Analysis of the Correlation between Emerging Industry Development and University Students’ Entrepreneurship Based on Big Data

With the strong support of local governments for strategic emerging industries such as high-end equipment manufacturing, new materials, and new energy, strategic emerging industries are playing an increasingly important role in the economy and society. With the increasing enthusiasm of college graduates for independent entrepreneurship, college students’ entrepreneurship is constantly integrated with the development of strategic emerging industries. Based on this background, aiming at the practical problems of the development of strategic emerging industries, this study innovatively puts forward the method of using big data technology and GM model to realize the dynamic model analysis of the development of strategic emerging industries and college students’ entrepreneurial behavior. This article analyzes the correlation between dynamic big data such as industrial scale, industrial market, and industrial direction of local strategic emerging industries and university entrepreneurship, so as to provide theoretical support for the development strategy of strategic emerging industries. Through the neural network algorithm, this article evaluates the entrepreneurship of college students, so as to provide a digital basis for the layout of strategic emerging industries to attract talents and entrepreneurship. Experiments show that the big data integration system established by GM correlation analysis and ant colony Elman regression artificial neural network has high accuracy and can well identify the priority relevance of the industrial direction of strategic emerging industries to college students’ entrepreneurship. It provides theoretical support for regional policy makers to better formulate college students’ entrepreneurship strategy and the development plan of emerging industries.

DATA GOVERNANCE MECHANISMS IN INDONESIA’S COVID-19 DATA INTEGRATION SYSTEM

During a pandemic, policy decisions are made quickly and correctly. The need for COVID-19 data becomes an absolute basis for policymaking. This paper focuses on the mechanism of COVID-19 data management in the national COVID-19 task forces to be able to provide valid and realtime data. Researchers used qualitative analysis, with primary and secondary data collection. Researchers interview 3 information from the ministry of communication and informatics as a public communication team in the task force to accelerate the handling of COVID-19. The results showed that even data management was based on structural, procedural, and relational mechanisms. Structure mechanism has been formed strongly through the task force team from the national to the regions. Procedural data mechanisms, although changing procedures are now at the point of data integration. The relation mechanism shows that the coordination and communication relationship between member task forces has been done quite well where coordination is always done quickly.

A Data Integration Architecture for Smart Cities

The data generated by smart cities have low integration, as the systems that produce them are usually closed and developed for specific needs. Moreover, the large volume of data, and the semantic and structural changes in datasets over time make the use of data to support decision-making even more difficult. In this work, we identify the main requirements of a data integration system to support decision-making in cities, focusing on its challenges. We analyze some existing data integration solutions, to uncover their features and limitations. Based on these results, we propose a new microservice architecture to support the development of software platforms for integrating smart cities’ heterogeneous data and a guideline to assess their performance.

Design of Multimedia Vocal Music Education Data Integration System Based on Adaptive Genetic Algorithm

In order to solve the problems of low accuracy of data integration results, low integration efficiency, and easy confusion between different types of data in traditional methods, a multimedia vocal education data integration system based on adaptive genetic algorithm was designed. Specifically, the designed system is divided into three parts: data source management module, system administrator module, and database management module. The synchronized multimedia vocal education data are first processed by the synchronous multimedia vocal education data processing and then integrated by an adaptive genetic algorithm. The experimental results show that the longest data transmission time of the system is 2.3 s, which is much lower than that of the traditional method, and the accuracy of the integration result is higher, and the probability of data integration confusion is lower, which all indicate that the designed system has better application performance.

APPLE AND PEAR SCAB ONTOLOGY

An important issue in horticulture is ensuring plant disease, such as scab, prevention and treatment. Apple and pear are among the most widely grown (approximately 43% of all fruit tree area [1]) and economically important fruit crops specified worldwide and in Latvia. Scab diseases caused by ascomycetous fungi Venturia inaequalis and V.pyrina are economically the most important diseases worldwide. Research projects have produced research data covering various aspects of plant-pathogen interactions, but there is no internal linkage analysis, as well as implementation of other types of data (such as environmental and meteorological data, etc.). Establishing such a data integration system would allow the identification of new regularities in plant-pathogen interactions, and provide mechanisms for disease control decisions. Semantic analysis is one of information technology approaches to finding relationships in data. The product of analysis is ontology. There are plant disease ontologies which provide classification of diseases and describe their reasons. However, there is no ontology which describes a specific plant and relations among its farming parameters and disease probability. Such an ontology for apple and pear scab is presented in this paper. The constructed ontology can be applied to develop guidelines or digital expert systems.

