Abstract:The center of urban rail network needs to collect the data of all the urban rail lines and the size of table records will reach billions. The data query on urban rail network will need too much time and the system efficient is very low. We propose the program to optimize the system architecture by database cluster and middleware, which improves query efficiency because of more powerful storage and parallel processing capacity than that of a single database. An sharding method that divide the data horizontally by lines to avoid the expensive table joins crossing databases and the new rail line can be easily extended just by adding more database nodes. Serveral technologies, such as table partitioning, index, materialized view and SQL etc. are used also to optimize the reaction time when standalone inquiring. A special light-weight database access middleware used in the system architecture is designed to solved some key problems such as SQL parsing, route inquiring and result data merging etc.. The experiments are carrieded out on data from Guangzhou Metro as verifying the scheme of this paper. The results show that the reaction time of all types of queries is reduced 90% at least.

Abstract:This paper uses object-oriented programming language C++ as a development language. The work uses ADO database access technologies and socket network communication technology in warehouse management information system design. It uses Shufled Frog Leaping Algorithm on multiple-table query Optimization for relational database. It makes their communications and response time are significantly reduced. The system uses c/s framework with logon rights management, database access, database operations, database query management and function modules such as printing a report. At last, experiment proves system designed makes Query time improved significantly and it accurately complete all kinds of queries management tasks. The system improues the storage management efficiency and makes cost saued, it has a strong practical relevance.

Abstract:With the growing demand for data management, it is a great challenge to reduce and throttle the energy consumption in data centers. DBMS is a vital software in data center to deal with volumes of data, thus, energy efficient query processing and optimization is one of the critical issues to be solved. We proposed a novel cost model to estimate energy consumption of a query plan, and we investigated the effects of different optimization goals on query optimization under different hardware configurations. Experiements demonstrated that the results of performance- oriented and energy-oriented query optimization are same under the traditional hardware configuration, while the results turned out to be different under the new hardware configuration, which indicates that it is practical to improve the energy efficiency of database systems.