Research And Implementation Of Alot Material System Under Distributed Computing Environments

The problems of Alot Material under Distributed Computing Environment come down to many classical problems in computer domain. Especially in our modern society where Alot Material has been widely used, researches on Alot Material under Distributed computing environment to be more meaningful. Under the background of Alot Material under Distributed computing environment, this paper emphasizes to study three key problems: the integration of relational database, the design of database query optimization and alot material. Our mainly research includes:1. The Data Integration. With the help of integration tool, InforEAI, the paper puts forward a hierarchy of relation databases' integration. The concept of data mirror layer, with which original different databases firstly are converted to a standard database is introduced, and then, by trigger mechanic complicated online updating can be realized. A meta-trigger table is created to fulfill user-customization because in reality business conversion functions are changeable and unforeseeable.2. The Design of Database Query Optimization. We summaries the data of basic database, then concluded the information needed by making decisions which would be stored into standard analyses database to be used when making decision. During the implementation, we optimized the query and statistic to database by file organization, spliting tables and creating indexes, which enhanced the performance of query and statistic to databases, so we can support information to decision module quickly and correctly.3. The model of Alot Material. First because of the change of the number of material between supply and need, we couldn't know exactly situation before it happened, so we build the maths model of alot material with fuzzy constrained which accords with the fact and solves the problem better. Because solving the alot material with fuzzy constrained by decided methods will decrease the fuzzy meaning of model and will go against making decision reasonable, in this thesis we solve the model be GA. Through setting reasonable initial pop to generate the original genus and the design of crossing and mutating AO, we achieved to search the best solutions quickly.