Chinese traditional culture is not only a knowledge, but more importantly, it can shape personality. It can also serve as a way of thinking for the study of practical problems and open the way for innovative thinking.
In the era of big data, data has become another factor of production after labor, capital, land, knowledge, technology and management. With the continuous improvement of the level of informatization, data has been widely used in tax collection and management and tax business. With the help of highly developed information technology and the use of various information systems, it can greatly promote the efficiency of tax management and work quality; Work brings challenges. This paper studies the role of big data in tax collection and management and the improvement of the tax business environment. The research results show that big data tax collection and management has effectively improved the tax business environment: on the one hand, big data tax collection and management can achieve panoramic monitoring of tax sources, combat tax evasion and tax evasion, and significantly improve corporate tax compliance by mastering massive financial and tax data and using big data technology. On the other hand, the big data of tax collection and management reduces taxpayers’ tax costs through online tax collection, and realizes the “policy discoverer” through the intelligent retrieval and push system of preferential tax policies. At the same time, the big data of tax collection and management can reduce the cost of tax declaration through online tax collection, and realize the "policy finder" through the intelligent search and push system of preferential tax policies, so as to improve the execution of preferential tax policies. The research results of this paper provide theoretical support and empirical basis for the continuous improvement of tax informatization construction.
The rapid development of big data technology has transformed the business models of many Chinese enterprises to information. As a new type of production factor, data is gradually being valued by enterprises. Many companies have used big data technology to mine the potential value behind the data, and it has been widely used in the field of financial decision-making. Therefore, it is necessary to introduce big data technology into the financial decision-making process of enterprises, so as to organize and analyze relevant data onto enterprises in a timely and effective manner, so as to provide scientific and effective basis of enterprise decision-makers. This paper mainly adopts literature research method and interdisciplinary research method to deeply study the application process of big data technology in enterprise financial decision-making. This paper first illustrates the necessity of introducing big data technology into the financial decision-making process of enterprises. Then, the main methods and specific functions of big data technology in the process of enterprise financial decision-making to explain the role of big data technology in the process. Finally, the platform of enterprise financial decision-making is designed based on big data technology. This paper finds that the application of big data technology in the financial decision-making process of enterprises is mainly reflected in four aspects: first, data collection; Second, data storage; Third, financial analysis; Fourth, decision support. The introduction of big data technology into the financial decision-making process of enterprises is beneficial to broaden the channels of data collection and enrich the methods of financial analysis and decision-making, but also provides strong support for enterprises to make more accurate and effective financial decision-making schemes.
Today, in the era of big data, data shows the characteristics of large scale, modal diversity and rapid growth. The value of language data also increases explosively, and the value of Chinese corpus information analysis also increases correspondingly. The crawler obtains a large number of Chinese sentences from different styles, establishes a personal corpus, designs and optimizes the database, analyzes the sentence patterns of each corpus in the corpus, and finally establishes a Chinese sentence pattern database, makes full use of a large number of data in the sentence pattern database, and makes multi-directional statistics and analysis of sentence patterns. Taking the corpus in the sentence pattern corpus as a sample, it uses kmeans + + for clustering, The sentence space representation model of the article is constructed, and the feature vector of each article is obtained, so as to carry out the application research of sentence pattern.
Covid-19 has caused a plummet in the number of tourists visiting to the South Central of Vietnam and driven changes in their traveling behaviors. This has formed challenging barriers over the operations of entities in the tourism industry. The purpose of this paper is supporting the tourism organizations to attract domestic and foreign visitors to the South Central of Vietnam post the pandemic. The research uses the secondary data collected by Vitours company - a leading travel agency in South Central - to demonstrate tourists’ demands when visiting destinations in the area between September 2020 and April 2021. Data mining, clustering and association rules techniques are also applied to classify traveler's segmentations and analyze the connections among these groups. The findings of this research indicates 4 clusters of tourists and 6 association rules, which contributes to the stimulus of the tourism industry in South Central after Covid-19.
To improve the reliability of data asset management, Expand the data resource scheduling capability, Research on data asset management system based on data application scenarios, Big data fusion and cluster analysis of asset management in different application scenarios, Combined with the method of correlation feature analysis, To ving the relevant features of asset management big data, Through the fuzzy integrated evolutionary cluster analysis method, Conduct decision data fusion of asset management big data; Analyze the asset attributes in different application scenarios, Under the big data fusion clustering conditions, Using the method of sample regression analysis, Establish the feature matching and adaptive scheduling model of asset management under different application scenarios, Realize the optimization and construction of the asset management system. The simulation results show that the method has high information fusion, good classification of asset management and strong asset resource scheduling ability.
In order to better solve the problems of industry cooperation, mutual trust and data sharing, more accurate detection and prevention of security problems hidden in the business data of the State Grid, to achieve cross-industry model sharing training and ecological construction. This research proposes a multi-party data sharing model training engine based on "blockchain + federated learning", which can be used in scenarios such as smart retail, risk assessment and satisfaction prediction to achieve multi-party privacy data sharing, build a data ecology, break data silos, and mine data. Joint value, enabling multi-party secure computing. After testing, it is found that compared with the traditional consensus mechanism, the information sharing encryption scheme proposed in the article is more complete and more efficient in encryption processing, and can be used as a shared information encryption processing tool.
With its concise and clear syntax and rich third-party library resources, python has achieved remarkable results in big data collection, analysis, preprocessing and visualization. This paper takes the stock funds in the market as a sample, through data analysis means such as data table inspection, data table cleaning, data preprocessing, data extraction, data screening and summary, and data visualization, and taking the National Planning as the background, carries out screening statistical analysis on the fund data from the Perspective of industry, and classifies the industry proportion, industry popularity ranking, fund quantity statistics The fund net value trend analysis is visually displayed in a more intuitive way, so as to provide practical reference value for further understanding the fund development.
Business performance has increased dramatically owing to the increase use of multichannel services in global markets posed by COVID-19 pandemic new normal. The viability of commercial airports depends on strong business models that integrates multichannel services such as digital services, cloud services and big data analytics services to its building blocks. This paper examines how multichannel services and big data analytics services can be used to optimize and enhance airport business building blocks to its business models to increase airports' revenue and business value. A case study was conducted to analyze PNG's airport authority's (National Airports Corporation (NAC) existing business models in comparison to the Osterwalder's business model building blocks. A sustainable business model was proposed that integrates digital and web-based technologies to boost the model's commercial and operational viability.
This paper examines how to drive digital services with big data analytics. More specifically, it addresses why big data analytics matters digital services and what is the state of the art of digital services. It reviews big data analytics and presents a business processing flow approach to big data analytics. It explores big data analytics services as a digital service and proposes a question-oriented approach to driving digital services with big data analytics. The proposed approach in this paper might facilitate the research and development of big data, business analytics, e-business, e-services, digital services, and digital-society.
With the rapid development of the superiority of people ’ s life and the advancement of science and technology in the new era, the health management in the health care system has also deepened. Since health and health have high social influence and public opinion attention, in this big data era, relevant research in the field of health and health can capture, store and share the information and data of non-healthy people together, providing more comprehensive data support for the health status of sub-healthy people. Based on the big data environment, this paper applies big data technology to collect open source data sets in the field of health and health from the Internet, and carries out the analysis of physical condition data, and designs and develops a health management system based on big data technology. The system can realize effective and convenient intelligent monitoring of personal health management data, which will help better real-time monitoring and nursing to adjust people ' s health status, and achieve the goal of all-round personal health management in the whole process.
In order to protect data privacy in the context of big data, a new scheme to protect data privacy and user privacy is proposed. This study believes that blockchain technology can be used to build a massive private data retrieval and sharing platform, considering the realization of privacy protection under horizontal federated learning and vertical federated learning, combined with privacy computing technology, to verify the results of massive data retrieval, and to build a "data availability that is not available." The "visible" security reduction model involves three cryptographic algorithms, namely functional encryption, zero-knowledge proof and asymmetric encryption. By combining blockchain and cloud servers, a hybrid storage architecture with data storage on the chain and off-chain storage is realized. And use function encryption and zero-knowledge proof to achieve privacy protection and secure data sharing of verifiable results. The final experimental results prove that the security feasibility of this model is proposed in this paper, which can fully improve the level of data log security management, operation and maintenance supervision, and the quality and efficiency of data security protection, and improve the network security protection system of the power grid in all scenarios.
For the test data generation issues of multipath coverage, a test data generation method based on super heuristic algorithm is proposed to solve the problem of poor universality of heuristic algorithm in different tested programs. Firstly, a hyper-heuristic algorithm model for test data generation based on multi-path coverage is established; Then, for the high-level strategy of the super heuristic algorithm, the random selection mechanism is adopted, and the low-level algorithm library adopts six algorithms formed by combining three different crossover operators and two mutation operators that designed in combination with the characteristics of the test data generation problem; Finally, two benchmark programs and two industrial programs are used to illustrate the effectiveness of the method.
Electric power data assets are important resources of electric power companies generated in the process of power generation. This article adopts the construction of an analytic hierarchy model as the evaluation system for the value evaluation of power data assets, simplifies the cost and application components of power data, and uses analytic hierarchy to calculate data asset index weights. Combined with the inherent relationship between the actual system construction investment cost and the data application, the analysis is based on the interval time domain generated by the data application, and finally the score evaluation of the data application investment interval on the value of the data asset is obtained, and then it is provided to assist in improving the overall data asset value Decision-making model.
Big data and big data technology have been revolutionizing our work, lives, and society. Socioeconomic development has become a hot topic for academia, industries, organizations, and governments. This paper will examine big data-driven socioeconomic development from a strategic perspective. More specifically, this paper explores the importance of big data by looking at the big characteristics (10 Bigs) of big data. This paper presents a GDDP (gross domestic data products) as a GDP-like data metric for measuring economic performance and social progress. It proposes a strategic model for big data-driven socioeconomic development, which is supported by big data-driven technologies, services, economies, and societies. This research demonstrates that big data-driven SMACS (i.e., social, mobile, analytics, cloud, and service) technologies, services, economies, and societies underpin the big data-driven socioeconomic development. The proposed approach in this paper might facilitate the research and development of big data, big data analytics, socioeconomic development, artificial intelligence (AI), and digital society.
Many states in the US have carried out lotteries with tantalizing prizes to reduce the covid-19 vaccine hesitancy. However, there has yet been a consensus regarding the effectiveness of such incentives. This study conducts a synthetic control analysis for each treated state, to provide a better understanding of the influence that the lottery programs have made on the vaccination rates across different states. However, for all treated states, no evidence is found for the effects of the lotteries. Next, the article investigates the impact of people's policy ideology on the prediction of the vaccination rates under the synthetic control models. Within each treated state, counties are categorized into two regions according to their political affiliation. The comparison of the treatment effects between the two regions indicates that there is no relationship between people's policy ideology and the vaccination rates.
Based on the in-depth discussion of the uncertain influencing factors of dangerous chemicals in the process of road transportation, the historical data of dangerous chemicals road transportation accidents are sorted out, and the variable consistency dominance rough set theory (VC-DRSA) is proposed to quantitatively analyze the importance of uncertain factors of environmental risk in dangerous chemicals transportation, Furthermore, it reveals the causal relationship between risk factors and risks and its irreducible rules, which provides a certain theoretical basis for alleviating and preventing the risk of road transportation of dangerous chemicals
This paper examines responsible big data analytics for e-business services and looks at how to use responsible big data analytics to obtain responsible e-business services. It addresses why responsibility matters to big data analytics and e-business services. It reviews big data analytics and looks at Google Analytics as a data processing flow-oriented analytics, and presents a data processing flow approach to big data analytics. This paper discusses responsible big data and big data analytics based on two case studies and examines big data analytics services as an e-business service and proposes two strategies on how to apply responsible big data analytics to obtain responsible e-business services. The proposed approach in this paper might facilitate the research and development of big data, business analytics, responsible big data analytics, e-business, e-services, and e-society.
In recent years, the incidence of breast cancer has increased year by year, posing a great threat to women's health. There are two types of breast cancer, non-invasive and invasive, and most patients get invasive breast cancer, which is fatal. Therefore, it's meaningful and necessary for breast cancer diagnosis at early stage. Breast lumps are the most common manifestations and they are detectable on different formats of medical images. However, traditional human-based diagnosis and image analysis place high demands on doctors’ professional skills and energy. Fortunately, with the development of artificial intelligence technology, computer-aided diagnosis (CAD) is gradually replacing the traditional mammogram diagnosis and analysis. In this study, we developed a non-invasive CAD system on the digital mammograms from the Minimammographic dataset by using discrete wavelet transform (DWT) and higher order gradient (HoG) image decomposition methods for features extraction, and principle component analysis (PCA) for dimensionality reduction. Then we compared the classification effects of traditional machine learning algorithms and modern deep learning based algorithms on the decomposited images. The traditional supported vector machine (SVM) achieved an accuracy of 79.66% and the modern convolutional neuron network (CNN) achieved an accuracy of 83.05%. They showed a slight difference. Considering the computational cost of the CAD system, the traditional SVM seems to be more suitable. In addition, we also tried to use a breast lump index (BLI) to quantify the patients’ diagnosis results.
In recent years, predicting sports results has become increasingly popular. Basketball is one of the most popular sports in the world, especially in USA, the NCAA game attract lot of fans around over 1000 colleges. In this paper, a new machine learning framework for predicting NCAA game results is proposed in order to find the features that affects NBA game results, and obtain the best model to predict the result of NCAA game. We wanted to determine whether the machine learning approach was applicable to predicting the result of NCAA game and what were the important factors that affected the result of the game. We proposed to use different machine learning models to predict the winner. Specifically, different classification models such as decision tree, random forest, LDA, QDA, SVM, Naïve bayes were used to predict the result. Then for each model, 10-fold cross validation was used to check the mean accuracies of each model and obtain the highest accuracy. By comparing the accuracies and performances of each model, we can obtain the best model for prediction and know which features affect the result of NCAA regular season and tourney season. For regular season, the LDA, QDA, random forest and SVM models show good performance and the accuracy is about 99%. For tourney season, LDA and SVM should be good models to do the prediction and the accuracy is about 90%. In general, LDA, random forest and SVM could be the convinced model in this case to predict the winner of NCAA basketball game, and there are some possibilities for improvement. Pruning the tree, different type of kernel of SVM such as linear, radial basis function, polynomial, and more classification models will be used in the future to predict the winner of NCAA games.
