Big Data最新文献

Link prediction, which has important applications in many fields, predicts the possibility of the link between two nodes in a graph. Link prediction based on Graph Neural Network (GNN) obtains node representation and graph structure through GNN, which has attracted a growing amount of attention recently. However, the existing GNN-based link prediction approaches possess some shortcomings. On the one hand, because a graph contains different types of nodes, it leads to a great challenge for aggregating information and learning node representation from its neighbor nodes. On the other hand, the attention mechanism has been an effect instrument for enhancing the link prediction performance. However, the traditional attention mechanism is always monotonic for query nodes, which limits its influence on link prediction. To address these two problems, a Dual-Path Graph Neural Network (DPGNN) for link prediction is proposed in this study. First, we propose a novel Local Random Features Augmentation for Graph Convolution Network as a baseline of one path. Meanwhile, Graph Attention Network version 2 based on dynamic attention mechanism is adopted as a baseline of the other path. And then, we capture more meaningful node representation and more accurate link features by concatenating the information of these two paths. In addition, we propose an adaptive auxiliary module for better balancing the weight of auxiliary tasks, which brings more benefit to link prediction. Finally, extensive experiments verify the effectiveness and superiority of our proposed DPGNN for link prediction.

In the last 2 years, there has been a significant upswing in oil prices, leading to a decline in economic activity and demand. This trend holds substantial implications for the global economy, particularly within the emerging business landscape. Among the influential risk factors impacting the returns of shipping stocks, none looms larger than the volatility in oil prices. Yet, only a limited number of studies have explored the complex relationship between oil price shocks and the dynamics of the liner shipping industry, with specific focus on uncertainty linkages and potential diversification strategies. This study aims to investigate the co-movements and asymmetric associations between oil prices (specifically, West Texas Intermediate and Brent) and the stock returns of three prominent shipping companies from Germany, South Korea, and Taiwan. The results unequivocally highlight the indispensable role of oil prices in shaping both short-term and long-term shipping stock returns. In addition, the research underscores the statistical significance of exchange rates and interest rates in influencing these returns, with their effects varying across different time horizons. Notably, shipping stock prices exhibit heightened sensitivity to positive movements in oil prices, while exchange rates and interest rates exert contrasting impacts, one being positive and the other negative. These findings collectively illuminate the profound influence of market sentiment regarding crucial economic indicators within the global shipping sector.

With the acceleration of urbanization, air pollution, especially PM_2.5, has seriously affected human health and reduced people's life quality. Accurate PM_2.5 prediction is significant for environmental protection authorities to take actions and develop prevention countermeasures. In this article, an adapted Kalman filter (KF) approach is presented to remove the nonlinearity and stochastic uncertainty of time series, suffered by the autoregressive integrated moving average (ARIMA) model. To further improve the accuracy of PM_2.5 forecasting, a hybrid model is proposed by introducing an autoregressive (AR) model, where the AR part is used to determine the state-space equation, whereas the KF part is used for state estimation on PM_2.5 concentration series. A modified artificial neural network (ANN), called AR-ANN is introduced to compare with the AR-KF model. According to the results, the AR-KF model outperforms the AR-ANN model and the original ARIMA model on the predication accuracy; that is, the AR-ANN obtains 10.85 and 15.45 of mean absolute error and root mean square error, respectively, whereas the ARIMA gains 30.58 and 29.39 on the corresponding metrics. It, therefore, proves that the presented AR-KF model can be adopted for air pollutant concentration prediction.

Market uncertainty greatly interferes with the decisions and plans of market participants, thus increasing the risk of decision-making, leading to compromised interests of decision-makers. Cotton price index (hereinafter referred to as cotton price) volatility is highly noisy, nonlinear, and stochastic and is susceptible to supply and demand, climate, substitutes, and other policy factors, which are subject to large uncertainties. To reduce decision risk and provide decision support for policymakers, this article integrates 13 factors affecting cotton price index volatility based on existing research and further divides them into transaction data and interaction data. A long- and short-term memory (LSTM) model is constructed, and a comparison experiment is implemented to analyze the cotton price index volatility. To make the constructed model explainable, we use explainable artificial intelligence (XAI) techniques to perform statistical analysis of the input features. The experimental results show that the LSTM model can accurately analyze the cotton price index fluctuation trend but cannot accurately predict the actual price of cotton; the transaction data plus interaction data are more sensitive than the transaction data in analyzing the cotton price fluctuation trend and can have a positive effect on the cotton price fluctuation analysis. This study can accurately reflect the fluctuation trend of the cotton market, provide reference to the state, enterprises, and cotton farmers for decision-making, and reduce the risk caused by frequent fluctuation of cotton prices. The analysis of the model using XAI techniques builds the confidence of decision-makers in the model.

