用类脑网络提升大数据语义关联，自动发现并链接相似数据字段。
Hopfield Networks Meet Big Data: A Brain-Inspired Deep Learning Framework for Semantic Data Linking
- 模仿大脑左右半球分工，用深度霍普菲尔德网络做记忆与联想
- 在分布式系统中实现高准确率的数据消歧与集成，准确率显著提升
- 适合处理海量异构数据的场景，如跨库数据融合与智能分析
数据爆炸式增长催生了大量异构数据集，对预测分析和决策至关重要，但数据质量与语义一致性仍面临挑战。本文提出一种类脑分布式认知框架，将深度学习与霍普菲尔德网络结合，实现跨数据集语义相关属性的识别与链接。该架构模拟人脑双半球功能：右半球吸收新信息，左半球检索已有表征以建立关联。基于MapReduce与Hadoop分布式文件系统（HDFS）实现，利用深度霍普菲尔德网络作为联想记忆机制，强化频繁共现属性的关联记忆，并随数据模式动态调整关系。实验表明，霍普菲尔德记忆中的关联印记随时间增强，使链接数据保持上下文相关性，有效提升数据消歧与集成准确率。结果证明，深度霍普菲尔德网络与分布式认知处理的结合，为大规模环境下的复杂数据关系管理提供了一种可扩展、生物启发的解决方案。
原文摘要 · Abstract (English)
The exponential rise in data generation has led to vast, heterogeneous datasets crucial for predictive analytics and decision-making. Ensuring data quality and semantic integrity remains a challenge. This paper presents a brain-inspired distributed cognitive framework that integrates deep learning with Hopfield networks to identify and link semantically related attributes across datasets. Modeled on the dual-hemisphere functionality of the human brain, the right hemisphere assimilates new information while the left retrieves learned representations for association. Our architecture, implemented on MapReduce with Hadoop Distributed File System (HDFS), leverages deep Hopfield networks as an associative memory mechanism to enhance recall of frequently co-occurring attributes and dynamically adjust relationships based on evolving data patterns. Experiments show that associative imprints in Hopfield memory are reinforced over time, ensuring linked datasets remain contextually meaningful and improving data disambiguation and integration accuracy. Our results indicate that combining deep Hopfield networks with distributed cognitive processing offers a scalable, biologically inspired approach to managing complex data relationships in large-scale environments.
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