Cognitive computing, Big Data Analytics and data driven industrial marketing
محاسبات شناختی ، تحلیل داده های بزرگ و بازاریابی صنعتی مبتنی بر داده ها-2020
The integration of cognitive computing and big data analytics leads to a new paradigm that enables the application of the most sophisticated advances in information and communication technology (ICT) in business, including industry, business to business, and related decision-making process. The same paradigm will lead to several breakthroughs in the subfield of industrial marketing: a field both promising and extremely challenging. This special issue makes a case that cognitive computing and big data are a source of a new competitive advantage that, if properly embraced, will further consolidate industrial marketing management position in the of core the decision-making process of businesses operating locally and globally. In this vein, the value added of this special issue is twofold. On the one hand, this special issue communicates high quality research on big data analytics and data science as it is applied in industrial marketing management; On the other hand, it proposes a multidisciplinary approach to the study of the design, implementation and provision of sophisticated applications and systems necessary for data-driven industrial marketing decisions.
Achieving sustainable performance in a data-driven agriculture supply chain: A review for research and applications
دستیابی به عملکرد پایدار در زنجیره تأمین کشاورزی داده محور: مروری بر تحقیقات و کاربردها-2020
The lack of industrialization, inadequacy of the management, information inaccuracy, and inefficient supply chains are the significant issues in an agri-food supply chain. The proposed solutions to overcome these challenges should not only consider the way the food is produced but also take care of societal, environmental and economic concerns. There has been increasing use of emerging technologies in the agriculture supply chains. The internet of things, the blockchain, and big data technologies are potential enablers of sustainable agriculture supply chains. These technologies are driving the agricultural supply chain towards a digital supply chain environment that is data-driven. Realizing the significance of a data-driven sustainable agriculture supply chain we extracted and reviewed 84 academic journals from 2000 to 2017. The primary purpose of the review was to understand the level of analytics used (descriptive, predictive and prescriptive), sustainable agriculture supply chain objectives attained (social, environmental and economic), the supply chain processes from where the data is collected, and the supply chain resources deployed for the same. Based on the results of the review, we propose an application framework for the practitioners involved in the agri-food supply chain that identifies the supply chain visibility and supply chain resources as the main driving force for developing data analytics capability and achieving the sustainable performance. The framework will guide the practitioners to plan their investments to build a robust data-driven agri-food supply chain. Finally, we outline the future research directions and limitations of our study.
Keywords: Agriculture supply chain | Food supply chain | Sustainability | Sustainable performance | Supply chain visibility | Big data | Blockchain | Data analytics | Supply chain resources
Application of smart safety training and education in network teaching management
کاربرد آموزش ایمنی هوشمند و آموزش در مدیریت آموزش شبکه-2020
Aiming at the problems of poor resource scheduling and low degree of information fusion in the traditional network management method of intelligent security training and education optimization, an intelligent security training and education optimization network management model based on big data mining is proposed. Building intelligent safety training and education of big data fusion analysis model, using the method of association rules mining, complete the intelligent safety training and education statistics analysis, under the Internet environment using quantitative sensing fusion tracking method, the network teaching management information fusion processing, build large data information scheduling model based on network teaching management, fuzzy information fusion method to reconstruct 3 d information of the network teaching management, to establish the network teaching management big data spectral analysis model, the introduction of phase space reconstruction method, the network resource scheduling optimization of teaching management. The experimental results show that the proposed method has better resource scheduling performance, higher degree of information fusion, and can improve the ability of intelligent security training and education management.
Keywords: Smart safety training | Education | Network teaching management | Big data | Integration | Resource scheduling
Factor-based big data and predictive analytics capability assessment tool for the construction industry
داده های بزرگ مبتنی بر فاکتور و ابزار ارزیابی قابلیت تحلیلی پیش بینی کننده برای صنعت ساخت و ساز-2020
Big data and predictive analytics have huge potential to create value to the construction industry. However, there is a lack of benchmarking system to evaluate organizations competency to adopt big data and predictive analytics. Hence, this study aims to develop a big data and predictive analytics capability assessment tool that can measure construction organizations capability in big data and predictive analytics implementation and that also highlights strengths and weaknesses of the organization to provide a benchmark in the process of big data and predictive analytics implementation. 21 determinants were identified and assessed in sense of their impacts on an organizations capability to implement big data and predictive analytics. These determinants were categorized into five determinant groups and assigned weights, to form the basis for the big data and predictive analytics capability assessment tool. The developed tool was then validated with four construction organizations to reflect their big data and predictive analytic capability levels, strengths and weaknesses. The findings of this study contribute to knowledge and practice by identifying the determinants impacting construction organizations capability to adopt big data and predictive analytics and in the development of a computerized assessment tool which also serves as a benchmarking tool for construction organizations in the implementation of big data and predictive analytics.
