Organization and maintenance of large ordered indexes

Bayer, Rudolf; McCreight, Edward M.

doi:10.1007/bf00288683

Cited by 1,060 publications

(66 citation statements)

References 3 publications

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“…Any adequate data structure can be used to store the sorted lists, the choice being based on practical considerations. Normally some flavor of B-tree [21] should be the best solution for most database applications. In our test implementations, for the sake of simplicity, we have chosen a two-level indexing, with the first level fitting entirely in main memory.…”

Section: Discussionmentioning

confidence: 99%

High-dimensional descriptor indexing for large multimedia databases

Valle¹,

Cord²,

Philipp‐Foliguet³

2008

Proceedings of the 17th ACM Conference on Information and Knowledge Management

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In this paper we address the subject of large multimedia database indexing for content-based retrieval.We introduce multicurves, a new scheme for indexing highdimensional descriptors. This technique, based on the simultaneous use of moderate-dimensional space-filling curves, has as main advantages the ability to handle high-dimensional data (100 dimensions and over), to allow the easy maintenance of the indexes (inclusion and deletion of data), and to adapt well to secondary storage, thus providing scalability to huge databases (millions, or even thousands of millions of descriptors).We use multicurves to perform the approximate k nearest neighbors search with a very good compromise between precision and speed. The evaluation of multicurves, carried out on large databases, demonstrates that the strategy compares well to other up-to-date k nearest neighbor search strategies.We also test multicurves on the real-world application of image identification for cultural institutions. In this application, which requires the fast search of a large amount of local descriptors, multicurves allows a dramatic speed-up in comparison to the brute-force strategy of sequential search, without any noticeable precision loss.

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Section: Discussionmentioning

confidence: 99%

High-dimensional descriptor indexing for large multimedia databases

Valle¹,

Cord²,

Philipp‐Foliguet³

2008

Proceedings of the 17th ACM Conference on Information and Knowledge Management

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“…The result of function assignment is a list of functions with the corresponding read counts. The COG-eggNog scheme of functional descriptors [17], [18] is used for functional assignments, in conjunction with a pre-calculated database file that uses the B-tree index [33] for fast function retrieval. The B-tree index algorithm was implemented in C. Its implementation is part of a C library developed by our group and is freely available at https://code.google.com/p/bioc/.…”

Section: Methodsmentioning

confidence: 99%

Fast and Sensitive Alignment of Microbial Whole Genome Sequencing Reads to Large Sequence Datasets on a Desktop PC: Application to Metagenomic Datasets and Pathogen Identification

Vera

Ligeti

2014

PLoS ONE

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Next generation sequencing (NGS) of metagenomic samples is becoming a standard approach to detect individual species or pathogenic strains of microorganisms. Computer programs used in the NGS community have to balance between speed and sensitivity and as a result, species or strain level identification is often inaccurate and low abundance pathogens can sometimes be missed. We have developed Taxoner, an open source, taxon assignment pipeline that includes a fast aligner (e.g. Bowtie2) and a comprehensive DNA sequence database. We tested the program on simulated datasets as well as experimental data from Illumina, IonTorrent, and Roche 454 sequencing platforms. We found that Taxoner performs as well as, and often better than BLAST, but requires two orders of magnitude less running time meaning that it can be run on desktop or laptop computers. Taxoner is slower than the approaches that use small marker databases but is more sensitive due the comprehensive reference database. In addition, it can be easily tuned to specific applications using small tailored databases. When applied to metagenomic datasets, Taxoner can provide a functional summary of the genes mapped and can provide strain level identification. Taxoner is written in C for Linux operating systems. The code and documentation are available for research applications at http://code.google.com/p/taxoner.

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“…New hybrid indexing strategies, enhancements, variations and compositions of existing concepts, like the B-Tree [1] or the R-Tree [2] have been proposed to address Contents lists available at ScienceDirect journal homepage: www.elsevier.com/locate/jvlc performance issues on heterogeneous data. Approaches are present treating terms differently according to the frequency like [3].…”

Section: Related Workmentioning

confidence: 99%