An accurate resource usage prediction in the big data streaming applications still remains as one of the complex processes. In the existing works, various resource scaling techniques are developed for forecasting the resource usage in the big data streaming systems. However, the baseline streaming mechanisms limit with the issues of inefficient resource scaling, inaccurate forecasting, high latency, and running time. Therefore, the proposed work motivates to develop a new framework, named as Gaussian adapted Markov model (GAMM)-overhauled fluctuation analysis (OFA), for an efficient big data streaming in the cloud systems. The purpose of this work is to efficiently manage the time-bounded big data streaming applications with reduced error rate. In this study, the gating strategy is also used to extract the set of features for obtaining nonlinear distribution of data and fat convergence solution, used to perform the fluctuation analysis. Moreover, the layered architecture is developed for simplifying the process of resource forecasting in the streaming applications. During experimentation, the results of the proposed stream model GAMM-OFA are validated and compared by using different measures.

The mechanism of cooperative innovation (CI) for high-tech firms aims to improve their technological innovation performance. It is the effective integration of the internal and external innovation resources of these firms, along with the simultaneous reduction in the uncertainty of technological innovation and the maintenance of the comparative advantage of the firms in the competition. This study used 322 high-tech firms as our sample, which were located in 33 national innovation demonstration bases identified by the Chinese government. We implemented a multiple linear regression to test the impact of CI conducted by these high-tech firms at the level of their technological innovation performance. In addition, the study further examined the moderating effect of two boundary conditions-big data capabilities and policy support (PS)-on the main hypotheses. Our study found that high-tech firms carrying out CI can effectively improve their technological innovation performance, with big data capabilities and PS significantly enhancing the degree of this influence. The study reveals the intrinsic mechanism of the impact of CI on the technological innovation performance of high-tech firms, which, to a certain extent, expands the application context of CI and enriches the research perspective on the impact of CI on the innovation performance of firms. At the same time, the findings provide insight for how high-tech firms in the digital era can make reasonable use of data empowerment in the process of CI to achieve improved technological innovation performance.

Public procurement is viewed as a major market force that can be used to promote innovation and drive small and medium-sized enterprises growth. In such cases, procurement system design relies on intermediates that provide vertical linkages between suppliers and providers of innovative services and products. In this work we propose an innovative methodology for decision support in the process of supplier discovery, which precedes the final supplier selection. We focus on data gathered from community-based sources such as Reddit and Wikidata and avoid any use of historical open procurement datasets to identify small and medium sized suppliers of innovative products and services that own very little market shares. We look into a real-world procurement case study from the financial sector focusing on the Financial and Market Data offering and develop an interactive web-based support tool to address certain requirements of the Italian central bank. We demonstrate how a suitable selection of natural language processing models, such as a part-of-speech tagger and a word-embedding model, in combination with a novel named-entity-disambiguation algorithm, can efficiently analyze huge quantity of textual data, increasing the probability of a full coverage of the market.

Owing to increasing size of the real-world networks, their processing using classical techniques has become infeasible. The amount of storage and central processing unit time required for processing large networks is far beyond the capabilities of a high-end computing machine. Moreover, real-world network data are generally distributed in nature because they are collected and stored on distributed platforms. This has popularized the use of the MapReduce, a distributed data processing framework, for analyzing real-world network data. Existing MapReduce-based methods for connected components detection mainly struggle to minimize the number of MapReduce rounds and the amount of data generated and forwarded to the subsequent rounds. This article presents an efficient MapReduce-based approach for finding connected components, which does not forward the complete set of connected components to the subsequent rounds; instead, it writes them to the Hadoop Distributed File System as soon as they are found to reduce the amount of data forwarded to the subsequent rounds. It also presents an application of the proposed method in contact tracing. The proposed method is evaluated on several network data sets and compared with two state-of-the-art methods. The empirical results reveal that the proposed method performs significantly better and is scalable to find connected components in large-scale networks.

The stock market is heavily influenced by global sentiment, which is full of uncertainty and is characterized by extreme values and linear and nonlinear variables. High-frequency data generally refer to data that are collected at a very fast rate based on days, hours, minutes, and even seconds. Stock prices fluctuate rapidly and even at extremes along with changes in the variables that affect stock fluctuations. Research on investment risk estimation in the stock market that can identify extreme values is nonlinear, reliable in multivariate cases, and uses high-frequency data that are very important. The extreme value theory (EVT) approach can detect extreme values. This method is reliable in univariate cases and very complicated in multivariate cases. The purpose of this research was to collect, characterize, and analyze the investment risk estimation literature to identify research gaps. The literature used was selected by applying the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) and sourced from Sciencedirect.com and Scopus databases. A total of 1107 articles were produced from the search at the identification stage, reduced to 236 in the eligibility stage, and 90 articles in the included studies set. The bibliometric networks were visualized using the VOSviewer software, and the main keyword used as the search criteria is "VaR." The visualization showed that EVT, the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models, and historical simulation are models often used to estimate the investment risk; the application of the machine learning (ML)-based investment risk estimation model is low. There has been no research using a combination of EVT and ML to estimate the investment risk. The results showed that the hybrid model produced better Value-at-Risk (VaR) accuracy under uncertainty and nonlinear conditions. Generally, models only use daily return data as model input. Based on research gaps, a hybrid model framework for estimating risk measures is proposed using a combination of EVT and ML, using multivariable and high-frequency data to identify extreme values in the distribution of data. The goal is to produce an accurate and flexible estimated risk value against extreme changes and shocks in the stock market. Mathematics Subject Classification: 60G25; 62M20; 6245; 62P05; 91G70.