Big Data Enterprises Research Articles

Enterprise ecological niche measurement and competitive effect analysis based on digital business recognition

ABSTRACT Previous studies have emphasised the positive role of digital technologies in gaining competitive advantage, but the differences between technologies and the role of environmental matching have been overlooked. In this paper, we explore the relative positions and competitive landscape formed by digital technology firms. First, the study uses an NLP approach to measure corporate ecological niche based on digital business, which includes two indicators of width and overlap, and the original corpus comes from the annual reports of 409 listed companies. Then, based on the ecological niche separation theory, we analyse the competitive effects. The results show that the competitive effect formed by the representative digital technology enterprises can be divided into complementary, independent and substitution. Among them, cloud computing and big data enterprises are at the stage of complementary effect, blockchain enterprises form a unique independent competitive pattern, while the substitution effect of artificial intelligence and Internet of Things enterprises is more obvious. This paper implements an ecological niche measure that embodies digital features, which delivers fresh and useful insights into the application of the ecological niche theory in the digital environments and further reveals the unique competitive dynamics in the digital environment.

Nested Dynamical Modeling of Neural Systems: A strategy and some consequences

Neuroscience has become a big data enterprise. This is due in large part to the rapidly growing quantity and quality of data and increased appreciation of non-neuronal physiology and environments in explaining behavior, cognition, and consciousness. One way neuroscience is dealing with this embarrassment of riches is by appealing to investigative frameworks that put the multiscale nature of neural systems at the forefront. The current work offers one such approach: Nested dynamical modeling, a strategy for creating models of phenomena comprised of multiple spatial and/or temporal scales for purposes of exploration, explanation, and understanding. Building from dynamical systems theory and synergetics, nested dynamical modeling applies a methodological approach aimed at nesting models at one scale of inquiry within models at other scales without compromising biological realism. This strategy is demonstrated via a proof of concept. Some consequences this approach has for the epistemological and theoretical commitments of neuroscience are discussed.

The Impact of Ambidextrous Innovation Human Capital on the Technological Innovation Efficiency and Stage Efficiency of Big Data Enterprises

Based on a resource-based view, we explore the relationship between ambidextrous innovation human capital and the efficiency of technological innovation and its stage efficiency in big data enterprises. We divide their technological innovation into two stages: technological development and the commercialization of technological achievements. For a more reasonable efficiency measurement, we construct an efficiency evaluation index system based on the entropy value method (EVM) and the analytical hierarchy process (AHP), and we measure and evaluate their technological innovation efficiency from both static and dynamic perspectives. We conduct an empirical analysis with a sample of Chinese big data listed companies from 2014–2017. After regression analysis and its robustness test, we find that ambidextrous innovation human capital has a significant impact on the technological innovation efficiency of big data enterprises, that incremental innovation human capital and radical innovation human capital have opposite impact relationships in the technological development stage and that radical innovation human capital may have a more significant economic significance on the efficiency of commercializing technological achievements of big data enterprises. In the course of our analysis, we also find some other relevant findings. Finally, we formulate some relevant management suggestions.

Financial Technology, Big Data Enterprise Financing Constraints and Big Data Industry Development: Empirical Analysis Based on Mediating Effect and Threshold Effect

Based on theoretical analysis, we select the relevant data of 30 provinces (autonomous regions and municipalities) in China from 2013 to 2019, and empirically test the impact of financial technology on the development of big data industry and its mechanism using dynamic panel data model, mediating effect test method and threshold effect model. The benchmark regression results show that the regression coefficient of financial technology to big data industry is significantly positive at the significance level of 10%, indicating that the financial technology can directly promote the development of big data industry. The regression coefficient of the dynamic lag term of big data industry is negative, but not significant, indicating that the dynamic lag effect of big data industry is not obvious. The mediating effect test results show that the financial technology can indirectly promote the development of big data industry by alleviating the big data enterprise financing constraints. The big data enterprise financing constraints have a partial mediating effect, and the mediating effect account for 27.63% of the total effect. In addition, the threshold effect test results show that the direct effect of financial technology on big data industry is significantly enhanced when the development level of financial technology is higher than 5.8790, that is, there is a positive threshold effect of financial technology directly promoting the development of big data industry. However, the indirect effect of financial technology on big data industry is relatively weak when the development level of financial technology is higher than 5.4328, that is, financial technology indirectly promotes the development of big data industry by alleviating the big data enterprise financing constraints, which has a negative threshold effect.

