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نتیجه جستجو - Big Data analytics

تعداد مقالات یافته شده: 233
ردیف عنوان نوع
1 The big picture on the internet of things and the smart city: a review of what we know and what we need to know
تصویر بزرگ در اینترنت اشیا و شهر هوشمند: مروری بر آنچه می‌دانیم و آنچه باید بدانیم-2022
This study examines how the application of the IoT in smart cities is discussed in the current academic literature. Based on bibliometric techniques, 1,802 articles were retrieved from the Scopus database and analyzed to identify the temporal nature of IoT research, the most relevant journals, authors, countries, keywords, and studies. The software tool VOSviewer was used to build the keyword co-occurrence network and to cluster the pertinent literature. Results show the significant growth of IoT research in recent years. The most productive authors, journals, and countries were also identified. The main findings from the keyword co-occurrence clustering and an in-depth qualitative analysis indicate that the IoT is used alongside other technologies including cloud computing, big data analytics, blockchain, artificial intelligence, and wireless telecommunication networks. The major applications of the IoT for smart cities include smart buildings, transportation, healthcare, smart parking, and smart grids. This review is one of the first attempts to map global IoT research in a smart city context and uses a comprehensive set of articles and bibliometric techniques to provide scholars and practitioners with an overview of what has been studied so far and to identify research gaps at the intersection of the IoT and the smart city.
keywords: اینترنت اشیا | شهر هوشمند | مرور | کتاب سنجی | Internet of things | Smart city | Review | Bibliometrics
مقاله انگلیسی
2 Digital Livestock Farming
دامداری دیجیتال-2021
As the global human population increases, livestock agriculture must adapt to provide more livestock products and with improved efficiency while also addressing concerns about animal welfare, environmental sustainability, and public health. The purpose of this paper is to critically review the current state of the art in digitalizing animal agriculture with Precision Livestock Farming (PLF) technologies, specifically biometric sensors, big data, and blockchain technology. Biometric sensors include either noninvasive or invasive sensors that monitor an individual animal’s health and behavior in real time, allowing farmers to integrate this data for population-level analyses. Real-time information from biometric sensors is processed and integrated using big data analytics systems that rely on statistical algorithms to sort through large, complex data sets to provide farmers with relevant trending patterns and decision-making tools. Sensors enabled blockchain technology affords secure and guaranteed traceability of animal products from farm to table, a key advantage in monitoring disease outbreaks and preventing related economic losses and food-related health pandemics. Thanks to PLF technologies, livestock agriculture has the potential to address the abovementioned pressing concerns by becoming more transparent and fostering increased consumer trust. However, new PLF technologies are still evolving and core component technologies (such as blockchain) are still in their infancy and insufficiently validated at scale. The next generation of PLF technologies calls for preventive and predictive analytics platforms that can sort through massive amounts of data while accounting for specific variables accurately and accessibly. Issues with data privacy, security, and integration need to be addressed before the deployment of multi-farm shared PLF solutions be- comes commercially feasible. Implications Advanced digitalization technologies can help modern farms optimize economic contribution per animal, reduce the drudgery of repetitive farming tasks, and overcome less effective isolated solutions. There is now a strong cultural emphasis on reducing animal experiments and physical contact with animals in-order-to enhance animal welfare and avoid disease outbreaks. This trend has the potential to fuel more research on the use of novel biometric sensors, big data, and blockchain technology for the mutual benefit of livestock producers, consumers, and the farm animals themselves. Farmers’ autonomy and data-driven farming approaches compared to experience-driven animal manage- ment practices are just several of the multiple barriers that digitalization must overcome before it can become widely implemented.
