Static query result caching revisited

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

Query Load Balancing by Caching Search Results in Peer-to-Peer Information Retrieval Networks

For peer-to-peer web search engines it is important to keep the delay between receiving a query and providing search results within an acceptable range for the end user. How to achieve this remains an open challenge. One way to reduce delays is by caching search results for queries and allowing peers to access each others cache. In this paper we explore the limitations of search result caching in large-scale peer-to-peer information retrieval networks by simulating such networks with increasing levels of realism. We find that cache hit ratios of at least thirty-three percent are attainable

Efficient result caching mechanisms in search engines

Ankara : The Department of Computer Engineering and the Graduate School of Engineering and Science of Bilkent University, 2014.Thesis (Master's) -- Bilkent University, 2014.Includes bibliographical references leaves 60-63.The performance of a search engine depends on its components such as crawler, indexer and processor. The query latency, accuracy and recency of the results play crucial role in determining the performance. High performance can be provided with powerful hardware in the data center, but keeping the operational costs restrained is mandatory for search engines for commercial durability. This thesis focuses on techniques to boost the performance of search engines by means of reducing both the number of queries issued to the backend and the cost to process a query stream. This can be accomplished by taking advantage of the temporal locality of the queries. Caching the result for a recently issued query removes the need to reprocess this query when it is issued again by the same or different user. Therefore, deploying query result cache decreases the load on the resources of the search engine which increases the processing power. The main objective of this thesis is to improve search engine performance by enhancing productivity of result cache. This is done by endeavoring to maximize the cache hit rate and minimizing the processing cost by using the per query statistics such as frequency, timestamp and cost. While providing high hit rates and low processing costs improves performance, the freshness of the queries in the cache has to be considered as well for user satisfaction. Therefore, a variety of techniques are examined in this thesis to bound the staleness of cache results without blasting the backend with refresh queries. The offered techniques are demonstrated to be efficient by using real query log data from a commercial search engine.Sazoğlu, Fethi BurakM.S

A financial cost metric for result caching

Web search engines cache results of frequent and/or recent queries. Result caching strategies can be evaluated using different metrics, hit rate being the most well-known. Recent works take the processing overhead of queries into account when evaluating the performance of result caching strategies and propose cost-aware caching strategies. In this paper, we propose a financial cost metric that goes one step beyond and takes also the hourly electricity prices into account when computing the cost. We evaluate the most well-known static, dynamic, and hybrid result caching strategies under this new metric. Moreover, we propose a financial-cost-aware version of the well-known LRU strategy and show that it outperforms the original LRU strategy in terms of the financial cost metric. Copyright © 2013 ACM

DotSlash: Providing Dynamic Scalability to Web Applications with On-demand Distributed Query Result Caching

Scalability poses a significant challenge for today's web applications, mainly due to the large population of potential users. To effectively address the problem of short-term dramatic load spikes caused by web hotspots, we developed a self-configuring and scalable rescue system called DotSlash. The primary goal of our system is to provide dynamic scalability to web applications by enabling a web site to obtain resources dynamically, and use them autonomically without any administrative intervention. To address the database server bottleneck, DotSlash allows a web site to set up on-demand distributed query result caching, which greatly reduces the database workload for read mostly databases, and thus increases the request rate supported at a DotSlash-enabled web site. The novelty of our work is that our query result caching is on demand, and operated based on load conditions. The caching remains inactive as long as the load is normal, but is activated once the load is heavy. This approach offers good data consistency during normal load situations, and good scalability with relaxed data consistency for heavy load periods. We have built a prototype system for the widely used LAMP configuration, and evaluated our system using the RUBBoS bulletin board benchmark. Experiments show that a DotSlash-enhanced web site can improve the maximum request rate supported by a factor of 5 using 8 rescue servers for the RUBBoS submission mix, and by a factor of 10 using 15 rescue servers for the RUBBoS read-only mix

Second chance: A hybrid approach for dynamic result caching and prefetching in search engines

Cataloged from PDF version of article.Web search engines are known to cache the results of previously issued queries. The stored results typically contain the document summaries and some data that is used to construct the final search result page returned to the user. An alternative strategy is to store in the cache only the result document IDs, which take much less space, allowing results of more queries to be cached. These two strategies lead to an interesting trade-off between the hit rate and the average query response latency. In this work, in order to exploit this trade-off, we propose a hybrid result caching strategy where a dynamic result cache is split into two sections: an HTML cache and a docID cache. Moreover, using a realistic cost model, we evaluate the performance of different result prefetching strategies for the proposed hybrid cache and the baseline HTML-only cache. Finally, we propose a machine learning approach to predict singleton queries, which occur only once in the query stream. We show that when the proposed hybrid result caching strategy is coupled with the singleton query predictor, the hit rate is further improved. © 2013 ACM

Living Knowledge

Diversity, especially manifested in language and knowledge, is a function of local goals, needs, competences, beliefs, culture, opinions and personal experience. The Living Knowledge project considers diversity as an asset rather than a problem. With the project, foundational ideas emerged from the synergic contribution of different disciplines, methodologies (with which many partners were previously unfamiliar) and technologies flowed in concrete diversity-aware applications such as the Future Predictor and the Media Content Analyser providing users with better structured information while coping with Web scale complexities. The key notions of diversity, fact, opinion and bias have been defined in relation to three methodologies: Media Content Analysis (MCA) which operates from a social sciences perspective; Multimodal Genre Analysis (MGA) which operates from a semiotic perspective and Facet Analysis (FA) which operates from a knowledge representation and organization perspective. A conceptual architecture that pulls all of them together has become the core of the tools for automatic extraction and the way they interact. In particular, the conceptual architecture has been implemented with the Media Content Analyser application. The scientific and technological results obtained are described in the following

Importance of Explicit Vectorization for CPU and GPU Software Performance

Much of the current focus in high-performance computing is on multi-threading, multi-computing, and graphics processing unit (GPU) computing. However, vectorization and non-parallel optimization techniques, which can often be employed additionally, are less frequently discussed. In this paper, we present an analysis of several optimizations done on both central processing unit (CPU) and GPU implementations of a particular computationally intensive Metropolis Monte Carlo algorithm. Explicit vectorization on the CPU and the equivalent, explicit memory coalescing, on the GPU are found to be critical to achieving good performance of this algorithm in both environments. The fully-optimized CPU version achieves a 9x to 12x speedup over the original CPU version, in addition to speedup from multi-threading. This is 2x faster than the fully-optimized GPU version.Comment: 17 pages, 17 figure