycle remanufacturing. After analyzing the reasons, through integrating smart enabling technologies, a new PLM paradigm focusing on the multi-life-cycle remanufacturing process: Big Data driven Hierarchical Digital Twin Predictive Remanufacturing (BDHDTPREMfg) is proposed. And the definition of BDHDTPREMfg is proposed. A big data driven layered architecture and the hierarchical CPS-Digital-Twin(CPSDT) reconfiguration control mechanism of BDHDTPREMfg are respectively developed. Then, this paper presents an application scenario of BDHDTPREMfg to validate the feasibility and effectiveness. Based on the above application analysis, the benefits of penetrating BDHDTPREMfg into the entire lifecycle are demonstrated. The summary of this paper and future research work is discussed in the end.
Keywords: multi-life-cycle remanufacturing | Sustainable products | Big data | CPS-Digital-twin(CPSDT) | IoT-cloud | Reconfiguration
Big data in agriculture: Does the new oil lead to sustainability?
داده های بزرگ در کشاورزی: آیا سوخت جدید منجر به پایداری می شود؟-2020
Big data represent a new productive factor (the “new oil” for advocates) that generates new realities in agriculture. By adding an extra “cyber” dimension to current farming systems, big data lead to the emergence of new, complex cyber-physical-social systems. However, our understanding of the sustainability of such systems is still at a rudimental stage. In this critical review we attempt to shed some light on this topic, by identifying and presenting some issues that put in doubt the sustainability of big data agriculture. By using a punctuated equilibria lens, we argue that despite their contribution to the economic and environmental performance of farming, big data act as a speciation mechanism. Hence, they lead to new forms of intraspecific, interspecific and intergeneric competition, thus putting at risk the most vulnerable players of the game. We conclude by pointing out that to holistically address the interrelation between big data and agricultural sustainability we need a hybrid research line, which will combine the qualities of both technology-oriented research and critical social science.
Keywords: Big data | Smart farming | Digital farming | Cyber-physical-social systems | Sustainability | Agriculture
An empirical case study on Indian consumers sentiment towards electric vehicles: A big data analytics approach
یک مطالعه موردی تجربی در مورد احساسات مصرف کنندگان هندی نسبت به وسایل نقلیه برقی: یک رویکرد تحلیل داده های بزرگ-2020
Today, climate change due to global warming is a significant concern to all of us. Indias rate of greenhouse gas emissions is increasing day by day, placing India in the top ten emitters in the world. Air pollution is one of the significant contributors to the greenhouse effect. Transportation contributes about 10% of the air pollution in India. The Indian government is taking steps to reduce air pollution by encouraging the use of electric vehicles. But, success depends on consumers sentiment, perception and understanding towards Electric Vehicles (EV). This case study tried to capture the feeling, attitude, and emotions of Indian consumers towards electric vehicles. The main objective of this study was to extract opinions valuable to prospective buyers (to know what is best for them), marketers (for determining what features should be advertised) and manufacturers (for deciding what features should be improved) using Deep Learning techniques (e.g Doc2Vec Algorithm, Recurrent Neural Network (RNN), Convolutional Neural Network (CNN)). Due to the very nature of social media data, big data platform was chosen to analyze the sentiment towards EV. Deep Learning based techniques were preferred over traditional machine learning algorithms (Support Vector Machine, Logistic regression and Decision tree, etc.) due to its superior text mining capabilities. Two years data (2016 to 2018) were collected from different social media platform for this case study. The results showed the efficiency of deep learning algorithms and found CNN yield better results in-compare to others. The proposed optimal model will help consumers, designers and manufacturers in their decision-making capabilities to choose, design and manufacture EV.
Keywords: Electric vehicles | Deep learning | Big data | Sentiment analysis | India
The integrated effect of Big Data Analytics, Lean Six Sigma and Green Manufacturing on the environmental performance of manufacturing companies: The case of North Africa
اثر یکپارچه تحلیل داده های بزرگ، تولید شش سیگما و سبز بر عملکرد زیست محیطی شرکت های تولیدی: مورد شمال آفریقا-2020
With the advent of Big Data Analytics (BDA) alongside the maturity of specific improvement approaches such as Lean Six Sigma (LSS) and Green Manufacturing (GM), the integration of these initiatives to achieve higher environmental performance (EP) is gathering the interest of both researchers and practitioners. The present study builds on the resources based view of capabilities to propose and empirically test a framework exploring whether LSS and GM mediate the relationship between BDA capabilities and EP. A two-stage hybrid Factorial Analysis - Structural Equation Modeling is used to draw insights from 201 industry practitioners from North African companies. The findings confirm the direct influence of BDA on EP and also identify LSS and GM as significant mediating variables that act as a catalyst to boost indirect impacts of BDA on EP. This study can help researchers and practitioners to fully understand and benefit from BDA capabilities and improvement initiatives such as LSS and GM while managing environmental issues. The study discusses theoretical and managerial implications for enhancing the environmental performance of the manufacturing organizations.
