Paper Title
Ontology Based Data Integration To Improve Data Quality With Cache

In today’s world the amount of data is increasing tremendously. In order to analyze data and make decisions, data residing at different sources are integrated. Data integration is the process of integrating data from different data sources. Data federation is a data integration strategy used to create integrated virtual view. The data integration process involves schema matching, duplicate detection and data fusion. The semantic heterogeneity is resolved using ontology. The data conflicts that occur during the data integration are resolved using the Enhanced Markov Logic Network (EMLN) to improve the quality of the data . To improve the performance of system cache is implemented. Enhanced LRU with Frequency (ELRLF) algorithm is used for page replacement in cache. This cache technique used to reduce number of times scanning of local ontology. Virtual table is created to populate the result of integration service. A new cache optimization algorithm, Enhanced LRU with frequency is used to improve the response time and recall rate. Enhanced LRU with frequency uses the hash map with skip list data structure to perform efficient searching of data item in cache. Ontology based data integration using cache support decision making for disaster management application. Keywords- Data integration, Ontology, Semantic heterogeneity, Data quality, Cache optimization.