7,640 research outputs found
An extension of SPARQL for expressing qualitative preferences
In this paper we present SPREFQL, an extension of the SPARQL language that
allows appending a PREFER clause that expresses "soft" preferences over the
query results obtained by the main body of the query. The extension does not
add expressivity and any SPREFQL query can be transformed to an equivalent
standard SPARQL query. However, clearly separating preferences from the "hard"
patterns and filters in the WHERE clause gives queries where the intention of
the client is more cleanly expressed, an advantage for both human readability
and machine optimization. In the paper we formally define the syntax and the
semantics of the extension and we also provide empirical evidence that
optimizations specific to SPREFQL improve run-time efficiency by comparison to
the usually applied optimizations on the equivalent standard SPARQL query.Comment: Accepted to the 2017 International Semantic Web Conference, Vienna,
October 201
Distributed top-k aggregation queries at large
Top-k query processing is a fundamental building block for efficient ranking in a large number of applications. Efficiency is a central issue, especially for distributed settings, when the data is spread across different nodes in a network. This paper introduces novel optimization methods for top-k aggregation queries in such distributed environments. The optimizations can be applied to all algorithms that fall into the frameworks of the prior TPUT and KLEE methods. The optimizations address three degrees of freedom: 1) hierarchically grouping input lists into top-k operator trees and optimizing the tree structure, 2) computing data-adaptive scan depths for different input sources, and 3) data-adaptive sampling of a small subset of input sources in scenarios with hundreds or thousands of query-relevant network nodes. All optimizations are based on a statistical cost model that utilizes local synopses, e.g., in the form of histograms, efficiently computed convolutions, and estimators based on order statistics. The paper presents comprehensive experiments, with three different real-life datasets and using the ns-2 network simulator for a packet-level simulation of a large Internet-style network
A Data Transformation System for Biological Data Sources
Scientific data of importance to biologists in the Human Genome Project resides not only in conventional databases, but in structured files maintained in a number of different formats (e.g. ASN.1 and ACE) as well a.s sequence analysis packages (e.g. BLAST and FASTA). These formats and packages contain a number of data types not found in conventional databases, such as lists and variants, and may be deeply nested. We present in this paper techniques for querying and transforming such data, and illustrate their use in a prototype system developed in conjunction with the Human Genome Center for Chromosome 22. We also describe optimizations performed by the system, a crucial issue for bulk data
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