WiMi Is Researching Blockchain Optimized Data Storage Model Based on Extreme Learning Machine (ELM) Sharding

Rhea-AI Summary

WiMi Hologram Cloud Inc. (NASDAQ: WIMI) is researching a blockchain optimized data storage model based on Extreme Learning Machine (ELM) sharding. This innovative model aims to improve blockchain system performance, reduce storage costs, enhance data privacy and security, and increase data processing efficiency.

Blockchain Technology Expert

The integration of Extreme Learning Machine (ELM) sharding into blockchain data storage represents a significant advancement in the field of distributed ledger technologies. Sharding, as a concept, is not new to blockchain ecosystems. It is a scalability solution that partitions the database to spread the load, thereby enhancing the throughput of transactions. However, the novel approach of using ELM to optimize sharding processes could potentially alleviate the bottlenecks of blockchain performance, particularly in relation to data storage and processing speed.

ELM's unique characteristics of fast training speed and strong generalization ability suggest that it could improve the efficiency of shard management and data processing within each shard. This could lead to reduced reliance on full nodes, which are typically a limiting factor in scaling blockchain networks. For businesses and investors, such technological improvements could translate into lower operational costs and enhanced data security, making blockchain solutions more viable for large-scale applications.

However, the practical implementation and the actual performance gains of this ELM-based sharding model in a real-world blockchain network remain to be seen. It will be crucial to monitor the subsequent development and testing phases to gauge the model's impact on the industry.

Data Security Analyst

From a data security perspective, the proposed ELM sharding model by WiMi Hologram Cloud Inc. could offer a more granular approach to data privacy and integrity. By partitioning data across multiple ELM nodes, the risk of a single point of failure is mitigated. This means that even if one shard is compromised, the integrity of the entire blockchain remains intact. It is a layered defense strategy that can be particularly appealing for industries that handle sensitive information, such as finance or healthcare.

Furthermore, the use of machine learning algorithms like ELM within blockchain could pave the way for advanced predictive analytics and automated anomaly detection within the ledger. This could strengthen the blockchain's resilience against fraudulent activities and enhance its overall security posture. Stakeholders should consider the potential for such technologies to revolutionize data management practices within their respective industries.

Financial Analyst

For investors, the announcement by WiMi Hologram Cloud Inc. is a forward-looking development that could have long-term financial implications. If successfully implemented, the ELM sharding model could position WiMi as a pioneer in blockchain storage solutions, potentially opening up new revenue streams and partnerships. The company's focus on reducing storage costs and improving transaction speeds addresses two critical pain points that could drive adoption and increase market share in the competitive blockchain industry.

However, investors should also weigh the research and development risks involved. The transition from a traditional blockchain model to one that incorporates ELM sharding will require significant investment and could encounter technical challenges. The timeline for commercialization and the model's reception by the blockchain community will be key factors influencing the financial outcomes of this initiative.

BEIJING, March 8, 2024 /PRNewswire/ -- WiMi Hologram Cloud Inc. (NASDAQ: WIMI) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, today announced that it is researching a blockchain optimized data storage model based on Extreme Learning Machine (ELM) sharding. ELM is a machine learning algorithm based on artificial neural networks. The core idea of ELM is to initialize the connection weights between the input layer to the hidden layer randomly. Then through parsing, the connection weights between the output layer to the hidden layer are quickly calculated, and a simple and efficient neural network model is constructed by randomly generating the input weights and the bias of the hidden layer neurons. Compared with traditional neural network algorithms, ELM is characterized by fast training speed and strong generalization ability. Whereas sharding is a method of splitting data into multiple segments in a blockchain system, each of which can be processed and stored independently, sharding improves the performance of the blockchain system.

The ELM-based sharding blockchain data storage model divides the blockchain network into multiple shards, each of which needs to store and process only part of the blockchain's data. Specifically, each slice only needs to store and verify the blocks and transaction data associated with it, without storing a full copy of the entire blockchain. This can greatly reduce storage requirements, lower storage costs, and improve the efficiency and performance of data synchronization. In this model, each slice can use ELM as its local storage and computation unit. ELM is an efficient machine learning algorithm with fast training and prediction capabilities. Each slice can use ELM to store and process its own blockchain data without relying on other slices or full nodes. This increases the efficiency of data storage and processing and reduces the dependency on full nodes.

Specifically, it is first necessary to split the original blockchain data into multiple segments and store these segments on different nodes, thus realizing the distributed management of data storage as well as achieving parallel processing of data. Each node only needs to store the sharding data it is responsible for, which greatly reduces the storage pressure of nodes. Each shard can be managed by one or more ELM nodes. ELM nodes use ELM algorithms to train and process the data. ELM is a fast and efficient machine learning algorithm that can process and analyze large amounts of data in a short period of time. Each ELM node can independently process and store the data sharding it is responsible for. This sharding technology improves the parallel processing of data, thus increasing the performance of the entire blockchain system. At the same time, since each node only needs to manage a portion of the data, it can reduce the cost of data storage and processing.

In addition, the ELM-based sharding technology can also provide higher data privacy and security. Since the data is partitioned into multiple segments and managed by different ELM nodes, even if one node is attacked or the data is leaked, it will not have an impact on the data integrity of the whole system.

The ELM-based sharding blockchain data storage model researched by WiMi can solve the problems of storage capacity and transaction speed faced by the traditional blockchain data storage model, reduce the storage cost, improve the efficiency and performance of data synchronization, and provide a more efficient way of data management and access to further improve the performance and scalability of the blockchain system. This model is of great significance in the application of blockchain technology and data storage models.

About WIMI Hologram Cloud

WIMI Hologram Cloud, Inc. (NASDAQ:WIMI) is a holographic cloud comprehensive technical solution provider that focuses on professional areas including holographic AR automotive HUD software, 3D holographic pulse LiDAR, head-mounted light field holographic equipment, holographic semiconductor, holographic cloud software, holographic car navigation and others. Its services and holographic AR technologies include holographic AR automotive application, 3D holographic pulse LiDAR technology, holographic vision semiconductor technology, holographic software development, holographic AR advertising technology, holographic AR entertainment technology, holographic ARSDK payment, interactive holographic communication and other holographic AR technologies.

Safe Harbor Statements

This press release contains "forward-looking statements" within the Private Securities Litigation Reform Act of 1995. These forward-looking statements can be identified by terminology such as "will,""expects,""anticipates,""future,""intends,""plans,""believes,""estimates," and similar statements. Statements that are not historical facts, including statements about the Company's beliefs and expectations, are forward-looking statements. Among other things, the business outlook and quotations from management in this press release and the Company's strategic and operational plans contain forward−looking statements. The Company may also make written or oral forward−looking statements in its periodic reports to the US Securities and Exchange Commission ("SEC") on Forms 20−F and 6−K, in its annual report to shareholders, in press releases, and other written materials, and in oral statements made by its officers, directors or employees to third parties. Forward-looking statements involve inherent risks and uncertainties. Several factors could cause actual results to differ materially from those contained in any forward−looking statement, including but not limited to the following: the Company's goals and strategies; the Company's future business development, financial condition, and results of operations; the expected growth of the AR holographic industry; and the Company's expectations regarding demand for and market acceptance of its products and services.

Further information regarding these and other risks is included in the Company's annual report on Form 20-F and the current report on Form 6-K and other documents filed with the SEC. All information provided in this press release is as of the date of this press release. The Company does not undertake any obligation to update any forward-looking statement except as required under applicable laws.

 

 

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SOURCE WiMi Hologram Cloud Inc.

What technology is WiMi Hologram Cloud Inc. researching?

WiMi Hologram Cloud Inc. is researching a blockchain optimized data storage model based on Extreme Learning Machine (ELM) sharding.

What is ELM and how does it work?

ELM is a machine learning algorithm based on artificial neural networks. It initializes connection weights randomly and quickly calculates connection weights between layers, creating an efficient neural network model.

What is sharding in a blockchain system?

Sharding is a method of splitting data into multiple segments in a blockchain system, allowing each segment to be processed and stored independently to improve system performance.

How does the ELM-based sharding blockchain data storage model improve efficiency?

The model divides the blockchain network into multiple shards, reducing storage requirements, lowering costs, and improving data synchronization efficiency.

What are the benefits of using ELM in the sharding technology?

ELM provides fast training and prediction capabilities, allowing each slice to store and process its own blockchain data independently, enhancing data storage and processing efficiency.

How does the ELM-based sharding technology enhance data privacy and security?

By partitioning data into segments managed by different ELM nodes, the technology ensures data integrity even if one node is attacked or data is leaked, improving overall system security.