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

Efficiency and effectiveness of query processing in cluster-based retrieval

Cataloged from PDF version of article.Our research shows that for large databases, without considerable additional storage overhead, cluster-based retrieval (CBR) can compete with the time efficiency and effectiveness of the inverted index-based full search (FS). The proposed CBR method employs a storage structure that blends the cluster membership information into the inverted file posting lists. This approach significantly reduces the cost of similarity calculations for document ranking during query processing and improves efficiency. For example, in terms of in-memory computations, our new approach can reduce query processing time to 39% of FS. The experiments confirm that the approach is scalable and system performance improves with increasing database size. In the experiments, we use the cover coefficient-based clustering methodology ((CM)-M-3), and the Financial Times database of TREC containing 210 158 documents of size 564 MB defined by 229 748 terms with total of 29 545 234 inverted index elements. This study provides CBR efficiency and effectiveness experiments using the largest corpus in an environment that employs no user interaction or user behavior assumption for clustering. (C) 2003 Elsevier Ltd. All rights reserved

Overview of the INEX 2009 XML Mining Track: Clustering and classification of XML documents

This report explains the objectives, datasets and evaluation criteria of both the clustering and classification tasks set in the INEX 2009 XML Mining track. The report also describes the approaches and results obtained by the different participants

The CUBRIK Project: Human-enhanced time-aware multimedia search:

The Cubrik Project is an Integrated Project of the 7th Frame- work Programme that aims at contributing to the multimedia search domain by opening the architecture of multimedia search engines to the integration of open source and third party content annotation and query processing components, and by exploiting the contribution of humans and communities in all the phases of multimedia search, from content processing to query processing and relevance feedback processing. The CUBRIK presentation will showcase the architectural concept and scientific background of the project and demonstrate an initial scenario of human-enhanced content and query processing pipeline