21,750 research outputs found
Entropy-scaling search of massive biological data
Many datasets exhibit a well-defined structure that can be exploited to
design faster search tools, but it is not always clear when such acceleration
is possible. Here, we introduce a framework for similarity search based on
characterizing a dataset's entropy and fractal dimension. We prove that
searching scales in time with metric entropy (number of covering hyperspheres),
if the fractal dimension of the dataset is low, and scales in space with the
sum of metric entropy and information-theoretic entropy (randomness of the
data). Using these ideas, we present accelerated versions of standard tools,
with no loss in specificity and little loss in sensitivity, for use in three
domains---high-throughput drug screening (Ammolite, 150x speedup), metagenomics
(MICA, 3.5x speedup of DIAMOND [3,700x BLASTX]), and protein structure search
(esFragBag, 10x speedup of FragBag). Our framework can be used to achieve
"compressive omics," and the general theory can be readily applied to data
science problems outside of biology.Comment: Including supplement: 41 pages, 6 figures, 4 tables, 1 bo
siEDM: an efficient string index and search algorithm for edit distance with moves
Although several self-indexes for highly repetitive text collections exist,
developing an index and search algorithm with editing operations remains a
challenge. Edit distance with moves (EDM) is a string-to-string distance
measure that includes substring moves in addition to ordinal editing operations
to turn one string into another. Although the problem of computing EDM is
intractable, it has a wide range of potential applications, especially in
approximate string retrieval. Despite the importance of computing EDM, there
has been no efficient method for indexing and searching large text collections
based on the EDM measure. We propose the first algorithm, named string index
for edit distance with moves (siEDM), for indexing and searching strings with
EDM. The siEDM algorithm builds an index structure by leveraging the idea
behind the edit sensitive parsing (ESP), an efficient algorithm enabling
approximately computing EDM with guarantees of upper and lower bounds for the
exact EDM. siEDM efficiently prunes the space for searching query strings by
the proposed method, which enables fast query searches with the same guarantee
as ESP. We experimentally tested the ability of siEDM to index and search
strings on benchmark datasets, and we showed siEDM's efficiency.Comment: 23 page
Video browsing interfaces and applications: a review
We present a comprehensive review of the state of the art in video browsing and retrieval systems, with special emphasis on interfaces and applications. There has been a significant increase in activity (e.g., storage, retrieval, and sharing) employing video data in the past decade, both for personal and professional use. The ever-growing amount of video content available for human consumption and the inherent characteristics of video data—which, if presented in its raw format, is rather unwieldy and costly—have become driving forces for the development of more effective solutions to present video contents and allow rich user interaction. As a result, there are many contemporary research efforts toward developing better video browsing solutions, which we summarize. We review more than 40 different video browsing and retrieval interfaces and classify them into three groups: applications that use video-player-like interaction, video retrieval applications, and browsing solutions based on video surrogates. For each category, we present a summary of existing work, highlight the technical aspects of each solution, and compare them against each other
Universal Indexes for Highly Repetitive Document Collections
Indexing highly repetitive collections has become a relevant problem with the
emergence of large repositories of versioned documents, among other
applications. These collections may reach huge sizes, but are formed mostly of
documents that are near-copies of others. Traditional techniques for indexing
these collections fail to properly exploit their regularities in order to
reduce space.
We introduce new techniques for compressing inverted indexes that exploit
this near-copy regularity. They are based on run-length, Lempel-Ziv, or grammar
compression of the differential inverted lists, instead of the usual practice
of gap-encoding them. We show that, in this highly repetitive setting, our
compression methods significantly reduce the space obtained with classical
techniques, at the price of moderate slowdowns. Moreover, our best methods are
universal, that is, they do not need to know the versioning structure of the
collection, nor that a clear versioning structure even exists.
We also introduce compressed self-indexes in the comparison. These are
designed for general strings (not only natural language texts) and represent
the text collection plus the index structure (not an inverted index) in
integrated form. We show that these techniques can compress much further, using
a small fraction of the space required by our new inverted indexes. Yet, they
are orders of magnitude slower.Comment: This research has received funding from the European Union's Horizon
2020 research and innovation programme under the Marie Sk{\l}odowska-Curie
Actions H2020-MSCA-RISE-2015 BIRDS GA No. 69094
Scalable Similarity Search for Molecular Descriptors
Similarity search over chemical compound databases is a fundamental task in
the discovery and design of novel drug-like molecules. Such databases often
encode molecules as non-negative integer vectors, called molecular descriptors,
which represent rich information on various molecular properties. While there
exist efficient indexing structures for searching databases of binary vectors,
solutions for more general integer vectors are in their infancy. In this paper
we present a time- and space- efficient index for the problem that we call the
succinct intervals-splitting tree algorithm for molecular descriptors (SITAd).
Our approach extends efficient methods for binary-vector databases, and uses
ideas from succinct data structures. Our experiments, on a large database of
over 40 million compounds, show SITAd significantly outperforms alternative
approaches in practice.Comment: To be appeared in the Proceedings of SISAP'1
Digital Image Access & Retrieval
The 33th Annual Clinic on Library Applications of Data Processing, held at the University of Illinois at Urbana-Champaign in March of 1996, addressed the theme of "Digital Image Access & Retrieval." The papers from this conference cover a wide range of topics concerning digital imaging technology for visual resource collections. Papers covered three general areas: (1) systems, planning, and implementation; (2) automatic and semi-automatic indexing; and (3) preservation with the bulk of the conference focusing on indexing and retrieval.published or submitted for publicatio
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