An Aggregation Cache Replacement Algorithm Based on Ontology Clustering

This paper describes the theory, implementation, and experimental evaluation of an Aggregation Cache Replacement （ ACR ） algorithm. By considering application background, carefully choosing weight values, using a special formula to calculate the similarity, and clustering ontologies by similarity for getting more embedded deep relations, ACR combines the ontology similarity with the value of object and decides which object is to be replaced. We demonstrate the usefulness of ACR through experiments. （a） It is found that the aggregation tree is created wholly differently according to the application cases. Therefore, clustering can direct the content adaptation more accurately according to the user perception and can satisfy the user with different preferences. （b） After comparing this new method with widely-used algorithm Last-Recently-Used （LRU） and First-in-First-out （FIFO） method, it is found that ACR outperforms the later two in accuracy and usability. （c） It has a better semantic explanation and makes adaptation more personalized and more precise.

An Aggregation Cache Replacement algorithm based on ontology clustering

This paper describes the theory, implementation, and experimental evaluation of an Aggregation Cache Replacement (ACR) algorithm. By considering application background, carefully choosing weight values, using a special formula to calculate the similarity, and clustering ontologies by similarity for getting more embedded deep relations, ACR combines the ontology similarity with the value of object and decides which object is to be replaced. We demonstrate the usefulness of ACR through experiments. @ It is found that the aggregation tree is created wholly differently according to the application cases. Therefore, clustering can direct the content adaptation more accurately according to the user perception and can satisfy the user with different preferences. ß After comparing this new method with widely-used algorithm Last-Recently-Used (LRU) and First-in-First-out (FIFO) method, it is found that ACR outperforms the later two in accuracy and usability. © It has a better semantic explanation and makes adaptation more personalized and more precise.