The invention concerns an economical technique for storing, rerunning and processing DATES for the YEAR 2000. It is designed for perpetuating the appliances affected by the problem of managing year 2000 and the considerable stocks of computer archives accumulated over the last decades well beyond the 21?st� century while avoiding the excessive costs of standard solutions and the high risks resulting from manual modifications of the constitutive elements of the applications (programme codes, JCL's, screen grids, data files, etc.). This invention is based on the following techniques: introducing a new type of 'DATE' data in the syntax of standard languages L3G/L4G; dissociating the procedures carried out on the 'DATE' data from their storage format; applying a compression technique which will both extend the porting of the values of 'DATE' data and remain as close as possible to the physical structures of the present data; applying a dynamic transcoding technique to enable the co existence between the old formats and the new formats of the 'DATE' data.

Since accurate migrations of data fragments in distributed database systems； known dynamic fragment allocation； play an important role in amendment of distributed database performance； several algorithms each of which shows different performance in various conditions； have been proposed to improve dynamic fragment allocation in distributed database systems. In this paper； we are going to propose a novel algorithm which is able to detect oscillation conditions and provides a solution to prevent redundant fragment migration. Proposed algorithm； an improved version of the previous Near Neighborhood Allocation (NNA) algorithm； uses a fuzzy inference engine to detect oscillations in fragment request and ignore fragment migrations under these conditions. Our results indicate that； fuzzy based clearly improved the performance of distributed database systems in which oscillation conditions are more probable. This algorithm； providing data clustering； is very suitable for DDBS in the networks； with heavy loads； and frequent requests for data fragments coming from different sites.