WiMi Hologram Cloud Inc. (NASDAQ: WIMI) (“WiMi” or the “Company”), a leading global Hologram Augmented Reality (“AR”) Technology provider, today announced the development of a data interaction system that integrates data mining and neural network topology visualization. The system meets the requirements of real-time data interaction, realizes flexible configuration of data interaction, and can effectively solve the problem of “information silos.” In addition, it achieves safe and reliable information transmission by using encryption and redundant checksum technology to ensure the integrity, accuracy, reliability, and security of the data interaction process.
Data mining is a trending topic in artificial intelligence and database research. It is the integration of database and artificial intelligence technology. The so-called data mining refers to the process of mining valuable and helpful information from a large amount of data in a database and searching for hidden information from a large amount of data through algorithms. Data mining has developed a set of mining models covering association, classification, clustering, etc. The whole process of data mining is as follows:
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Data cleaning: The raw data used for knowledge discovery in practical applications usually must be completed. Except for special applications such as outlier analysis, noise should be eliminated, inconsistent data should be removed, anomalous and erroneous values should be corrected, and uncertain or incomplete values should be completed.
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Data integration: Combining data from multiple sources and different forms into one data.
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Data Selection: Extracts and analyzes task-relevant data from the database.
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Data Transformation: Converting and unifying data into a form suitable for mining through aggregation operations.
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Data mining: As the most important step in the whole process, the core operation uses automatic and intelligent methods to extract data patterns.
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Pattern evaluation: Filtering patterns of knowledge based on specific metrics of interest.
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Knowledge representation: Using visualization and knowledge representation techniques to display the mined knowledge to the user.
WiMi uses neural networks for data classification and analysis in data mining. Data analysis using neural networks has the following advantages: first, it is noise tolerant; second, it provides high accuracy for complex nonlinear mappings; third, it can be implemented on parallel hardware and is highly maintainable; and fourth, it can be easily updated with new data and can be easily automated.
Currently, neural networks have been widely used in image recognition, segmentation, speech recognition, and other fields. With an analogy to human brain information transfer, the training method can fundamentally change the network structure and get better training performance. Neural network topology visualization presents the connection relationship of network nodes as graphical images composed of points and lines, etc. This can clearly and intuitively reflect the network operation, assist people in evaluating, predicting, and analyzing various aspects of the network nodes and links, and effectively recognize and understand the information, patterns, and changes within the network.
Users can observe and analyze the drawing results by extracting network topology features and performing geometric mapping to complete visual reception. For time-varying data, it can show the time-varying evolution process of network structure through animation simulation and other expressions, thus helping users to think and summarize and build a basic understanding of network data timeliness. Through continuous iterative, interactive feedback, the system optimizes the plotting results and, with the help of other hardware auxiliary devices, improves the user’s cognition of the potential information characteristics and laws of large-scale complex network data.
Another core element of WiMi’s data interaction system is user interaction. Interaction is a dialogue between users and the system, the process of interactive manipulation and understanding of data. Interaction effectively alleviates the contradiction between limited visualization space and data overload and helps expand the reach for information representation in visualization, thus addressing the gap between limited space and data volume and complexity. At the same time, interaction enables users to understand and analyze the data better, helping them explore the data and improve data awareness.
Network topology visualization technology has flourished with the development of the Internet. With the development of emerging technologies such as graphical computing and virtual reality, significant progress has been made in the research of network topology visualization technology, which also has a wide range of application prospects in network topology analysis, security situational awareness, management, and Internet modelling.
Network topology visualization technology has flourished with the development of the Internet. With emerging technologies such as graphical computing and virtual reality, the research on network topology visualization technology has also made significant progress. The technology also has broad application prospects in network topology analysis, security situational awareness, management, and Internet modelling.
Data mining is a multidisciplinary blend of technologies. The use and combination of related algorithms and techniques vary according to application scenarios and needs. Applying the interpretability of neural networks in data mining will be a valuable research direction. Theoretical research will ultimately serve specific needs and applications. Combining both in applications still needs to be explored in the future. WiMi will further study this area and strive for breakthroughs that will profoundly impact the improvement of socio-economic benefits.