Keywords: Big data | Predictive analytics | Capability assessment tool | Construction industry | Organization capability
Probabilistic data structures for big data analytics: A comprehensive review
ساختار داده های احتمالی برای تجزیه و تحلیل داده های بزرگ: یک مرور جامع-2020
An exponential increase in the data generation resources is widely observed in last decade, because of evolution in technologies such as-cloud computing, IoT, social networking, etc. This enormous and unlimited growth of data has led to a paradigm shift in storage and retrieval patterns from traditional data structures to Probabilistic Data Structures (PDS). PDS are a group of data structures that are extremely useful for Big data and streaming applications in order to avoid high-latency analytical processes. These data structures use hash functions to compactly represent a set of items in stream-based computing while providing approximations with error bounds so that well-formed approximations get built into data collections directly. Compared to traditional data structures, PDS use much less memory and constant time in processing complex queries. This paper provides a detailed discussion of various issues which are normally encountered in massive data sets such as-storage, retrieval, query,etc. Further, role of PDS in solving these issues is also discussed where these data structures are used as temporary accumulators in query processing. Several variants of existing PDS along with their application areas have also been explored which give a holistic view of domains where these data structures can be applied for efficient storage and retrieval of massive data sets. Mathematical proofs of various parameters considered in the PDS have also been discussed in the paper. Moreover, the relative comparison of various PDS with respect to various parameters is also explored.
Keywords: Big data | Internet of things (IoT) | Probabilistic data structures | Bloom filter | Quotient filter | Count min sketch | HyperLogLog counter | Min-hash Locality | sensitive hashing
Use of a big data analysis technique for extracting HRA data from event investigation reports based on the Safety-II concept
استفاده از روش تجزیه و تحلیل داده های بزرگ برای استخراج داده های مجموعه فعالان حقوق بشر از رویداد گزارش تحقیقات بر اساس مفهوم ایمنی-II-2020
The safe operation of complex socio-technical systems including NPPs (Nuclear Power Plants) is a determinant for ensuring their sustainability. From this concern, it should be emphasized that a large portion of safety significant events were directly and/or indirectly caused by human errors. This means that the role of an HRA (Human Reliability Analysis) is critical because one of its applications is to systematically distinguish error-prone tasks triggering safety significant events. To this end, it is very important for HRA practitioners to access diverse HRA data which are helpful for understanding how and why human errors have occurred. In this study, a novel approach is suggested based on the Safety-II concept, which allows us to collect HRA data by considering failure and success cases in parallel. In addition, since huge amount of information can be gathered if the failure and success cases are simultaneously involved, a big data analysis technique called the CART (Classification And Regression Tree) is applied to deal with this problem. As a result, it seems that the novel approach proposed by combining the Safety-II concept with the CART technique is useful because HRA practitioners are able to get HRA data with respect to diverse task contexts.
Keywords: Human reliability analysis | Nuclear power plant | Safety-II | Classification and regression tree | Event investigation report
Veracity handling and instance reduction in big data using interval type-2 fuzzy sets
کنترل صحت و کاهش نمونه در داده های بزرگ با استفاده از بازها های مجموعه های فازی نوع 2-2020
Within the aspect of big data, veracity refers to the existing uncertainty in the dataset. The continuous flow of unstructured data with unwanted noise may bring abnormality in the dataset making them unusable. In this paper, we propose a novel method to handle the veracity characteristic of the big data using the concept of footprint of uncertainty (FOU) in interval type-2 fuzzy sets (IT2 FSs). The proposed method helps in handling the veracity issue in big data and reduces the instances to a manageable extent. We have compared the results with the existing clustering based methods and examined the relationship between the clusters and the FOUs by comparing their centroids and defuzzified values. To scrutinize the validity of our results, we have also performed a number of additional experiments by appending extra instances to the datasets. To check its consistency and efficacy, the proposed methodology is assessed from three different aspects. Experimental result validates that the proposed method can suitably handle the veracity issue in big datasets and is efficient in reducing the instances.