Keywords: Precision Livestock Farming | digitalization | Digital Technologies in Livestock Systems | sensor technology | big data | blockchain | data models | livestock agriculture
مقاله انگلیسی
3 A framework based on BWM for big data analytics (BDA) barriers in manufacturing supply chains
چارچوبی مبتنی بر BWM برای موانع تجزیه و تحلیل داده های بزرگ (BDA) در تولید زنجیره های تأمین-2021
Due to its potential utility, Big Data (BD) recently attracted researchers and practitioners in decision- making. Big Data analytics (BDA) becomes more common among manufacturing companies because it lets them gain insight and make decisions based on BD. Given the importance of both BD and BDA, this study aims to identify and analyse essential BDA adoption barriers in supply chains. This study explores the current knowledge base using a BWM (Best Worst Method) to discuss these barriers. Data were obtained from five Indian manufacturing companies. Research findings show that data-related barriers are most significant. The findings will help managers understand the exact nature of the challenges and possible advantages of the BDA and implement BDA policies for the growth and output of supply chain operations.© 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the 3rd International e-Con- ference on Frontiers in Mechanical Engineering and nano Technology.
Keywords: Big data analytics | Barriers | Manufacturing supply chains | Best worst method (BWM)
مقاله انگلیسی
4 Computer vision approach to characterize size and shape phenotypes of horticultural crops using high-throughput imagery
رویکرد بینایی رایانه ای برای توصیف فنوتیپ های اندازه و شکل محصولات باغی با استفاده از تصاویر با توان بالا-2021
For many horticultural crops, variation in quality (e.g., shape and size) contributes significantly to the crop’s market value. Metrics characterizing less subjective harvest quantities (e.g., yield and total biomass) areroutinely monitored. In contrast, metrics quantifying more subjective crop quality characteristics such as ideal size and shape remain difficult to characterize objectively at the production-scale due to the lack of modular technologies for high-throughput sensing and computation. Several horticultural crops are sent to packing facilities after having been harvested, where they are sorted into boxes and containers using high-throughput scanners. These scanners capture images of each fruit or vegetable being sorted and packed, but the images are typically used solely for sorting purposes and promptly discarded. With further analysis, these images could offer unparalleled insight on how crop quality metrics vary at the industrial production-scale and provide further insight into how these characteristics translate to overall market value. At present, methods for extracting and quantifying quality characteristics of crops using images generated by existing industrial infrastructure have not been developed. Furthermore, prior studies that investigated horticultural crop quality metrics, specifically of size and shape, used a limited number of samples, did not incorporate deformed or non-marketable samples, and did not use images captured from high-throughput systems. In this work, using sweetpotato (SP) as a use case, we introduce a computer vision algorithm for quantifying shape and size characteristics in a high-throughput manner. This approach generates 3D model of SPs from two 2D images captured by an industrial sorter 90 degrees apart and extracts 3D shape features in a few hundred milliseconds. We applied the 3D reconstruction and feature extraction method to thousands of image samples to demonstrate how variations in shape features across SP cultivars can be quantified. We created a SP shape dataset containing SP images, extracted shape features, and qualitative shape types (U.S. No. 1 or Cull). We used this dataset to develop a neural network-based shape classifier that was able to predict Cull vs. U.S. No. 1 SPs with 84.59% accuracy. In addition, using univariate Chi-squared tests and random forest, we identified the most important features for determining qualitative shape type (U.S. No. 1 or Cull) of the SPs. Our study serves as a key step towards enabling big data analytics for industrial SP agriculture. The methodological framework is readily transferable to other horticultural crops, particularly those that are sorted using commercial imaging equipment.
