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نتیجه جستجو - تحلیل داده های بزرگ

تعداد مقالات یافته شده: 200
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 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
3 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
4 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
5 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
6 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
7 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
8 Does government information release really matter in regulating contagionevolution of negative emotion during public emergencies? From the perspective of cognitive big data analytics
آیا انتشار اطلاعات دولتی در تنظیم تکامل منفی احساسات منفی در مواقع اضطراری عمومی اهمیت دارد؟ از منظر تجزیه و تحلیل داده های بزرگ شناختی-2020
The breeding and spreading of negative emotion in public emergencies posed severe challenges to social governance. The traditional government information release strategies ignored the negative emotion evolution mechanism. Focusing on the information release policies from the perspectives of the government during public emergency events, by using cognitive big data analytics, our research applies deep learning method into news framing framework construction process, and tries to explore the influencing mechanism of government information release strategy on contagion-evolution of negative emotion. In particular, this paper first uses Word2Vec, cosine word vector similarity calculation and SO-PMI algorithms to build a public emergenciesoriented emotional lexicon; then, it proposes a emotion computing method based on dependency parsing, designs an emotion binary tree and dependency-based emotion calculation rules; and at last, through an experiment, it shows that the emotional lexicon proposed in this paper has a wider coverage and higher accuracy than the existing ones, and it also performs a emotion evolution analysis on an actual public event based on the emotional lexicon, using the emotion computing method proposed. And the empirical results show that the algorithm is feasible and effective. The experimental results showed that this model could effectively conduct fine-grained emotion computing, improve the accuracy and computational efficiency of sentiment classification. The final empirical analysis found that due to such defects as slow speed, non transparent content, poor penitence and weak department coordination, the existing government information release strategies had a significant negative impact on the contagion-evolution of anxiety and disgust emotion, could not regulate negative emotions effectively. These research results will provide theoretical implications and technical supports for the social governance. And it could also help to establish negative emotion management mode, and construct a new pattern of the public opinion guidance.
Keywords: Government information release | Cognitive big data analytics | E-government | Sentiment analysis | Public emergency events
مقاله انگلیسی
9 Big data analytics for financial Market volatility forecast based on support vector machine
تجزیه و تحلیل داده های بزرگ برای پیش بینی نوسانات مالی بازار بر اساس دستگاه بردار پشتیبانی-2020
High-frequency data provides a lot of materials and broad research prospects for in-depth research and understanding on financial market behavior, but the problems solved in the research of high-frequency data are far less than the problems faced and encountered, and the research value of high-frequency data will be greatly reduced without solving these problems. Volatility is an important measurement index of market risk, and the research and forecasting on the volatility of high-frequency data is of great significance to investors, government regulators and capital markets. To this end, by modelling the jump volatility of high-frequency data, the shortterm volatility of high-frequency data are predicted.
Keywords: Big data | Financial market | Volatility | Support vector machine
مقاله انگلیسی
10 Text mining of industry 4:0 job advertisements
استخراج متن آگهی های شغلی صنعت 4:0-2020
Since changes in job characteristics in areas such as Industry 4.0 are rapid, fast tool for analysis of job advertisements is needed. Current knowledge about competencies required in Industry 4.0 is scarce. The goal of this paper is to develop a profile of Industry 4.0 job advertisements, using text mining on publicly available job advertisements, which are often used as a channel for collecting relevant information about the required knowledge and skills in rapid-changing industries. We searched website, which publishes job advertisements, related to Industry 4.0, and performed text mining analysis on the data collected from those job advertisements. Analysis of the job advertisements revealed that most of them were for full time entry; associate and mid-senior level management positions and mainly came from the United States and Germany. Text mining analysis resulted in two groups of job profiles. The first group of job profiles was focused solely on the knowledge related to Industry 4.0: cyberphysical systems and the Internet of things for robotized production; and smart production design and production control. The second group of job profiles was focused on more general knowledge areas, which are adapted to Industry 4.0: supply change management, customer satisfaction, and enterprise software. Topic mining was conducted on the extracted phrases generating various multidisciplinary job profiles. Higher educational institutions, human resources professionals, as well as experts that are already employed or aspire to be employed in Industry 4.0 organizations, would benefit from the results of our analysis.
Keywords: Human resource management | Text mining | Job profiles | Big data analytics | Industry 4.0 | Education | Smart factory
مقاله انگلیسی
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