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.