Keywords: Crop phenotyping | Machine learning | Computer vision
مقاله انگلیسی
5 Improving supply chain resilience through industry 4:0: A systematic literature review under the impressions of the COVID-19 pandemic
بهبود انعطاف پذیری زنجیره تأمین از طریق صنعت 4:0: بررسی ادبیات سیستماتیک تحت تأثیر همه گیری COVID-19-2021
The COVID-19 pandemic is one of the most severe supply chain disruptions in history and has challenged practitioners and scholars to improve the resilience of supply chains. Recent technological progress, especially industry 4.0, indicates promising possibilities to mitigate supply chain risks such as the COVID-19 pandemic. However, the literature lacks a comprehensive analysis of the link between industry 4.0 and supply chain resilience. To close this research gap, we present evidence from a systematic literature review, including 62 papers from high-quality journals. Based on a categorization of industry 4.0 enabler technologies and supply chain resilience antecedents, we introduce a holistic framework depicting the relationship between both areas while exploring the current state-of-the-art. To verify industry 4.0’s resilience opportunities in a severe supply chain disruption, we apply our framework to a use case, the COVID-19-affected automotive industry. Overall, our results reveal that big data analytics is particularly suitable for improving supply chain resilience, while other industry 4.0 enabler technologies, including additive manufacturing and cyber-physical systems, still lack proof of effectiveness. Moreover, we demonstrate that visibility and velocity are the resilience antecedents that benefit most from industry 4.0 implementation. We also establish that industry 4.0 holistically supports pre-disruption resilience measures, enabling more effective proactive risk management. Both research and practice can benefit from this study. While scholars may analyze resilience potentials of under-explored enabler technologies, practitioners can use our findings to guide industry 4.0 investment decisions.
Keywords: Industry 4.0 | Supply chain risk management | Supply chain resilience | Supply chain disruption | Digital supply chain | Literature review
مقاله انگلیسی
6 Integrating big data analytics into supply chain finance: The roles of information processing and data-driven culture
ادغام تجزیه و تحلیل داده های بزرگ در امور مالی زنجیره تأمین: نقش پردازش اطلاعات و فرهنگ داده محور-2021
The role of big data in implementing supply chain finance (SCF) initiatives lacks empirical study. There is little guidance available for managers on developing an integrated SCF process in the era of big data. Using organizational information processing theory, this study develops and empirically tests a theoretical framework that investigates the effect of big data analytics capability (BDAC) on SCF Integration, and the moderating effect of data-driven culture. The hypothesized relationships were tested using structural equation modelling and moderated regression analysis, with primary survey data collected from a sample of 307 manufacturing firms in China. The results indicate that BDAC has a significant positive effect on internal SCF Integration, and internal SCF Integration fully mediates the relationships between BDAC and SCF Integration with customers and sup- pliers. Data-driven culture significantly moderates the effect of BDAC on internal SCF Integration. These empirical findings provide timely and useful guidance for managers on using big data analytics and data-driven culture to implement integrated SCF practices to survive in today’s data-rich and uncertain environment.
Keywords: Big data analytics capability | Data-driven culture | Integrated supply chain finance | Information processing capability
مقاله انگلیسی
7 TITAN: A knowledge-based platform for Big Data workflow management
TITAN: یک پلت فرم مبتنی بر دانش برای مدیریت گردش کار داده های بزرگ-2021
Modern applications of Big Data are transcending from being scalable solutions of data processing and analysis, to now provide advanced functionalities with the ability to exploit and understand the underpinning knowledge. This change is promoting the development of tools in the intersection of data processing, data analysis, knowledge extraction and management. In this paper, we propose TITAN, a software platform for managing all the life cycle of science workflows from deployment to execution in the context of Big Data applications. This platform is characterised by a design and operation mode driven by semantics at different levels: data sources, problem domain and workflow components. The proposed platform is developed upon an ontological framework of meta-data consistently managing processes and models and taking advantage of domain knowledge. TITAN comprises a well-grounded stack of Big Data technologies including Apache Kafka for inter-component communication, Apache Avro for data serialisation and Apache Spark for data analytics. A series of use cases are conducted for validation, which comprises workflow composition and semantic meta-data management in academic and real-world fields of human activity recognition and land use monitoring from satellite images./
keywords: تجزیه و تحلیل داده های بزرگ | مفاهیم | استخراج دانش | Big Data analytics | Semantics | Knowledge extraction
مقاله انگلیسی
8 The varying patterns of rail transit ridership and their relationships with fine-scale built environment factors: Big data analytics from Guangzhou
