MulChain: Enabling Advanced Cross-Modal Queries in Hybrid-Storage Blockchains

• URL: http://arxiv.org/abs/2502.18258v1
• Date: Tue, 25 Feb 2025 14:43:07 GMT
• Title: MulChain: Enabling Advanced Cross-Modal Queries in Hybrid-Storage Blockchains
• Authors: Zhiyuan Peng, Xin Yin, Gang Wang, Chenhao Ying, Wei Chen, Xikun Jiang, Yibin Xu, Yuan Luo,
• Abstract summary: Large multimodal data files, such as videos, are often stored offline, leaving only lightweight metadata on the chain.<n>Existing blockchains do not provide native support for multimodal data querying.<n>We propose MulChain, a novel architecture to enable smooth integration with existing blockchains.
• Score: 16.371307857771477
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With its decentralization and immutability, blockchain has emerged as a trusted foundation for data management and querying. Because blockchain storage space is limited, large multimodal data files, such as videos, are often stored offline, leaving only lightweight metadata on the chain. While this hybrid storage approach enhances storage efficiency, it introduces significant challenges for executing advanced queries on multimodal data. The metadata stored on-chain is often minimal and may not include all the attributes necessary for queries like time range or fuzzy queries. In addition, existing blockchains do not provide native support for multimodal data querying. Achieving this capability would necessitate extensive modifications to the underlying blockchain framework, even reconstructing its core architecture. Consequently, enabling blockchains with multimodal query capabilities remains a significant problem, which necessitates overcoming the following three key challenges: (1) Designing efficient indexing methods to adapt to varying workloads that involve frequent insertions and query operations; (2) Achieving seamless integration with existing blockchains without altering the underlying infrastructure; (3) Ensuring high query performance while minimizing gas consumption. To address these challenges, we propose MulChain, a novel middleware architecture to enable smooth integration with existing blockchains. At the core of MulChain is the BHashTree, a flexible data structure that dynamically switches between tree and hash nodes based on workload characteristics, ensuring efficient insertion and query operations. Furthermore, the middleware provides standardized interfaces for blockchain systems, unifying query methods across different platforms.

Related papers

• Zaptos: Towards Optimal Blockchain Latency [52.30047458198369]
  We introduce Zaptos, a parallel pipelined architecture designed to minimize end-to-end latency.<n>Zaptos achieves a throughput of 20,000 transactions per second with sub-second latency.
  arXiv  Detail & Related papers  (2025-01-18T00:22:22Z)
• AsCAN: Asymmetric Convolution-Attention Networks for Efficient Recognition and Generation [48.82264764771652]
  We introduce AsCAN -- a hybrid architecture, combining both convolutional and transformer blocks. AsCAN supports a variety of tasks: recognition, segmentation, class-conditional image generation. We then scale the same architecture to solve a large-scale text-to-image task and show state-of-the-art performance.
  arXiv  Detail & Related papers  (2024-11-07T18:43:17Z)
• BlockFound: Customized blockchain foundation model for anomaly detection [47.04595143348698]
  BlockFound is a customized foundation model for anomaly blockchain transaction detection. We introduce a series of customized designs to model the unique data structure of blockchain transactions. BlockFound is the only method that successfully detects anomalous transactions on Solana with high accuracy.
  arXiv  Detail & Related papers  (2024-10-05T05:11:34Z)
• DBNode: A Decentralized Storage System for Big Data Storage in Consortium Blockchains [2.474908349649168]
  This paper proposes a decentralized storage system for Hyperledger Fabric. We employ erasure coding to partition files, then organize these chunks into a hierarchical structure. We design a two-layer hash-slots mechanism and a mirror strategy, enabling high data availability.
  arXiv  Detail & Related papers  (2024-09-30T09:22:15Z)
• Turning Trash into Treasure: Accelerating Inference of Large Language Models with Token Recycling [53.58854856174773]
  Speculative decoding is an approach to accelerate inference through a guess-and-verify paradigm. Token Recycling stores candidate tokens in an adjacency matrix and employs a breadth-first search algorithm. It significantly outperforms existing train-free methods by 30% and even a training method by 25%.
  arXiv  Detail & Related papers  (2024-08-16T12:20:56Z)
• Should my Blockchain Learn to Drive? A Study of Hyperledger Fabric [14.834625066344586]
  This paper explores the concept of self-driving blockchains, which have the potential to predict workload changes and reconfigure themselves for optimal performance without human intervention. We identify specific parameters and components in Hyperledger Fabric, a popular permissioned blockchain system, that are suitable for autonomous adaptation and offer potential solutions for the challenges involved. Our experiments indicate up to 11% improvement in success throughput and a 30% decrease in latency, making this a significant step towards implementing a fully autonomous blockchain system in the future.
  arXiv  Detail & Related papers  (2024-06-10T14:33:59Z)
• What Blocks My Blockchain's Throughput? Developing a Generalizable Approach for Identifying Bottlenecks in Permissioned Blockchains [0.3495246564946556]
  We develop a more unified and graphical approach for identifying bottlenecks in permissioned blockchains. We conduct in-depth case studies on Hyperledger Fabric and Quorum, two widely used permissioned blockchains.
  arXiv  Detail & Related papers  (2024-04-02T13:00:50Z)
• Enhancing Trust and Privacy in Distributed Networks: A Comprehensive Survey on Blockchain-based Federated Learning [51.13534069758711]
  Decentralized approaches like blockchain offer a compelling solution by implementing a consensus mechanism among multiple entities. Federated Learning (FL) enables participants to collaboratively train models while safeguarding data privacy. This paper investigates the synergy between blockchain's security features and FL's privacy-preserving model training capabilities.
  arXiv  Detail & Related papers  (2024-03-28T07:08:26Z)
• Generative AI-enabled Blockchain Networks: Fundamentals, Applications, and Case Study [73.87110604150315]
  Generative Artificial Intelligence (GAI) has emerged as a promising solution to address challenges of blockchain technology. In this paper, we first introduce GAI techniques, outline their applications, and discuss existing solutions for integrating GAI into blockchains.
  arXiv  Detail & Related papers  (2024-01-28T10:46:17Z)
• Multi-dimensional Data Quick Query for Blockchain-based Federated Learning [6.499393722730449]
  We propose a novel data structure to improve the query efficiency within each block named MerkleRB-Tree. In detail, we leverage Minimal Bounding Rectangle(MBR) and bloom-filters for the query process of multi-dimensional continuous-valued attributes and discrete-valued attributes respectively.
  arXiv  Detail & Related papers  (2023-09-27T01:35:11Z)
• Selective Deletion in a Blockchain [0.0]
  We present the first concept for the selective deletion of single entries in a blockchain. The general consensus algorithm is extended by the functionality of regularly creating summary blocks. With a shifting marker of the Genesis Block, data can be deleted from the beginning of a blockchain.
  arXiv  Detail & Related papers  (2021-01-14T08:06:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.