RETRACTED ARTICLE: Rainfall weather condition prediction based on embedded system and big data financial management

This paper analyzes the practical application requirements of embedded systems. According to the analysis, we conceive and develop a general experimental platform composed of embedded processor motherboard, general peripheral equipment, and custom connection board. We completed the selection and design of hardware peripherals, driver creation, and testing and showed how to transplant the basic driver for each module. Then embedded system is used in rainfall prediction, which plays an important role. The distribution of four different types of representative stations is analyzed, and the probability characteristics of different precipitation forecast levels are summarized. Secondly, from the point of view of time and space, we will study the total precipitation and flood index during the Meiyu period in Northwest China, and understand their spatial and temporal characteristics. Finally, the precipitation in the coming Meiyu period is predicted by R/S analysis and stationary time series prediction method. However, in the era of big data, the competition is becoming more and more fierce. For the financial industry, financial risk is an inevitable problem of financial development, especially in the competitive market environment; the abnormal competition behavior of some financial companies is destroying the financial market. Financial management is an important task for financial institutions. Only with good performance in risk management can big data be better applied to and stimulate the financial industry. In this paper, through the establishment of risk management mechanism, the comprehensive analysis of big data enterprises, the identification of risk factors of big data enterprises, the development of preventive strategies, and other measures, we can effectively reduce the risk loss and promote the stable development of big data financial management.

Research on the Application and Development of Big Data Technology in China’s Computer Industry under the Going-out Strategy

Big data industry should give appropriate support and incentive policies to encourage it to strengthen the research and development of big data core technologies and effectively improve the practical application capabilities of big data. Considering the development of big data enterprises, it must be based on the support of advanced technology. Of course, as an enterprise, the operating cost is also one of the key considerations. In this regard, in order to promote the profit growth of enterprises, the government must increase the research and development of special technologies for big data and actively promote the diffusion of big data technology within and among industries, in order to promote the dividend of big data industry. Released as soon as possible, this way grasped the convergence of big data industry and traditional industry and then speed up the transformation of scientific and technological achievements.

Evaluation on Technology Innovation Efficiency of Big Data Enterprises Based on DEA

Discussing the technological innovation efficiency of big data enterprises and carrying out in-depth research on big data enterprises will help to accurately evaluate the development status of big data industry and guide the development of big data industry. This paper uses DEA-BCC model to evaluate the technological innovation efficiency of 21 big data enterprises. It is found that the technological innovation efficiency of big data enterprises is better. The average values of technological efficiency, pure technological efficiency and scale efficiency are 0.587, 0.772 and 0.750, respectively. The technological efficiency of big data enterprises is lower, and the individual differences are larger. At the same time, the research shows that there are 15 big data enterprises need to further expand the scale, and then improve production efficiency.

Spatial Analysis of Big Data Industrial Agglomeration and Development in China

Nowadays, our daily life constantly creates and needs to utilize tremendous amounts of datasets. Fortunately, the technologies of the internet, both in software and hardware, have the capability to transmit, store, and operate big data. With China being the most populous country in the world, developing the big data industry is, therefore, seen as an urgent task. As generating industrial agglomeration is important for forming a mature industry, this study aims to characterize the phenomenon of big data industrial agglomeration in China, and to identify the factors for developing the big data industry using spatial analysis approaches and GIS technology from a geographer’s perspective. The problems and strengths of these representative cities are discussed, from which the solutions and the possible directions for the future are also provided. The findings argued that China is still at the primary stage of the development in the big data industry. Only several cities had the presence of a strong agglomeration, but the intercity space spillover was weak. However, comparing the changes in industry distribution, the trend of agglomeration have appeared, and the benefits of industrial agglomeration have also worked. The principal factors of the big data industry and its agglomeration include the support of government and the outstanding higher education agglomeration. In addition, it was also noted that each city has its own characteristics and potentials to attract more big data enterprises, talent, and investment.

