Multi-source Heterogeneous Data Integration Research Articles

A Collaborative Emergency Drill System for Urban Tunnels Using BIM and an Agent-Based Model

With the rapid development of smart cities, the refined management of urban highway tunnels has put forward higher requirements for the emergency disposal ability of operation and maintenance personnel. This paper proposed a collaborative emergency drill system for urban tunnels using building information modeling (BIM) and an agent-based model. The objectives of this paper are as follows: (1) To help address the challenge of multi-person collaborative intelligent drills in complex emergency scenarios, this system constructed an emergency collaborative drill model and a virtual emergency scenario description method based on trait-based objects (TBOs). (2) To help address the challenge of the organization and integration of multi-source heterogeneous data in complex emergency scenarios, the system established an emergency scenario generation method through lightweight BIM data, standard emergency plan documents, and virtual emergency scenario data. The system was successfully applied to the Hongmei South Road Tunnel in Shanghai, China. The feasibility of the proposed system provided practical help for tunnel emergency management and was extended to other urban tunnels in Shanghai.

Circular Economy Model for Elderly Tourism Operation Based on Multi-source Heterogeneous Data Integration

ABSTRACT The emerging senior tourism industry has attracted increasing attention in China due to the continuous development of social, economic and technological factors. However, the senior tourism market in China still faces significant shortages, and further market development is needed. To address this issue, a senior tourism business model based on the circular economy, artificial intelligence (AI) technology and multi-source heterogeneous data integration has been proposed in this study. The model uses AI technology to integrate and analyze multi-source heterogeneous data, providing insights into the senior tourism market and facilitating precise marketing. Additionally, the model combines senior tourism products with sustainable development by adopting a circular economy model, achieving both economic and environmental benefits. The application of this model is expected to enhance the competitiveness and sustainable development of the Chinese senior tourism market, providing new ideas and references for the development of the industry. The integration of multi-source heterogeneous data is a significant feature of this model, which facilitates a more in-depth understanding of the senior tourism market. By analyzing various data sources, such as social media, search engines, and online reviews, the model can better identify senior tourists’ preferences and behaviors, thereby improving the precision of marketing strategies. Furthermore, by integrating environmental data, such as carbon footprint and energy consumption, the model can assess the sustainability of senior tourism products, contributing to the development of eco-friendly senior tourism.

Big Data Health Care Platform With Multisource Heterogeneous Data Integration and Massive High-Dimensional Data Governance for Large Hospitals: Design, Development, and Application.

BackgroundWith the advent of data-intensive science, a full integration of big data science and health care will bring a cross-field revolution to the medical community in China. The concept big data represents not only a technology but also a resource and a method. Big data are regarded as an important strategic resource both at the national level and at the medical institutional level, thus great importance has been attached to the construction of a big data platform for health care.ObjectiveWe aimed to develop and implement a big data platform for a large hospital, to overcome difficulties in integrating, calculating, storing, and governing multisource heterogeneous data in a standardized way, as well as to ensure health care data security.MethodsThe project to build a big data platform at West China Hospital of Sichuan University was launched in 2017. The West China Hospital of Sichuan University big data platform has extracted, integrated, and governed data from different departments and sections of the hospital since January 2008. A master–slave mode was implemented to realize the real-time integration of multisource heterogeneous massive data, and an environment that separates heterogeneous characteristic data storage and calculation processes was built. A business-based metadata model was improved for data quality control, and a standardized health care data governance system and scientific closed-loop data security ecology were established.ResultsAfter 3 years of design, development, and testing, the West China Hospital of Sichuan University big data platform was formally brought online in November 2020. It has formed a massive multidimensional data resource database, with more than 12.49 million patients, 75.67 million visits, and 8475 data variables. Along with hospital operations data, newly generated data are entered into the platform in real time. Since its launch, the platform has supported more than 20 major projects and provided data service, storage, and computing power support to many scientific teams, facilitating a shift in the data support model—from conventional manual extraction to self-service retrieval (which has reached 8561 retrievals per month).ConclusionsThe platform can combine operation systems data from all departments and sections in a hospital to form a massive high-dimensional high-quality health care database that allows electronic medical records to be used effectively and taps into the value of data to fully support clinical services, scientific research, and operations management. The West China Hospital of Sichuan University big data platform can successfully generate multisource heterogeneous data storage and computing power. By effectively governing massive multidimensional data gathered from multiple sources, the West China Hospital of Sichuan University big data platform provides highly available data assets and thus has a high application value in the health care field. The West China Hospital of Sichuan University big data platform facilitates simpler and more efficient utilization of electronic medical record data for real-world research.