Keywords: Environmental performance | Big data analytics | Lean Six Sigma | Green Manufacturing | Structural equation modeling
Behavior of crossover operators in NSGA-III for large-scale optimization problems
رفتار اپراتورهای متقاطع در NSGA-III برای مسائل بهینه سازی در مقیاس بزرگ-2020
Traditional multi-objective optimization evolutionary algorithms (MOEAs) do not usu- ally meet the requirements for online data processing because of their high compu- tational costs. This drawback has resulted in difficulties in the deployment of MOEAs for multi-objective, large-scale optimization problems. Among different evolutionary algo- rithms, non-dominated sorting genetic algorithm-the third version (NSGA-III) is a fairly new method capable of solving large-scale optimization problems with acceptable com- putational requirements. In this paper, the performance of three crossover operators of the NSGA-III algorithm is benchmarked using a large-scale optimization problem based on human electroencephalogram (EEG) signal processing. The studied operators are simu- lated binary (SBX), uniform crossover (UC), and single point (SI) crossovers. Furthermore, enhanced versions of the NSGA-III algorithm are proposed through introducing the con- cept of Stud and designing several improved crossover operators of SBX, UC, and SI. The performance of the proposed NSGA-III variants is verified on six large-scale optimization problems. Experimental results indicate that the NSGA-III methods with UC and UC-Stud (UCS) outperform the other developed variants.
Keywords: Electroencephalography | Large-scale optimization | Big data optimization | Evolutionary multi-objective optimization | NSGA-III | Crossover operator | Performance analysis
Clicking position and user posting behavior in online review systems: A data-driven agent-based modeling approach
کلیک بر روی موقعیت و رفتار ارسال کاربر در سیستم های مرور آنلاین: یک روش مدل سازی مبتنی بر عامل مبتنی بر داده ها-2020
In online review systems, a participant’s level of knowledge impacts his/her posting behav- iors, and an increase in knowledge occurs when the participant reads the reviews posted on the systems. To capture the collective dynamics of posting reviews, we used real-world big data collected over 153 months to drive an agent-based model for replicating the oper- ation process of online review systems. The model explains the effects of clicking position (e.g., on a review webpage’s serial list) and the number of items per webpage on posting contributions. Reading reviews from the last webpage only, or from the first webpage and last webpage simultaneously, can promote a greater review volume than reading reviews in other positions. This illustrates that representing primacy (first items) and recency (re- cent items) within one page simultaneously, or displaying recent items in reverse chrono- logical order, are relatively better strategies for the webpage display of online reviews. The number of items plays a nonlinear moderating role in bridging the clicking position and posting behavior, and we determine the optimal number of items. To effectively establish strategies for webpage design in online review systems, business managers must switch from reliance on experience to reliance on an agent-based model as a decision support system for the formalized webpage design of online review systems.
