this paper appears in [15], and updated information is available at http://cis.poly.edu/westlab/odissea/

Compression reduces both the size of indexes and the time needed to evaluate queries. In this paper, we revisit the compression of inverted lists of document postings that store the position and frequency of indexed terms, considering two approaches to improving retrieval efficiency: better implementation and better choice of integer compression schemes. First, we propose several simple optimisations to well-known integer compression schemes, and show experimentally that these lead to significant reductions in time. Second, we explore the impact of choice of compression scheme on retrieval efficiency. In experiments on large collections of data, we show two surprising results: use of simple byte-aligned codes halves the query evaluation time compared to the most compact Golomb-Rice bitwise compression schemes; and, even when an index fits entirely in memory, byte-aligned codes result in faster query evaluation than does an uncompressed index, emphasising that the cost of transferring data from memory to the CPU cache is less for an appropriately compressed index than for an uncompressed index. Moreover, byte-aligned schemes have only a modest space overhead: the most compact schemes result in indexes that are around 10 % of the size of the collection, while a byte-aligned scheme is around 13%. We conclude that fast byte-aligned codes should be used to store integers in inverted lists.

We introduce static index pruning methods that significantly reduce the index size in information retrieval systems. We investigate uniform and term-based methods that each remove selected entries from the index and yet have only a minor effect on retrieval results. In uniform pruning, there is a fixed cutoff threshold, and all index entries whose contribution to relevance scores is bounded above by a given threshold are removed from the index. In term-based pruning, the cutoff threshold is determined for each term, and thus may vary from term to term. We give experimental evidence that for each level of compression, term-based pruning outperforms uniform pruning, under various measures of precision. We present theoretical and experimental evidence that under our term-based pruning scheme, it is possible to prune the index greatly and still get retrieval results that are almost as good as those based on the full index. Topic areas: indexing, compression 1.

This paper presents a novel engine, coined TopX, for efficient ranked retrieval of XML documents over semistructured but nonschematic data collections. The algorithm follows the paradigm of threshold algorithms for top-k query processing with a focus on inexpensive sequential accesses to index lists and only a few judiciously scheduled random accesses. The difficulties in applying...

This paper addresses the efficient processing of top-k queries in wide-area distributed data repositories where the index lists for the attribute values (or text terms) of a query are distributed across a number of data peers and the computational costs include network latency, bandwidth consumption, and local peer work. We present KLEE, a novel algorithmic framework for distributed top-k queries, designed for high performance and flexibility. KLEE makes a strong case for approximate top-k algorithms over widely distributed data sources. It shows how great gains in efficiency can be enjoyed at low result-quality penalties. Further, KLEE affords the query-initiating peer the flexibility to trade-off result quality and expected performance and to trade-off the number of communication phases engaged during query execution versus network bandwidth performance. We have implemented KLEE and related algorithms and conducted a comprehensive performance evaluation. Our evaluation employed real-world and synthetic large, web-data collections, and query benchmarks. Our experimental results show that KLEE can achieve major performance gains in terms of network bandwidth, query response times, and much lighter peer loads, all with small errors in result precision and other result-quality measures.

Large web search engines have to answer thousands of queries per second with interactive response times. A major factor in the cost of executing a query is given by the lengths of the inverted lists for the query terms, which increase with the size of the document collection and are often in the range of many megabytes. To address this issue, IR and database researchers have proposed pruning techniques that compute or approximate term-based ranking functions without scanning over the full inverted lists.

Queries to text collections are resolved by ranking the documents in the collection and returning the highest-scoring documents to the user. An alternative retrieval method is to rank passages, that is, short fragments of documents, a strategy that can improve effectiveness and identify relevant material in documents that are too large for users to consider as a whole. However, ranking of passages can considerably increase retrieval costs. In this paper we explore alternative query evaluation techniques, and develop new techniques for evaluating queries on passages. We show experimentally that, appropriately implemented, effective passage retrieval is practical in limited memory on a desktop machine. Compared to passage ranking with adaptations of current document ranking algorithms, our new "DO-TOS" passage ranking algorithm requires only a fraction of the resources, at the cost of a small loss of effectiveness.

In this paper, we study query processing in a distributed text database. The novelty is a real distributed architecture implementation that offers concurrent query service. The distributed system adopts a network of workstations model and the client-server paradigm. The document collection is indexed with an inverted file. We adopt two distinct strategies of index partitioning in the distributed system, namely local index partitioning and global index partitioning. In both strategies, documents are ranked using the vector space model along with a document filtering technique for fast ranking. We evaluate and compare the impact of the two index partitioning strategies on query processing performance. Experimental results on retrieval efficiency show that, within our framework, the global index partitioning outperforms the local index partitioning. 1.

Large web search engines have to answer thousands of queries per second with interactive response times. Due to the sizes of the data sets involved, often in the range of multiple terabytes, a single query may require the processing of hundreds of megabytes or more of index data. To keep up with this immense workload, large search engines employ clusters of hundreds or thousands of machines, and a number of techniques such as caching, index compression, and index and query pruning are used to improve scalability. In particular, two-level caching techniques cache results of repeated identical queries at the frontend, while index data for frequently used query terms are cached in each node at a lower level. We propose and evaluate a three-level caching scheme that adds an intermediate level of caching for additional performance gains. This intermediate level attempts to exploit frequently occurring pairs of terms by caching intersections or projections of the corresponding inverted lists. We propose and study several offline and online algorithms for the resulting weighted caching problem, which turns out to be surprisingly rich in structure. Our experimental evaluation based on a large web crawl and real search engine query log shows significant performance gains for the best schemes, both in isolation and in combination with the other caching levels. We also observe that a careful selection of cache admission and eviction policies is crucial for best overall performance.

Exhaustive evaluation of ranked queries can be expensive, particularly when only a small subset of the overall ranking is required, or when queries contain common terms. This concern gives rise to techniques for dynamic query pruning, that is, methods for eliminating redundant parts of the usual exhaustive evaluation, yet still generating a demonstrably “good enough ” set of answers to the query. In this work we propose new pruning methods that make use of impact-sorted indexes. Compared to exhaustive evaluation, the new methods reduce the amount of computation performed, reduce the amount of memory required for accumulators, reduce the amount of data transferred from disk, and at the same time allow performance guarantees in terms of precision and mean average precision. These strong claims are backed by experiments using the TREC Terabyte collection and queries. Categories and Subject Descriptors H.3.1 [Information Storage and Retrieval]: Content analysis and indexing – indexing methods; H.3.2 [Information Storage and Retrieval]: