NFT Holder Data processing with machine learning

[seriybeliy11]

Summary

Utilize the TON Blockchain APIs for collection and analysis of NFT holder data using machine learning.

Bounty Context

Why it's Important? This bounty would provide comprehensive insights about NFT holders on the TON blockchain by processing gathered data through machine learning. It would also significantly optimize the tracking of NFT transactions, providing valuable information for businesses, individuals and developers who interact with TON Blockchain.

Problem showcase The challenge is to interact with TON blockchain node via API, collect the necessary data about NFT holdings and their addresses, effectively process this data via machine learning to obtain the desired insights, and then implement a suitable interface to present the processed data.

Potential Solution The solution would involve setting up a TON blockchain node to access APIs for data retrieval. The next step is developing software capable of gathering data about NFT holdings using the accessed APIs. Collected data would then be processed using machine learning methodologies to extract the necessary information. Lastly, an interface would be implemented to display the results in a coherent manner that can be embedded as a microservice in any system.

Defenition of Done

• Setting up of TON blockchain node and gaining API access
• Successful development and testing of data collection software
• Effective processing of collected data using Machine Learning
• Implementation of a user-friendly interface for display of processed data
• Capability to integrate as a microservice in other systems

References

Related reference materials for similar solutions are not available at the moment.

REWARD

• SBT Bounty Reward
• 2000 USD in TON

🪄 Created with the help of TON Bounties Creator Bot.

[delovoyhomie]

@seriybeliy11, what information about NFT holders do you want to obtain, and how will it be useful? And the parsing process through API is very lengthy, and I'm afraid your application won't be able to keep up with the pace of new NFTs. How do you plan to address this issue?

[seriybeliy11]

We understand that the parsing process through API can be lengthy, but we have strategies in place to address this issue, such as optimizing the API requests and using efficient data processing techniques.

Here are some specific methods we plan to use to optimize API requests and process data efficiently:

• Batch API requests to reduce the number of calls and minimize latency
• Use parallel processing to handle multiple requests simultaneously
• Implement caching mechanisms to store frequently used data and reduce the load on the API
• Apply data compression techniques to reduce the size of data being transferred and stored
• Use efficient data structures and algorithms to optimize data processing and analysis

[seriybeliy11]

@delovoyhomie

this bounty could be a good mvp for grant.

The system should be something like Arkham in my mind, only simpler. Also, when defining the portrait we can not just say "this is definitely a collector who is interested in gaming nfts", but also implement percentage confidence analytics in our analysis - 56% accuracy (for example)

[ProgramCrafter]

I note that this project is easily extendable to other blockchains (so double-granting is possible), and does not provide extra value to TON ecosystem more than to others.

I also note that collecting data about users, if officially supported by TON, may cause an outburst.

And, do you intend to use LLMs to create profile? If so, what's the "efficient data processing technique" to process many users fast?

[seriybeliy11]

@ProgramCrafter

Our goal is to provide valuable insights and analytics based on this OPEN-data while maintaining the highest standards of privacy and transparency.

Firstly, we use distributed computing and parallel processing to split the workload across multiple nodes, enabling us to process large datasets quickly and efficiently. This approach significantly reduces processing time and allows us to scale our system as the number of users grows. Secondly, we implement data compression and optimization techniques to minimize the size of the data being processed, which in turn reduces the computational resources required. This ensures that our system remains fast and responsive even when dealing with large amounts of data. Thirdly, we leverage machine learning algorithms and models that are specifically designed for high-speed data processing. These models can quickly analyze and categorize user data, enabling us to create accurate user profiles in real-time.

Using LLM is a non-optimized approach. Our main goal is to classify holders. There will be a lot of work to optimize the parsing. Machine learning algorithms will also be used, and we will come up with a complete architecture that will allow processing the input data stream without delay.

I can't agree with you about the low cost. TON, like any blockchains, needs flexible monitoring not only at the infrastructure level, but also at the level of marketing and trends. The concept of data brokerage doesn't scare us, and it shouldn't scare you either :)

[seriybeliy11]

@delovoyhomie

Is this application relevant?

[delovoyhomie]

@seriybeliy11, thank you for your initiative, but unfortunately, we cannot support your bounty. I strongly recommend you to come back with an MVP and try again with renewed vigor to showcase your product.