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