Conversations and connections: improving real-time health data on behalf of public interest

Real time data and big data analytics is at the forefront and future of healthcare. Current trends indicate a growing interest and commitment to building real time data applications and big data enterprise. This movement must be accompanied by support, commitment and thorough collaboration. Data that proves valuable must drive program implementation, scaled initiatives and effective research. Predictive modeling for surveillance, public health and clinical use must prove cost effective as well. Support and advancement for this must be embraced by all in healthcare, with initial use and incorporation into everyday applications. Additionally, global public health can be aided and should embrace real time data system development, for transparency, predictive analytics and robust surveillance. When aggregated and applied reliably, big data can benefit all stakeholders and the general public. This opportunity should be enhanced, realized and cultivated.

Research on the influencing factors of financing efficiency of big data industry based on panel data model--Empirical evidence from Guizhou province

This paper mainly studies the influence factors of financing efficiency of Guizhou big data industry, and selects the financial and macro data of 20 Guizhou big data enterprises from 2010 to 2016. Using the DEA model to obtain the financing efficiency of Guizhou big data enterprises. A panel data model is constructed to select the six macro and micro influencing factors for panel data analysis. The results show that the external economic environment, the turnover rate of the total assets of the enterprises, the increase of operating income, the increase of the revenue per share of each share of the business income have positive impact on the financing efficiency of of the big data industry in Guizhou. The key to improve the financing efficiency of Guizhou big data enterprises is to improve.

A Two-Way Street: Green Big Data Processing for a Greener Smart Grid

Integrating renewables into the mainstream energy market is pivotal for the green revolution promised by the smart grid. The real power behind realization of the smart grid goals lies in the volume, variety, velocity of the big data generated by a variety of sources. Nevertheless, the smart grid needs data centers to digest the big data for its profound green revolution. However, big data processing is the radix for data centers to be seen as energy black holes. Unless data centers are transformed into energy-efficient enterprises, big data are going to be responsible for superfluous energy burn, potentially reversing the smart grid genesis with regard to green environmental impact. This paper describes the role of the big data enterprise in envisioning the smart grid. We dissect the big data enterprise into six vital planes impacting the energy footprints of data centers. We present a survey of key strategies to make these six vital planes greener. Moreover, we present open challenges and directions in this context. We assert that a cross-plane approach toward a greener optimization is crucial. In this vein, we present a green orchestrator that is capable of incorporating different planes in an integrated fashion to boost energy profile of the big data enterprise.

The U.S. Army Person-Event Data Environment: A Military-Civilian Big Data Enterprise.

This report describes a groundbreaking military-civilian collaboration that benefits from an Army and Department of Defense (DoD) big data business intelligence platform called the Person-Event Data Environment (PDE). The PDE is a consolidated data repository that contains unclassified but sensitive manpower, training, financial, health, and medical records covering U.S. Army personnel (Active Duty, Reserve, and National Guard), civilian contractors, and military dependents. These unique data assets provide a veridical timeline capturing each soldier's military experience from entry to separation from the armed forces. The PDE was designed to afford unprecedented cost-efficiencies by bringing researchers and military scientists to a single computerized repository rather than porting vast data resources to individual laboratories. With funding from the Robert Wood Johnson Foundation, researchers from the University of Pennsylvania Positive Psychology Center joined forces with the U.S. Army Research Facilitation Laboratory, forming the scientific backbone of the military-civilian collaboration. This unparalleled opportunity was necessitated by a growing need to learn more about relations between psychological and health assets and health outcomes, including healthcare utilization and costs-issues of major importance for both military and civilian population health. The PDE represents more than 100 times the population size and many times the number of linked variables covered by the nation's leading sources of population health data (e.g., the National Health and Nutrition Examination Survey). Following extensive Army vetting procedures, civilian researchers can mine the PDE's trove of information using a suite of statistical packages made available in a Citrix Virtual Desktop. A SharePoint collaboration and governance management environment ensures user compliance with federal and DoD regulations concerning human subjects' protections and also provides a secure portal for multisite collaborations. Taking similarities and differences between military and civilian populations into account, PDE studies can provide much more detailed insight into health-related questions of broad societal concern. Finding ways to make the rich repository of digitized information in the PDE available through military-civilian collaboration can help solve critical medical and behavioral issues affecting the health and well-being of our nations' military and civilian populations.