> For the complete documentation index, see [llms.txt](https://docs.knn3.xyz/welcome/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.knn3.xyz/welcome/k.transformer/real-time-data-service/k.graphx-legacy/k.graphx-extension-dynamic-verifiable-credentials.md).

# K.GraphX Extension: Dynamic Verifiable Credentials

## **What is Dynamic Verifiable Credentials ?**

**Dynamic Verifiable Credentials（DVC）** is intended to be a lightweight label solution that assists GraphX in richer label applications. In short, DVC is a verifiable and queriable label service that helps identify entity or relationship characteristics.

**Dynamic**

DVC can realize the aggregation of multi-dimensional data such as user's asset data, behavior data, and relationship data, and store all user details on s3/IPFS/Arweave and other decentralized storage platforms. It can easily query Web3 users' real-time activities on the chain, and the real-time performance of data change capture can reach 10 seconds.

**Verifiable**

DVC data is verifiable. Any third party can view these aggregated data through the fixed on-chain address (wallet address or domain name). At the same time, KNN3 uses data paging to improve performance and realize data verification and map presentation in real-time.

**Credentials**

The neutral data credentials makes KNN3 data credible. As a label, DVC has scalability and composability.

## **What can you do with DVC ?**

As an extension of GraphX, DVC data is aggregated and processed, and finally uploaded to S3 in the form of files for everyone to access. It can also support uplink or docking with any decentralized storage scheme (IPFS, etc.) to ensure the authenticity and verifiability of the data. The access address path is fixed. Users only need to configure a fixed access path and wallet address to see certain types of data in the relevant address, without requiring users to merge and aggregate data themselves. 

Users can submit requests via GitHub([https://github.com/Web3-Data-Collaboration-Proposals/DCPs repository](https://github.com/Web3-Data-Collaboration-Proposals/DCPs)) or get in touch with the team. KNN3 will expand DVC data according to user needs to meet the data integration needs of more users.

{% embed url="<https://github.com/Web3-Data-Collaboration-Proposals/DCPs>" %}

## **How does it work ?**

DVC uses the Spark real-time analysis engine for data analysis and aggregation to ensure real-time data (within 10 seconds) and uploads the aggregated data to S3 or other decentralized file services.


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