Keywords: Instance reduction | Big data veracity | Interval type-2 fuzzy sets | Cluster centroid | Footprint of uncertainty
Stochastic parallel extreme artificial hydrocarbon networks: An implementation for fast and robust supervised machine learning in high-dimensional data
شبکه های هیدروکربنی مصنوعی موازی تصادفی تصادفی: پیاده سازی برای یادگیری ماشین تحت نظارت سریع و قوی در داده های با ابعاد بالا-2020
Artificial hydrocarbon networks (AHN) – a supervised learning method inspired on organic chemical structures and mechanisms – have shown improvements in predictive power and interpretability in comparison with other well-known machine learning models. However, AHN are very time-consuming that are not able to deal with large data until now. In this paper, we introduce the stochastic parallel extreme artificial hydrocarbon networks (SPE-AHN), an algorithm for fast and robust training of supervised AHN models in high-dimensional data. This training method comprises a population-based meta-heuristic optimization with defined individual encoding and objective function related to the AHN-model, an implementation in parallel-computing, and a stochastic learning approach for consuming large data. We conducted three experiments with synthetic and real data sets to validate the training execution time and performance of the proposed algorithm. Experimental results demonstrated that the proposed SPE-AHN outperforms the original-AHN method, increasing the speed of training more than 10, 000???? times in the worst case scenario. Additionally, we present two case studies in real data sets for solar-panel deployment prediction (regression problem), and human falls and daily activities classification in healthcare monitoring systems (classification problem). These case studies showed that SPEAHN improves the state-of-the-art machine learning models in both engineering problems. We anticipate our new training algorithm to be useful in many applications of AHN like robotics, finance, medical engineering, aerospace, and others, in which large amounts of data (e.g. big data) is essential.
Keywords: Machine learning | Parallel computing | Extreme learning machines | Stochastic learning | Regression | Classification | Big data
The Dark Web and cannabis use in the United States: Evidence from a big data research design
استفاده از وب تاریک و حشیش در ایالات متحده: شواهدی از طراحی تحقیقات داده های بزرگ-2020
Background: Cannabis is one of the most commonly sold drugs on cryptomarkets. Because of the anonymitygranting functions of Tor, no study has traced the within-country effect of the Dark Web on cannabis consumption patterns. This article uses a big data research design to examine the association between revealed interest in the Dark Web and self-reported cannabis use within US states from 2011 when Silk Road launched to 2015 when Operation Onymous shuttered nine markets. Methods: This study uses mixed effects ordinary least squared regressions to analyze U.S. state/year panel data, using robust standard errors to correct for heteroscedasticity. Marginal effect plots illustrate substantive effects. The dataset consists of state-level variables drawn from the Uniform Crime Report (UCR), the American Community Survey (ACS), the National Survey on Drug Use and Health, the Correlates of State Policy Project, and the Bureau of Justice Statistics Justice Expenditure and Employment Extracts. Data for the Dark Web interest measure are drawn from Google Trends. The proxy for Dark Web interest is an index of eight Dark Web related search queries. Results: The regression analysis indicates that Dark Web interest in US states positively correlates with cannabis consumption rates overall and among older adults (26+), but not youth (12–17) or younger adults (18–25). Additionally, Dark Web interest is positively associated with more frequent cannabis usage rates (i.e. use in the past month, excluding first time use) both overall and among older adults, but not among youth or younger adults. Dark Web interest does not correlate with casual use (i.e. use in the last year, excluding use in the past month) for any age bracket. Interacting Dark Web interest with state-level legalization regimes indicates that the association between Dark Web interest and cannabis consumption in the past year is no different in medically legalized states and amplified in states with recreational legalization. Lastly, the Dark Web interest term does not correlate with first time cannabis either overall or for any age category. Conclusions: Interest in the Dark Web is associated with increased cannabis use in U.S. states from 2011–2015, but the effect is concentrated in states with more frequent cannabis users, older users, and in states with recreational legalization of cannabis.
Keywords: Dark web | Cryptomarkets | Cannabis | Silk Road | Google Trends | Cannabis Legalization
An anatomy of waste generation flows in construction projects using passive bigger data
آناتومی جریان تولید زباله در پروژه های ساختمانی با استفاده از داده های بزرگتر -2020
Understanding waste generation flow is vital to any evidence-based effort by policy-makers and practitioners to successfully manage construction project waste. Previous research has found that accumulative waste generation in construction projects follows an S-curve, but improving our understanding of waste generation requires its investigation at a higher level of granularity. Such efforts, however, are often constrained by lack of quality ‘‘bigger” data, i.e. data that is bigger than normal small data. This research aims to provide an anatomy of waste generation flow in building projects by making use of a large set of data on waste generation in 19 demolition, 59 foundation, and 54 new building projects undertaken in Hong Kong between 2011 and 2019. We know that waste is generated in far from a steady stream as it is always impacted by contingent factors. However, we do find that peaks of waste generation in foundation projects appear when project duration is at 50–85%, and in new building projects at 40–70% of total project time. Our research provides useful information for waste managers in developing their waste management plans, arranging waste hauling logistics, and benchmarking waste management performance.
Keywords: Construction waste management | Waste generation flow | Building projects | Bigger data