Browsing by Subject "Static caching"

Now showing 1 - 4 of 4

Open Access
A five-level static cache architecture for web search engines
(Elsevier Ltd, 2012) Ozcan, R.; Altingovde, I. S.; Cambazoglu, B. B.; Junqueira, F. P.; Ulusoy, Özgür
Caching is a crucial performance component of large-scale web search engines, as it greatly helps reducing average query response times and query processing workloads on backend search clusters. In this paper, we describe a multi-level static cache architecture that stores five different item types: query results, precomputed scores, posting lists, precomputed intersections of posting lists, and documents. Moreover, we propose a greedy heuristic to prioritize items for caching, based on gains computed by using items' past access frequencies, estimated computational costs, and storage overheads. This heuristic takes into account the inter-dependency between individual items when making its caching decisions, i.e.; after a particular item is cached, gains of all items that are affected by this decision are updated. Our simulations under realistic assumptions reveal that the proposed heuristic performs better than dividing the entire cache space among particular item types at fixed proportions. © 2010 Elsevier Ltd. All rights reserved.
Open Access
A machine learning approach for result caching in web search engines
(Elsevier, 2017) Kucukyilmaz T.; Cambazoglu, B. B.; Aykanat, Cevdet; Baeza-Yates R.
A commonly used technique for improving search engine performance is result caching. In result caching, precomputed results (e.g., URLs and snippets of best matching pages) of certain queries are stored in a fast-access storage. The future occurrences of a query whose results are already stored in the cache can be directly served by the result cache, eliminating the need to process the query using costly computing resources. Although other performance metrics are possible, the main performance metric for evaluating the success of a result cache is hit rate. In this work, we present a machine learning approach to improve the hit rate of a result cache by facilitating a large number of features extracted from search engine query logs. We then apply the proposed machine learning approach to static, dynamic, and static-dynamic caching. Compared to the previous methods in the literature, the proposed approach improves the hit rate of the result cache up to 0.66%, which corresponds to 9.60% of the potential room for improvement. © 2017 Elsevier Ltd
Open Access
Static query result caching revisited
(ACM, 2008-04) Özcan, Rıfat; Altıngövde, İsmail Şengör; Ulusoy, Özgür
Query result caching is an important mechanism for search engine efficiency. In this study, we first review several query features that are used to determine the contents of a static result cache. Next, we introduce a new feature that more accurately represents the popularity of a query by measuring the stability of query frequency over a set of time intervals. Experimental results show that this new feature achieves hit ratios better than those of the previously proposed features.
Open Access
Utilization of navigational queries for result presentation and caching in search engines
(ACM, 2008-10) Özcan, Rıfat; Altıngövde, İsmail Şengör; Ulusoy, Özgür
We propose result page models with varying granularities for navigational queries and show that this approach provides a better utilization of cache space and reduces bandwidth requirements.