الگوهای مختلف تفریحی حمل و نقل ریلی و روابط آنها با عوامل محیطی ساخته شده در مقیاس خوب: تجزیه و تحلیل داده های بزرگ از گوانگژو-2020
Investigating the varying ridership patterns of rail transit ridership and their influencing factors at the station level is essential for station planning, urban planning, and passenger flow management. Although many studies have investigated the associations between rail transit ridership and built environment, few studies combined spatial big data to characterize the built environment factors at a fine scale and linked those factors with the varying patterns of rail transit ridership. In this study, we characterized the fine-scale built environment factors in the central urban area of Guangzhou, China, by integrating multi-source geospatial big data including Tencent user data, building footprint and stories, points of interest (POI) data and Google Earth high-resolution images. Six direct ridership models (DRMs) based on the backward stepwise regression method were built to compare the different effects between daily, temporal and directional ridership. The results indicated that number of station entrances/exits and transfer dummy, were positively associated with rail transit ridership, while connecting bus station sites and the parking lots were not significantly related to ridership. Population density and common residences land were found to be dominating factors in promoting morning boarding & evening alighting ridership, which implied that these two factors should be focused on to encourage commuting-purpose rail transit usage. However, the indistinct effect of urban villages on rail transit ridership suggested planners to pay more attentions on urban regeneration at the pedestrian catchment areas (PCAs) with urban villages. High employment density and a large FAR were suggested at the employment-oriented areas owing to their importance in promoting rail transit ridership, especially the morning alighting & evening boarding ridership. Moreover, educational research land use significantly affected weekday ridership while sports land use positively influenced weekend ridership, which suggested planners to pay more attention on the non-commuting trips. The different influencing mechanisms of various types of rail transit ridership highlighted the need to consider land use balance planning and trip demand optimization in highly urbanized metropolises in developing countries.
Keywords: Rail transit ridership | Big data | Fine-scale | Built environment | Guangzhou
مقاله انگلیسی
9 The questions we ask: Opportunities and challenges for using big data analytics to strategically manage human capital resources
سوالاتی که می پرسیم: فرصت ها و چالش های استفاده از تجزیه و تحلیل داده های بزرگ برای مدیریت استراتژیک منابع سرمایه انسانی-2020
Big data analytics have transformed research in many fields, including the business areas of marketing, accounting and finance, and supply chain management. Yet, the discussion surrounding big data analytics in human resource management has primarily focused on job candidate screenings. In this article, we consider how significant strategic human capital questions can be addressed with big data analytics, enabling HR to enhance overall firm performance. We also examine how new data sources that help assess workforce performance in real time can assist in the identification and development of the knowledge stars that contribute to firm performance disproportionately as well as help reinforce firm capabilities. But in order for big data analytics to be successful in the HR field, regulatory and ethical challenges must also be addressed; these include privacy concerns and, in Europe, the General Data Protection Regulation (GDPR). We conclude by discussing how big data analytics can facilitate strategic change within HR and the organization as a whole.
KEYWORDS: Big data analytics | Workforce analytics | Stakeholder management | Strategic human | capital | Knowledge stars | Human resource management
مقاله انگلیسی
10 Big Data Analytics for Venture Capital Application:Towards Innovation Performance Improvement
تجزیه و تحلیل داده های بزرگ برای برنامه های سرمایه گذاری: به سمت بهبود عملکرد نوآوری-2020
By using the panel date of Chinese enterprises, this paper analyzes the influence of venture capital on innovation performance. In this paper, the number of patent application and the patent quality(invention patent applications, number of effective patents, IPC number of international patent classification, and patent claims) are used to measure the innovation performance of enterprises, and the regression results show that the innovation performance is significantly promoted by the venture capital; for industries with higher dependence on external financing and high technology intensity and areas with better protection of property rights, venture capital promotes innovation performance more significantly. In this paper, it further distinguishes the characteristics of venture capital institutions, and finds that the promotion effect of non-state-owned venture capital on innovation performance is significantly greater than that of state-owned venture capital; the venture capital institutions with high reputation and high network capital play a more significant role in promoting innovation performance.
Keywords: Data panel model | Big data | Innovation performance
مقاله انگلیسی
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