  Streaming data join is a critical process in the field of near-real-time data
warehousing. For this purpose, an adaptive semi-stream join algorithm called
CACHEJOIN (Cache Join) focusing non-uniform stream data is provided in the
literature. However, this algorithm cannot exploit the memory and CPU resources
optimally and consequently it leaves its service rate suboptimal due to
sequential execution of both of its phases, called stream-probing (SP) phase
and disk-probing (DP) phase. By integrating the advantages of CACHEJOIN, in
this paper we present two modifications in it. First is called P-CACHEJOIN
(Parallel Cache Join) that enables the parallel processing of two phases in
CACHEJOIN. This increases number of joined stream records and therefore
improves throughput considerably. Second is called OP-CACHEJOIN (Optimized
Parallel Cache Join) that implements a parallel loading of stored data into
memory while the DP phase is executing. We present the performance analysis of
both of our approaches with existing CACHEJOIN empirically using synthetic
skewed dataset.
