iosr-JCE   Volume-9 ~ Issue-3

Abstract: In this paper, we present performance improvement techniques for data retrieval from customized data warehouses for efficient querying and Online Analytical Processing (OLAP) in relation to efficient database and memory management. Different database management techniques, e.g. indexing, partitioning etc. play vital role in efficient memory management. A comparison of data retrieval time for a particular query from a relational database as well as data warehouse database with and without indexing is performed. We show that the application of different database management techniques result faster query execution by reducing data retrieval time. This improved efficiency may increase the efficiency of OLAP operations, which results better data warehouse performance.

Keywords - Data Warehouse, Indexing, OLAP, Partitioning, Querying.

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Abstract: Handling missing attribute values is the greatest challenging process in data analysis. There are so many approaches that can be adopted to handle the missing attributes. In this paper, a comparative analysis is made of an incomplete dataset for future prediction using rough set approach and random tree generation in data mining. The result of simple classification technique (using random tree classifier) is compared with the result of rough set attribute reduction performed based on Rule induction and decision tree. WEKA (Waikato Environment for Knowledge Analysis), a Data Mining tool and ROSE2 (Rough Set Data Explorer), a Rough Set approach tool have been used for the experiment. The result of the experiment shows that the random tree classification algorithm gives promising results with utmost accuracy and produces best decision rule using decision tree for the original incomplete data or with the missing attribute values (i.e. missing attributes are simply ignored). Whereas in rough set approach, the missing attribute values are filled with the most common values of that attribute domain. This paper brings out a conclusion that the missing data simply ignored yields best decision than filling some data in the place of missing attribute value.

Keywords- Random Tree, WEKA, ROSE2, Missing attribute, Incomplete dataset, Classification, Rule Induction, Decision Tree

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Abstract: A resilient execution environment can be developed for a critical application even in the presence of corrupted OS kernel. The attacker tries to capture the application content by corrupting the OS when an application is executing. In previous case the attacker corrupts the OS by injecting code then the application terminates immediately without executing it. In this current system even in the presence of corruption the application is executed with out any interception and it provide a resilient authenticated execution of critical application in entrusted environment by using Virtual Machine Monitor (VMM). VMM is a monitoring technique to monitor all the activities during execution and it is one of the online based recovery schemes to identify any such corruption. It repairs the memory corruption and allows the process for normal execution. VMM solutions generally broadcast into two categories they are memory authentication and memory duplication. Memory authentication is to check the integrity of an application and memory duplication is to rectify the corruption. The proposed system is applied for military application, hospitals, and colleges and for all critical applications. Keywords- Memory corruption, operating systems, security, Virtual machine monitors (VMM).

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