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Recently, the compacted de Bruijn graph (cDBG) of complete genome sequences was successfully used in read mapping due to its ability to deal with the repetitions in genomes. However, current approaches are not flexible enough to fit frequently building the graphs with different $k$ k -mer lengths. Instead of building the graph directly, how can we build the compacted de Bruijin graph of longer $k$ k -mer based on the one of short $k$ k -mer? In this article, we present StLiter, a novel algorithm to build the compacted de Bruijn graph either directly from genome sequences or indirectly based on the graph of a short $k$ k -mer. For 100 simulated human genomes, StLiter can construct the graph of $k$ k -mer length 15-18 in 2.5-3.2 hours with maximal ∼70GB memory in the case of without considering the reverese complements of the reference genomes. And it costs 4.5-5.9 hours when considering the reverse complements. In experiments, we compared StLiter with TwoPaCo, the state-of-art method for building the graph, on 4 datasets. For $k$ k -mer length 15-18, StLiter can build the graph 5-9 times faster than TwoPaCo using less maximal memory cost. For $k$ k -mer length larger than 18, given the graph of a short $({\mathrm{k}}$ (k - ${\mathrm{x}})$ x) -mer, such as ${\mathrm{x}}$ x = 1-2, compared with TwoPaCo building the graph directly, StLiter can also build the graph more efficiently. The source codes of StLiter can be downloaded from web site https://github.com/BioLab-cz/StLiter. [ABSTRACT FROM AUTHOR]

Skyline queries are important in many application domains. In this paper, we propose a novel structure Skyline Diagram, which given a set of points, partitions the plane into a set of regions, referred to as skyline polyominos. All query points in the same skyline polyomino have the same skyline query results. Similar to $k$ k th-order Voronoi diagram commonly used to facilitate $k$ k nearest neighbor ($k$ k NN) queries, skyline diagram can be used to facilitate skyline queries and many other applications. However, it may be computationally expensive to build the skyline diagram. By exploiting some interesting properties of skyline, we present several efficient algorithms for building the diagram with respect to three kinds of skyline queries, quadrant, global, and dynamic skylines. In addition, we propose an approximate skyline diagram which can significantly reduce the space cost. Experimental results on both real and synthetic datasets show that our algorithms are efficient and scalable. [ABSTRACT FROM AUTHOR]

This work was supported in part by the National Natural Science Fund of China under Grant 61233011, Grant 61374006, Grant 61473086, Grant 61463008, Grant 61703100, Grant 61773118, and Grant 61773117, in part by the Major Program of National Natural Science Foundation of China under Grant 11190015, in part by the Natural Science Foundation of Jiangsu under Grant BK20131300 and Grant BK20170692, in part by the Innovation Fund of Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education, Nanjing University of Science and Technology, under Grant JYB201601, in part by the Innovation Fund of Key Laboratory of Measurement and Control of Complex Systems of Engineering, Southeast University, under Grant MCCSE2017B01, and in part by the Fundamental Research Funds for the Central Universities under Grant 2242016k30009.

In this paper, we deal with the integration of multiple sources of information such as Earth observation (EO) synthetic aperture radar (SAR) images and their metadata, semantic descriptors of the image content, as well as other publicly available geospatial data sources expressed as linked open data for posing complex queries in order to support geospatial data analytics. Our approach lays the foundations for the development of richer tools and applications that focus on EO image analytics using ontologies and linked open data. We introduce a system architecture where a common satellite image product is transformed from its initial format into to actionable intelligence information, which includes image descriptors, metadata, image tiles, and semantic labels resulting in an EO-data model. We also create a SAR image ontology based on our EO-data model and a two-level taxonomy classification scheme of the image content. We demonstrate our approach by linking high-resolution TerraSAR-X images with information from CORINE Land Cover (CLC), Urban Atlas (UA), GeoNames, and OpenStreetMap (OSM), which are represented in the standard triple model of the resource description frameworks (RDFs). [ABSTRACT FROM PUBLISHER]

Databases may be compromised by asking a set of queries which return only statistical information and making inferences about a specific entry from the results of the set of queries. We present methods of constructing the set of queries to compromise a database where all the queries in the set return the maximum of a set of k elements, or all queries return the mean of a set of k elements. In the case of the mean query, all elements involved will be compromised. We also consider bounds on the number of queries required to compromise the database. [ABSTRACT FROM AUTHOR]

Users can compromise data bases by asking a series of questions, even when the data bases are allowed to lie. [ABSTRACT FROM AUTHOR]