WiMi Hologram Cloud a leading global Hologram Augmented Reality (“AR”) Technology provider announced that its R&D team developed big data real-time computing engine components joint computing system for how to utilize data efficiently and give full play to data performance.
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Big data real-time computing engine component federated computing system is a system for obtaining valuable information and insights through real-time processing and computation of big data. In this system, various data sources can be integrated into a single data platform for operations such as data collection, normalization and cleansing. Then, the processed data is fed into a real-time computing engine for complex calculations and analysis to produce more accurate results, and, ultimately, the results are presented to users to help them better understand the data.
WiMi’s big data real-time computing engine components federated computing system mainly includes core modules such as data acquisition and cleaning, real-time computing engine, algorithm library, data visualization, etc. These core technology modules cooperate to form a complete federated computing system, which provides users with highly efficient, accurate and convenient data analysis services.
Data acquisition and cleaning module: this module is responsible for collecting data from various data sources and cleaning and pre-processing the data to ensure the accuracy and completeness of the data for subsequent computational processing.
Real-time computing engine module: this module is the core part of the system, which supports real-time data processing and analysis in multiple computing modes and high concurrency scenarios, greatly improves the efficiency of data utilization, and can quickly respond to user requests and provide customized computing services.
Algorithm database module: this module includes a variety of data analysis algorithms and models, which can automatically identify patterns and laws in the data, deeply analyze and mine real-time data, and provide users with more accurate analysis results.
Data visualization module: this module presents the analysis results to the user in the form of charts, reports, etc., so that the user can understand the data analysis results more intuitively and help the user understand the data better.
High availability architecture module: this module adopts the design of high availability architecture, which can greatly improve the reliability and stability of the system and ensure the long-term stable operation of the system.
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The big data real-time computing engine component federated computing system greatly improves the efficiency of data utilization and can provide more accurate and comprehensive data support for decision-makers in various fields, and it has been widely applied in many fields such as finance and healthcare. For example, in the financial field, it can help decision-makers better grasp the direction of the market through the analysis of market data; in the medical field, it can assist doctors in diagnosis and treatment through the analysis of patient data; in the logistics field, it can improve the distribution efficiency and reduce costs through the analysis of transportation data.
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