Improving Search Engine Performance Through Dynamic Caching

Abstract

Web search engines process several millions of queries per second over several billions of documents. Without any optimization, this process can be very expensive in terms of processing times. In this regard, appropriate use of computing power is essential. One way to tackle this problem is through the use of caching mechanisms. Keep in mind, most research based on caching mechanisms uses repetitive queries-it means queries syntactically equals-to conform caches. Furthermore, the universe of repetitive queries is small in comparison with a set of similar semantically queries. This paper presents a dynamic cache that relies on an online algorithm, which performs a semantic match between the user's query and queries stored in the cache. Broadly speaking, the algorithm employs a priority queue, where popular queries are stored along with their relevant documents. Empirical results show that our proposed approach improves the response times and precision. Moreover, the use of semantically related keywords proves to be a key contribution that had been overlooked in previous research.