Optimization of cache-based semi-stream joins

Near-real-time data warehousing is an emerging area of research in order to meet the high and up-to-date demands of business organizations. This means customers transactions executed at data source level need to reflect into the data warehouse immediately that requires semi-stream join between a stream of customer transactions and disk-based master data. For this purpose a well-known algorithm called CACHEJOIN was presented in the literature. Due to the sequential execution of both of its phases, called stream-probing phase and disk-probing phase, the algorithm cannot exploit the memory and CPU resources optimally and consequently it leaves it suboptimal. We present an algorithm P-CACHEJOIN that aims to optimize the performance of CACHEJOIN. P-CACHEJOIN allows parallel execution of two phases of CACHEJOIN which maximizes number of joined stream tuples and consequently improves service rate of the algorithm significantly. This paper presents the performance analysis of P-CACHEJOIN with existing CACHEJOIN empirically using synthetic dataset.