Keywords: Agent-based modeling | Big data | Online review systems | Clicking position | Posting behavior
The role of data within coastal resilience assessments: an East Anglia, UK, case study
نقش داده ها در ارزیابی های تاب آوری ساحلی: آنگلیای شرقی ، انگلیس ، مطالعه موردی-2020
Embracing the concept of resilience within coastal management marks a step change in thinking, building on the inputs of more traditional risk assessments, and further accounting for capacities to respond, recover and implement contingency measures. Nevertheless, many past resilience assessments have been theoretical and have failed to address the requirements of practitioners. Assessment methods can also be subjective, relying on opinion-based judgements, and can lack empirical validation. Scope exists to address these challenges through drawing on rapidly emerging sources of data and smart analytics. This, alongside the careful selection of the metrics used in assessment of resilience, can facilitate more robust assessment methods. This work sets out to establish a set of core metrics, and data sources suitable for inclusion within a data-driven coastal resilience assessment. A case study region of East Anglia, UK, is focused on, and data types and sources associated with a set of proven assessment metrics were identified. Virtually all risk-specific metrics could be satisfied using available or derived data sources. However, a high percentage of the resilience-specific metrics would still require human input. This indicates that assessment of resilience is inherently more subjective than assessment of risk. Yet resilience assessments incorporate both risk and resilience specific variables. As such it was possible to link 75% of our selected metrics to empirical sources. Through taking a case study approach and discussing a set of requirements outlined by a coastal authority, this paper reveals scope for the incorporation of rapidly progressing data collection, dissemination, and analytical methods, within dynamic coastal resilience assessments. This could facilitate more sustainable evidence-based management of coastal regions
Keywords: Coastal management | Resilience metrics | Geospatial data | Open source data | Big data
A fully scalable big data framework for Botnet detection based on network traffic analysis
چارچوب داده های بزرگ کاملاً مقیاس پذیر برای تشخیص Botnet مبتنی بر آنالیز ترافیک شبکه-2020
Many traditional Botnet detection methods have trouble scaling up to meet the needs of multi-Gbps networks. This scalability challenge is not just limited to bottlenecks in the detection process, but across all individual components of the Botnet detection system in- cluding data gathering, storage, feature extraction, and analysis. In this paper, we propose a fully scalable big data framework that enables scaling for each individual component of Botnet detection. Our framework can be used with any Botnet detection method - includ- ing statistical methods, machine learning methods, and graph-based methods. Our experi- mental results show that the proposed framework successfully scales in live tests on a real network with 5Gbps of traffic throughput and 50 millions IP addresses visits. In addition, our run time scales logarithmically with respect to the volume of the input for example, when the scale of the input data multiplies by 4 ×, the total run time increases by only 31%. This is significant improvement compared to schemes such as Botcluster in which run time increases by 86% under similar scale condition.
Keywords: Botnet detection | Big data | Hadoop | Spark | Machine learning | Scalability
Privacy-preserving clustering for big data in cyber-physical-social systems: Survey and perspectives
خوشه بندی حفظ حریم خصوصی برای داده های بزرگ در سیستم های سایبر-فیزیکی-اجتماعی: بررسی و چشم انداز-2020
Clustering technique plays a critical role in data mining, and has received great success to solve application problems like community analysis, image retrieval, personalized rec- ommendation, activity prediction, etc. This paper first reviews the traditional clustering and the emerging multiple clustering methods, respectively. Although the existing meth- ods have superior performance on some small or certain datasets, they fall short when clustering is performed on CPSS big data because of the high cost of computation and stor- age. With the powerful cloud computing, this challenge can be effectively addressed, but it brings enormous threat to individual or company’s privacy. Currently, privacy preserving data mining has attracted widespread attention in academia. Compared to other reviews, this paper focuses on privacy preserving clustering technique, guiding a detailed overview and discussion. Specifically, we introduce a novel privacy-preserving tensor-based multi- ple clustering, propose a privacy-preserving tensor-based multiple clustering analytic and service framework, and give an illustrated case study on the public transportation dataset. Furthermore, we indicate the remaining challenges of privacy preserving clustering and discuss the future significant research in this area.
Keywords: CPSS | Big data | Cloud computing | Privacy preserving | Clustering
Keep it simple stupid! A non-parametric kernel regression approach to forecast travel speeds
آن را احمقانه نگه دارید! یک روش رگرسیون هسته غیر پارامتری برای پیش بینی سرعت سفر-2020
The approach taken by the second place winner of the TRANSFOR prediction challenge is presented. The challenge involves forecasting travel speeds on two arterial links in Xi’an City in China for two five hour periods on a single day. Travel speeds are measured from trajectory information on probe vehicles from a fleet of vehicles for a large sub-area of the city. After experimenting with several deep learning methods, we settle on a simple non-parametric kernel regression approach. The method, borrowed from previous work in fixed route transit predictions, formalizes the intuition that in urban systems most failure patterns are recurrent. Our choice is supported by test results where the method outperformed all evaluated neural architectures. The results suggest simple methods are very competitive, particularly considering the high lifecycle cost of deep learning models.
Keywords: Traffic forecasting | Machine learning | Big data