Corpus translation system based on Web Services and its application

In order to construct an efficient translation system, this paper constructs a corpus translation system based on Web Services. Moreover, this paper builds a network term detection system based on machine learning algorithms, expands the corpus data with the support of the crawler system, and uses WEB retrieval translation technology. At the same time, in response to the problem of sentence length changes in English abstracts, this paper proposes a method to obtain standard sentence length changes based on edit distance and SVM sorting. Based on requirements, this paper designs the architecture and data integration process of the data integration system. In addition, this paper outlines the detailed design and implementation process of each module of the system, and proposes a system performance optimization plan, and combines translation requirements to construct a corpus translation system based on Web Services. Finally, this paper designs experiments to verify the performance of the model. The research results show that the system constructed in this paper has a good application effect.

Multi-Data Integration System to Capture Detailed Strong Ground Motion in the Tokyo Metropolitan Area

To accurately capture ground motion in the Tokyo metropolitan area, we have developed a multi-data integration system that combines a large amount of ground motion data gathered from nationwide strong-motion seismograph networks (K-NET and KiK-net); Metropolitan Seismic Observation network (MeSO-net), which covers the Tokyo metropolitan area with a high density of about 300 observation stations; observation equipment held by private companies; and smartphone-based seismographs. K-NET, KiK-net, and MeSO-net are operated by National Research Institute for Earth Science and Disaster Resilience. The seismic waveform data recorded by MeSO-net, which are based on borehole observations, are one of the most important data sets for this system. To ensure collection of the waveform data, we strengthened the data center functions and made the collected data available to the public. In addition, to estimate the ground motion at the surface, which is important for disaster prevention, from the waveform data of MeSO-net, we carried out temporary seismic and microtremor array observations on the ground surface at each MeSO-net borehole station, and estimated ground amplification characteristics and the S-wave velocity structure. We also developed a smartphone-based seismograph with the aim of realizing seismic observations for tens of thousands of sites in the future. We recruited monitors to deploy the smartphone seismometers in the Tokyo metropolitan area, and developed a function to notify monitors of the results of a rough evaluation of the soundness of buildings based on observation data acquired during an earthquake. Furthermore, we have developed a Tokyo metropolitan area version of Kyoshin Monitor, the strong motion monitor system, with which the current ground motion in the Tokyo metropolitan area can be captured in real time by integrating and visualizing observation data from K-NET, KiK-net, and MeSO-net on a map on the website. We can capture the propagation of the ground motion in detail directly from the high-density data set integrated from these three networks. In addition, we also integrated data from Super-Dense Real-time Monitoring of Earthquakes (SUPREME) network of Tokyo Gas Co., Ltd., which operates about 4,000 observation stations in the Tokyo metropolitan area, after applying a time correction. We verified the integration method by reproducing the ground motion in the Tokyo metropolitan area during the 2011 Tohoku earthquake. The study findings have made it clear that the ground motion in the Tokyo metropolitan area can be captured in more detail by the integration of data produced by the public and private sectors.

Sepsis prediction via the clinical data integration system in the ICU

Sepsis is an essential issue in critical care medicine, and early detection and intervention are key for survival. We established the sepsis early warning system based on a data integration platform that can be implemented in ICU. The sepsis early warning module can detect the onset of sepsis 5 hours proceeding, and the data integration platform integrates, standardizes, and stores information from different medical devices, making the inference of the early warning module possible. Our best early warning model got an AUC of 0.9833 in the task of detect sepsis in 4 hours proceeding on the open-source database. Our data integration platform has already been operational in a hospital for months.

Orchestrating privacy-protected big data analyses of data from different resources with R and DataSHIELD

Combined analysis of multiple, large datasets is a common objective in the health- and biosciences. Existing methods tend to require researchers to physically bring data together in one place or follow an analysis plan and share results. Developed over the last 10 years, the DataSHIELD platform is a collection of R packages that reduce the challenges of these methods. These include ethico-legal constraints which limit researchers’ ability to physically bring data together and the analytical inflexibility associated with conventional approaches to sharing results. The key feature of DataSHIELD is that data from research studies stay on a server at each of the institutions that are responsible for the data. Each institution has control over who can access their data. The platform allows an analyst to pass commands to each server and the analyst receives results that do not disclose the individual-level data of any study participants. DataSHIELD uses Opal which is a data integration system used by epidemiological studies and developed by the OBiBa open source project in the domain of bioinformatics. However, until now the analysis of big data with DataSHIELD has been limited by the storage formats available in Opal and the analysis capabilities available in the DataSHIELD R packages. We present a new architecture (“resources”) for DataSHIELD and Opal to allow large, complex datasets to be used at their original location, in their original format and with external computing facilities. We provide some real big data analysis examples in genomics and geospatial projects. For genomic data analyses, we also illustrate how to extend the resources concept to address specific big data infrastructures such as GA4GH or EGA, and make use of shell commands. Our new infrastructure will help researchers to perform data analyses in a privacy-protected way from existing data sharing initiatives or projects. To help researchers use this framework, we describe selected packages and present an online book (https://isglobal-brge.github.io/resource_bookdown).