Research and application of cluster distributed generation virtual power plant regulation mode based on multi-source heterogeneous data flow analysis

Abstract In view of the increasing demand for multi-source heterogeneous data interaction in cluster distributed power virtual power plant(VPP), this paper analyzes the data sources and data flows in each interactive system, and studies the interface strategy between these heterogeneous data and VPP; On this basis, a multi-source heterogeneous data integration platform is built. Based on the data platform, a two-level coordination mode of intelligent regulation of VPP is proposed and realized; Finally, a practical demonstration project is carried out to verify the feasibility of the relevant schemes.

Multi-Objective Spatial Suitability Evaluation and Conflict Optimization Considering Productivity, Sustainability, and Livability in Southwestern Mountainous Areas of China

Space is the fundamental carrier for production, living, and ecological activities, and optimizing the spatial pattern is of vital importance to promote regional sustainable development. To achieve this goal, the core issues are to identify the risks of resource and environmental constraints of development and to realize the rational distribution of human living space. Based on the integration of multisource heterogeneous data, taking Yunnan Province, a typical mountainous area in China, as an example, this research proposes a multi-object suitability evaluation method based on 50 × 50 m grid data at the provincial scale. We build a spatial conflict analysis model to identify production–living–ecological space (PLES) and propose governance suggestions for different functional areas. The results show that (1) areas suitable for ecology make up the greatest proportion of Yunnan Province, but areas with living and ecological functions show obvious spatial complementarity; (2) areas suitable for production are restricted by steep slope, geological hazards and fragmented pattern; (3) areas suitable for living is rare, and they are mainly concentrated in the plains of central Yunnan; and (4) twenty-seven percent of area has potential spatial conflicts, among which 4.38% of the area is all suitable for production–living–ecological. The production–living advantage areas are concentrated in the central Yunnan UA (Urban agglomeration), which has a high spatial overlap. These results are expected to provide valuable insights to support comprehensive multifunctional spatial utilization and sustainable development in mountainous areas.

Two-stage adaptive integration of multi-source heterogeneous data based on an improved random subspace and prediction of default risk of microcredit

Some scholars have shown that the machine learning methods based on a single-source data can successfully monitor the risks of formal financial activities, but not those of informal financial activities. This is because the data generated by formal financial activities, whether it is the structured or unstructured data, are of high quality and quantity, while the data generated by informal financial activities are not. Therefore, multi-source data are the key to monitor the risks of informal financial activities through machine learning. Although a few studies attempted to use multi-source data for financial risk prediction, they simply stack the obtained multi-source data, but ignore the original sources, heterogeneity, mutual redundancy and other characteristics of the data, so that the improvement of the prediction effect is not obvious. Therefore, TSAIB_RS method based on the two-stage adaptive integration of multi-source heterogeneous data was constructed in the paper, in which the data with different sources and different distributions were adaptively integrated. In order to test the reliability of TSAIB_RS method, the paper takes the default risk of microcredit in China as the test target and compares the prediction results of various test methods. It concludes that TSAIB_RS method can significantly improve the prediction effects.

Design and implementation of planting information service system based on GIS

The information service of planting features various service content; scattered and isolated information; diversity of data source and format, as well as high cost of system operation. Since agricultural production decision-making and management have diversified demands on scientific and technological service, a GIS-based planting information service system was designed and developed. This system integrates the geographic information data and planting service thematic data, which is based on multi-source heterogeneous data integration, GIS spatial analysis, responsive framework and other technologies. Through various terminals such as computers and mobile phones, the system provides land circulation information inquiry, soil fertility analysis, crop variety selection, production materials purchase, agricultural technology guidance, labor employment recruitment, agricultural product sales and other comprehensive information services of the whole planting life cycle. It guides farmers to plant rationally, and helps farmers resolve the issues relating to information islands and the information shortage problem in production and life by “one-stop”, and increase the famers’ income. It can also provide decision-making gist for agricultural administrative departments to adjust planting structure, optimize the rural industrial layout and related policy formulation. Through demonstration in Zhucheng, a new model for serving “agriculture, rural areas and farmers” with surveying and mapping geographic information has been formed in terms of resource integration, geospatial data services, and service information access.

Research on Universal Processing Framework of Multi-source Heterogeneous Data Integration in GIS Domain Based on Software Technology

It is difficult to make fine-grained random call in GIS system development or data processing owing to the multi-source heterogeneity of spatial data. Moreover, to convert the data into a unified format easily can lead to the loss of the core attributes. In this paper, we discuss the translation methods among several different data formats, including attribute and business data , network data, CAD data, heterogeneous GIS spatial data, and domestic and foreign GIS data, etc, based on software technology like FME, MyFME, Arcgis, MessagePack package, crawling tools and so on. Then, we propose a universal processing framework for multi-source heterogeneous data integration in the GIS field, and verify the performance of the proposed framework through integrating several kinds of data.