Abstract: The need to provide up-to-date information, streaming warehouses are used to screen multipart systems such as web site complexes, data centers and world-wide networks, congregating and comparing encumbered collections of happenings and measurements. For profound analysis and for rapid responses, both chronological data and concurrent data that raising the problems in streaming warehouses were used. The data warehouse gathers a large number of streaming data provisions that are generated by external sources and reach target asynchronously. Scheduling updates is the most important processes that are concerned severely in streaming warehouses. Scheduling algorithms provided for loading data in concurrent data warehouses that are used in the applications such as online monetary trading, IP network screening and credit card scam detection. In this study, the evaluation of performance analysis of RECSS algorithm is described. The first objective of this study is to schedule the updates one by one on one or more processors in a way to reduce the total mustiness. In turn to verify the total fairness, the second objective of this study is to limit the maximum "extend" in which to define (approximately) as the ratio between the periods of time of an update waits till being the process is concluded and the length of each updates. When compared to the previous research, it deserves the mustiness is provided that the processors are adequately fast and find that only those update propagation algorithms which enforce no scheduling constraints are tolerable for use in a concurrent streaming warehouse.