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The process of gathering and associating data from multiple sensors orsub-detectors due to a common physical event (the process of event-building) isused in many fields, including high-energy physics and $\gamma$-ray astronomy.Fault tolerance in event-building is a challenging problem that increases indifficulty with higher data throughput rates and increasing numbers ofsub-detectors. We draw on biological self-assembly models in the development ofa novel event-building paradigm that treats each packet of data from anindividual sensor or sub-detector as if it were a molecule in solution. Just asmolecules are capable of forming chemical bonds, "bonds" can be defined betweendata packets using metadata-based discriminants. A database -- which plays therole of a beaker of solution -- continually selects pairs of assemblies atrandom to test for bonds, which allows single packets and small assemblies toaggregate into larger assemblies. During this process higher-qualityassociations supersede spurious ones. The database thereby becomes fluid,dynamic, and self-correcting rather than static. We will describe tests of theself-assembly paradigm using our first fluid database prototype and data fromthe VERITAS $\gamma$-ray telescope.

Testing a Novel Self-Assembling Data Paradigm in the Context of IACT Data