A new ontology-based similarity approach for measuring caching coverages provided by mediation systems

Most mediation systems use a caching policy in order to overcome their performance challenges. One of the most widely adopted strategies is known as semantic caching. Semantic caches are called so because they store the descriptions of all submitted queries. Although they may seem to be based on semantics because of their name, this is not really the case. In fact, they actually compare the syntax of the cached queries to the syntax of the new query to retrieve responses from the cache. This can lead to significant delays, especially if multiple requests are stored in the cache. In this work, we propose a new semantic approach based on ontologies to compute the semantic similarity between two given queries, and we provide also a new algorithm to filter all regions of the cache that do not semantically cover a user query. In this way, the use of the cache would be optimal and fast at the same time, despite the large number of regions in the cache. In fact, only the most beneficial regions will be processed to retrieve data from the cache.

Automated summary

Most mediation systems use caching policies, with semantic caching being a widely adopted strategy. However, the current semantic caching approach compares syntax rather than semantics, leading to delays when multiple requests are stored in the cache. This work proposes a new ontology-based semantic approach and algorithm to filter regions in the cache that do not semantically cover a user query, optimizing cache usage for faster retrieval.