An Efficient Multi-Layer Indexing Method on Blockchain for Multimodal Data Querying

In the digital society era, the generation frequency of multimodal data is rapidly increasing. Blockchain, recognized as a trusted distributed database technology, provides a new solution for the trustworthy storage and efficient management of multimodal data. However, blockchain systems support only query that use transaction hash values as keywords and cannot directly leverage the content features of multimodal data, leading to generally low query efficiency. To address this issue, this paper introduces an efficient multi-layer indexing method on blockchain for multimodal data querying. It establishes an effective mapping between on-chain and off-chain data through a verifiable on-chain and off-chain collaborative storage architecture. The paper also proposes Multi-layer Bitmap Block Index (MBBI) and Cuckoo Merkel Tree (C-MT) to optimize the querying process. Experimental results demonstrate that this method not only ensures the consistency and integrity of metadata across on-chain and off-chain but also significantly enhances the efficiency of multimodal data query. This offers a feasible solution for the storage and query needs of large-scale multimodal data.