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In the Web3 ecosystem, on-chain data has long been in a state of 'fragmented islands': users' asset holdings, transaction behaviors, ecological contributions, social relationships, and other data are scattered across different chains, protocols, and scenarios, lacking effective associations - wanting to analyze whether 'the NFT holdings of a certain address have risk linkage with DeFi staking behaviors' requires manual checks across NFT tools and DeFi platforms and then cross-referencing; wanting to verify whether 'the team background of a certain project is associated with token price fluctuations' requires repeated switching between block explorers, social platforms, and market tools. Traditional on-chain data tools can only 'display data in a single dimension' (e.g., viewing only assets, only transactions, or only social), failing to construct a 'multi-dimensional association view', resulting in a lot of hidden associative value (like risk transmission paths, profit linkage opportunities, ecological collaboration rules) being overlooked. The core breakthrough of Bubblemaps lies in constructing a cross-dimensional association analysis engine of 'multi-source data access - association rule modeling - visual association presentation', allowing dispersed on-chain data to form an 'association network' that helps users mine 'deep value that cannot be discovered through a single data dimension'.