  Many well-known, real-world problems involve dynamic data which describe the
relationship among the entities. Hypergraphs are powerful combinatorial
structures that are frequently used to model such data. For many of today's
data-centric applications, this data is streaming; new items arrive
continuously, and the data grows with time. With paradigms such as Internet of
Things and Edge Computing, such applications become more natural and more
practical. In this work, we assume a streaming model where the data is modeled
as a hypergraph, which is generated at the edge. This data then partitioned and
sent to remote nodes via an algorithm running on a memory-restricted device
such as a single board computer. Such a partitioning is usually performed by
taking a connectivity metric into account to minimize the communication cost of
later analyses that will be performed in a distributed fashion. Although there
are many offline tools that can partition static hypergraphs excellently,
algorithms for the streaming settings are rare. We analyze a well-known
algorithm from the literature and significantly improve its running time by
altering its inner data structure. For instance, on a medium-scale hypergraph,
the new algorithm reduces the runtime from 17800 seconds to 10 seconds. We then
propose sketch- and hash-based algorithms, as well as ones that can leverage
extra memory to store a small portion of the data to enable the refinement of
partitioning when possible. We experimentally analyze the performance of these
algorithms and report their run times, connectivity metric scores, and memory
uses on a high-end server and four different single-board computer
architectures.
