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
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
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
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
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
Big data analytics in health sector: Theoretical framework, techniques and prospects
تجزیه و تحلیل داده های بزرگ در بخش بهداشت و درمان: چارچوب نظری ، تکنیک ها و چشم انداز-2020
Clinicians, healthcare providers-suppliers, policy makers and patients are experiencing exciting opportunities in light of new information deriving from the analysis of big data sets, a capability that has emerged in the last decades. Due to the rapid increase of publications in the healthcare industry, we have conducted a structured review regarding healthcare big data analytics. With reference to the resource-based view theory we focus on how big data resources are utilised to create organization values/capabilities, and through content analysis of the selected publications we discuss: the classification of big data types related to healthcare, the associate analysis techniques, the created value for stakeholders, the platforms and tools for handling big health data and future aspects in the field. We present a number of pragmatic examples to show how the advances in healthcare were made possible. We believe that the findings of this review are stimulating and provide valuable information to practitioners, policy makers and researchers while presenting them with certain paths for future research.
Keywords: Big data analytics | Health-Medicine | Decision-making | Machine learning | Operations research (OR) techniques
Towards a real-time processing framework based on improved distributed recurrent neural network variants with fastText for social big data analytics
به سمت یک چارچوب پردازش در زمان واقعی بر اساس بهبود انواع شبکه عصبی مکرر توزیع شده با fastText برای تجزیه و تحلیل داده های بزرگ اجتماعی-2020
Big data generated by social media stands for a valuable source of information, which offers an excellent opportunity to mine valuable insights. Particularly, User-generated contents such as reviews, recommendations, and users’ behavior data are useful for supporting several marketing activities of many companies. Knowing what users are saying about the products they bought or the services they used through reviews in social media represents a key factor for making decisions. Sentiment analysis is one of the fundamental tasks in Natural Language Processing. Although deep learning for sentiment analysis has achieved great success and allowed several firms to analyze and extract relevant information from their textual data, but as the volume of data grows, a model that runs in a traditional environment cannot be effective, which implies the importance of efficient distributed deep learning models for social Big Data analytics. Besides, it is known that social media analysis is a complex process, which involves a set of complex tasks. Therefore, it is important to address the challenges and issues of social big data analytics and enhance the performance of deep learning techniques in terms of classification accuracy to obtain better decisions. In this paper, we propose an approach for sentiment analysis, which is devoted to adopting fastText with Recurrent neural network variants to represent textual data efficiently. Then, it employs the new representations to perform the classification task. Its main objective is to enhance the performance of well-known Recurrent Neural Network (RNN) variants in terms of classification accuracy and handle large scale data. In addition, we propose a distributed intelligent system for real-time social big data analytics. It is designed to ingest, store, process, index, and visualize the huge amount of information in real-time. The proposed system adopts distributed machine learning with our proposed method for enhancing decision-making processes. Extensive experiments conducted on two benchmark data sets demonstrate that our proposal for sentiment analysis outperforms well-known distributed recurrent neural network variants (i.e., Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (BiLSTM), and Gated Recurrent Unit (GRU)). Specifically, we tested the efficiency of our approach using the three different deep learning models. The results show that our proposed approach is able to enhance the performance of the three models. The current work can provide several benefits for researchers and practitioners who want to collect, handle, analyze and visualize several sources of information in real-time. Also, it can contribute to a better understanding of public opinion and user behaviors using our proposed system with the improved variants of the most powerful distributed deep learning and machine learning algorithms. Furthermore, it is able to increase the classification accuracy of several existing works based on RNN models for sentiment analysis.
Keywords: Big data | FastText | Recurrent neural networks | LSTM | BiLSTM | GRU | Natural language processing | Sentiment analysis | Social big data analytics
A novel intelligent option price forecasting and trading system by multiple kernel adaptive filters
رویکرد پیش بینی قیمت و گزینه سیستم تجاری با فیلترهای انطباقی چند هسته ای-2020
Derivatives such as options are complex financial instruments. The risk in option trading leads to the demand of trading support systems for investors to control and hedge their risk. The nonlinearity and non-stationarity of option dynamics are the main challenge of option price forecasting. To address the problem, this study develops a multi-kernel adaptive filters (MKAF) for online option trading. MKAF is an improved version of the adaptive filter, which employs multiple kernels to enhance the richness of nonlinear feature representation. The MKAF is a fully adaptive online algorithm. The strength of MKAF is that the weights to the kernels are simultaneous optimally determined in filter coefficient updates. We do not need to design the weights separately. Therefore, MKAF is good at tracking nonstationary nonlinear option dynamics. Moreover, to reduce the computation time in updating the filter, and prevent overadaptation, the number of kernels is restricted by using coherence-based sparsification, which constructs a set of dictionary and uses a coherence threshold to restrict the dictionary size. This study compared the new method with traditional ones, we found the performance improvement is significant and robust. Especially, the cumulated trading profits are substantially increased
Keywords: Artificial intelligence | Adaptive filter | Multiple Kernel Machine | Big data analysis | Data mining | Financial forecasting
Actualizing big data analytics affordances: A revelatory case study
واقعی سازی هزینه های تحلیلی داده های بزرگ: یک مطالعه موردی الهامی-2020
Drawing on a revelatory case study, we identify four big data analytics (BDA) actualization mechanisms: (1) enhancing, (2) constructing, (3) coordinating, and (4) integrating, which manifest in actions on three sociotechnical system levels, i.e., the structure, actor, and technology levels. We investigate the actualization of four BDA affordances at an automotive manufacturing company, i.e., establishing customer-centric marketing, provisioning vehicle-data-driven services, data-driven vehicle developing, and optimizing production processes. This study introduces a theoretical perspective to BDA research that explains how organizational actions contribute to actualizing BDA affordances. We further provide practical implications that can help guide practitioners in BDA adoption.
Keywords: Big data analytics | Affordance theory | Socio-technical approach | Organizational transformation | Organizational benefits | Affordance actualization
Leveraging internet of things and big data analytics initiatives in European and American firms: Is data quality a way to extract business value?
اعمال اینترنت اشیاء و ابتکارات تحلیلی داده های بزرگ در شرکت های اروپایی و آمریکایی: آیا کیفیت داده راهی برای استخراج ارزش تجارت است؟-2020
Big data analytics (BDA) and the Internet of Things (IoT) tools are considered crucial investments for firms to distinguish themselves among competitors. Drawing on a strategic management perspective, this study proposes that BDA and IoT capabilities can create significant value in business processes if supported by a good level of data quality, which will lead to a better competitive advantage. Responses are collected from 618 European and American firms that use IoT and BDA applications. Partial least squares results reveal that better data quality is needed to unlock the value of IoT and BDA capabilities.
Keywords: Big data analytics | Internet of things | Strategic management | Knowledge-based theory | Dynamics capability theory