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This research aims to build a system for electronic documentation for the unit of graduate studies, which is used for managing electronic documents (official books, attachments, and instructions). The system was characterized by the possibility of participating electronic documents between administrative units and scientific departments in the college through the use of distributed database management system (Oracle), as well as the use of multimedia databases for dealing with images and (pdf) files, which represent the instruction manual for graduate work. System have been analyzed and identify entities and its attributes as well as the relationships between these entities, this model of entities and relationships was used to represent the database, and then convert the model into standard formats relations. Oracle 10g language was used to design distributed database. Finally, the Proposed model was applied to real data obtained from graduate unit in the college, and showed its efficiency in the management of data and official documents used.

In this study, query processing efficiency of the distributed database in the relational database Oracle9i was examined and compared. Our test data is based on a (Customer Type Definition), which is designed to represent customer information of bank. We generated 8 different sizes of test customers: 300, 600, 900, 2100,4500, 45000, 1360 Records. DML language (SQL) as tools for any comparison can be achieved by the statistical performance parameter (Elapsed time, CPU time, Logical read , Physical read, Logical write, Physical write, Elapsed time, UGA ) .statistical analysis using ANOVA TABLE is used to judge between comparisons. All the queries were run on two computers for different sizes of vertical and horizontal fragments. Changes in performance metrics for each query were written in table after experiments. Finally, we presented the results, and discussed where enhancements are required. Statistical analysis using ANOVA TABLE were used to distinguish between this comparison, it had been found that SELECT, UPDATE and DELETE have the most influence on the performance parameter respectively.

Data mining is the process of extracting hidden patterns from data. One of the most important activities in data mining is the association rule mining and the new head for data mining research area is privacy of mining. Privacy preserving data mining is a new research trend in privacy data for data mining and statistical database. Data mining can be applied on centered or distributed databases. Most efficient approaches for mining distributed databases suppose that all of the data at each site can be shared. Privacy concerns may prevent the sites from directly sharing the data, and some types of information about the data. Privacy Preserving Data Mining (PPDM) has become increasingly popular because it allows sharing of privacy sensitive data for analysis purposes. In this paper, the problem of privacy preserving association rule mining in horizontally distributed database is addressed by proposing a system to compute a global frequent itemsets or association rules from different sites without disclosing individual transactions. Indeed, a new algorithm is proposed to hide sensitive frequent itemsets or sensitive association rules from the global frequent itemsets by hiding them from each site individually. This can be done by modifying the original database for each site in order to decrease the support for each sensitive itemset or association rule. Experimental results show that the proposed algorithm hides rules in a distributed system with the good execution time, and with limited side effects. Also, the proposed system has the capability to calculate the global frequent itemsets from different sites and preserves the privacy for each site.