Our research shows that for large databases, without considerable additional storage overhead, clusterbased retrieval (CBR) can compete with the time efficiency and effectiveness of the inverted indexbased 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 (C 3 M), and the Financial Times database of TREC-4 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.

A recent ''third wave'' of neural network (NN) approaches now delivers state-ofthe-art performance in many machine learning tasks, spanning speech recognition, computer vision, and natural language processing. Because these modern NNs often comprise multiple interconnected layers, work in this area is often referred to as deep learning. Recent years have witnessed an explosive growth of research into NN-based approaches to information retrieval (IR). A significant body of work has now been created. In this paper,

________________________________________________________________________We propose a unique cluster-based retrieval (CBR) strategy using a new cluster-skipping inverted file for improving query processing efficiency. The new inverted file incorporates cluster membership and centroid information along with the usual document information into a single structure. In our incremental-CBR strategy, during query evaluation both best(-matching) clusters and best(-matching) documents of such clusters are computed together with a single posting list access per query term. As we switch from term to term, best clusters are recomputed and can dynamically change. During query-document matching, only relevant portions of the posting lists corresponding to the best clusters are considered and the rest is skipped. The proposed approach is essentially tailored for environments where inverted files are compressed, and provides substantial efficiency improvements while yielding comparable or sometimes better effectiveness figures. Our experiments with various collections show that, the incremental-CBR strategy using compressed cluster-skipping inverted file significantly improves CPU time efficiency regardless of the query length. The new compressed inverted file imposes an acceptable storage overhead in comparison to a typical inverted file. We also show that our approach scales well with the collection size.

Abstract. Search engines and large scale IR systems need to cache query results for efficiency and scalability purposes. In this study, we propose to explicitly incorporate the query costs in the static caching policy. To this end, a query's cost is represented by its execution time, which involves CPU time to decompress the postings and compute the query-document similarities to obtain the final top-N answers. Simulation results using a large Web crawl data and a real query log reveal that the proposed strategy improves overall system performance in terms of the total query execution time.

Cataloged from PDF version of article.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. (C) 2010 Elsevier Ltd. All rights reserved