  Caching search results is employed in information retrieval systems to
expedite query processing and reduce back-end server workload. Motivated by the
observation that queries belonging to different topics have different
temporal-locality patterns, we investigate a novel caching model called STD
(Static-Topic-Dynamic cache). It improves traditional SDC (Static-Dynamic
Cache) that stores in a static cache the results of popular queries and manages
the dynamic cache with a replacement policy for intercepting the temporal
variations in the query stream. Our proposed caching scheme includes another
layer for topic-based caching, where the entries are allocated to different
topics (e.g., weather, education). The results of queries characterized by a
topic are kept in the fraction of the cache dedicated to it. This permits to
adapt the cache-space utilization to the temporal locality of the various
topics and reduces cache misses due to those queries that are neither
sufficiently popular to be in the static portion nor requested within
short-time intervals to be in the dynamic portion. We simulate different
configurations for STD using two real-world query streams. Experiments
demonstrate that our approach outperforms SDC with an increase up to 3% in
terms of hit rates, and up to 36% of gap reduction w.r.t. SDC from the
theoretical optimal caching algorithm.
