5,181 research outputs found

    A spatial data handling system for retrieval of images by unrestricted regions of user interest

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    The Intelligent Data Management (IDM) project at NASA/Goddard Space Flight Center has prototyped an Intelligent Information Fusion System (IIFS), which automatically ingests metadata from remote sensor observations into a large catalog which is directly queryable by end-users. The greatest challenge in the implementation of this catalog was supporting spatially-driven searches, where the user has a possible complex region of interest and wishes to recover those images that overlap all or simply a part of that region. A spatial data management system is described, which is capable of storing and retrieving records of image data regardless of their source. This system was designed and implemented as part of the IIFS catalog. A new data structure, called a hypercylinder, is central to the design. The hypercylinder is specifically tailored for data distributed over the surface of a sphere, such as satellite observations of the Earth or space. Operations on the hypercylinder are regulated by two expert systems. The first governs the ingest of new metadata records, and maintains the efficiency of the data structure as it grows. The second translates, plans, and executes users' spatial queries, performing incremental optimization as partial query results are returned

    Designing Web-enabled services to provide damage estimation maps caused by natural hazards

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    The availability of building stock inventory data and demographic information is an important requirement for risk assessment studies when attempting to predict and estimate losses due to natural hazards such as earthquakes, storms, floods or tsunamis. The better this information is provided, the more accurate are predictions on damage to structures and lifelines and the better can expected impacts on the population be estimated. When a disaster strikes, a map is often one of the first requirements for answering questions related to location, casualties and damage zones caused by the event. Maps of appropriate scale that represent relative and absolute damage distributions may be of great importance for rescuing lives and properties, and for providing relief. However, this type of maps is often difficult to obtain during the first hours or even days after the occurrence of a natural disaster. The Open Geospatial Consortium Web Services (OWS) Specifications enable access to datasets and services using shared, distributed and interoperable environments through web-enabled services. In this paper we propose the use of OWS in view of these advantages as a possible solution for issues related to suitable dataset acquisition for risk assessment studies. The design of web-enabled services was carried out using the municipality of Managua (Nicaragua) and the development of damage and loss estimation maps caused by earthquakes as a first case study. Four organizations located in different places are involved in this proposal and connected through web services, each one with a specific role

    bdbms -- A Database Management System for Biological Data

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    Biologists are increasingly using databases for storing and managing their data. Biological databases typically consist of a mixture of raw data, metadata, sequences, annotations, and related data obtained from various sources. Current database technology lacks several functionalities that are needed by biological databases. In this paper, we introduce bdbms, an extensible prototype database management system for supporting biological data. bdbms extends the functionalities of current DBMSs to include: (1) Annotation and provenance management including storage, indexing, manipulation, and querying of annotation and provenance as first class objects in bdbms, (2) Local dependency tracking to track the dependencies and derivations among data items, (3) Update authorization to support data curation via content-based authorization, in contrast to identity-based authorization, and (4) New access methods and their supporting operators that support pattern matching on various types of compressed biological data types. This paper presents the design of bdbms along with the techniques proposed to support these functionalities including an extension to SQL. We also outline some open issues in building bdbms.Comment: This article is published under a Creative Commons License Agreement (http://creativecommons.org/licenses/by/2.5/.) You may copy, distribute, display, and perform the work, make derivative works and make commercial use of the work, but, you must attribute the work to the author and CIDR 2007. 3rd Biennial Conference on Innovative Data Systems Research (CIDR) January 710, 2007, Asilomar, California, US

    MODBASE, a database of annotated comparative protein structure models and associated resources.

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    MODBASE (http://salilab.org/modbase) is a database of annotated comparative protein structure models. The models are calculated by MODPIPE, an automated modeling pipeline that relies primarily on MODELLER for fold assignment, sequence-structure alignment, model building and model assessment (http:/salilab.org/modeller). MODBASE currently contains 5,152,695 reliable models for domains in 1,593,209 unique protein sequences; only models based on statistically significant alignments and/or models assessed to have the correct fold are included. MODBASE also allows users to calculate comparative models on demand, through an interface to the MODWEB modeling server (http://salilab.org/modweb). Other resources integrated with MODBASE include databases of multiple protein structure alignments (DBAli), structurally defined ligand binding sites (LIGBASE), predicted ligand binding sites (AnnoLyze), structurally defined binary domain interfaces (PIBASE) and annotated single nucleotide polymorphisms and somatic mutations found in human proteins (LS-SNP, LS-Mut). MODBASE models are also available through the Protein Model Portal (http://www.proteinmodelportal.org/)

    Pyrosequencing analysis of fungal assemblages from geographically distant, disparate soils reveals spatial patterning and a core mycobiome

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    Identifying a soil core microbiome is crucial to appreciate the established microbial consortium, which is not usually subjected to change and, hence, possibly resistant/resilient to disturbances and a varying soil context. Fungi are a major part of soil biodiversity, yet the mechanisms driving their large-scale ecological ranges and distribution are poorly understood. The degree of fungal community overlap among 16 soil samples from distinct ecosystems and distant geographic localities (truffle grounds, a Mediterranean agro-silvo-pastoral system, serpentine substrates and a contaminated industrial area) was assessed by examining the distribution of fungal ITS1 and ITS2 sequences in a dataset of 454 libraries. ITS1 and ITS2 sequences were assigned to 1,660 and 1,393 Operational Taxonomic Units (OTUs; as defined by 97% sequence similarity), respectively. Fungal beta-diversity was found to be spatially autocorrelated. At the level of individual OTUs, eight ITS1 and seven ITS2 OTUs were found in all soil sample groups. These ubiquitous taxa comprised generalist fungi with oligotrophic and chitinolytic abilities, suggesting that a stable core of fungi across the complex soil fungal assemblages is either endowed with the capacity of sustained development in the nutrient-poor soil conditions or with the ability to exploit organic resources (such as chitin) universally distributed in soils

    Generative Street Addresses from Satellite Imagery

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    We describe our automatic generative algorithm to create street addresses from satellite images by learning and labeling roads, regions, and address cells. Currently, 75% of the world’s roads lack adequate street addressing systems. Recent geocoding initiatives tend to convert pure latitude and longitude information into a memorable form for unknown areas. However, settlements are identified by streets, and such addressing schemes are not coherent with the road topology. Instead, we propose a generative address design that maps the globe in accordance with streets. Our algorithm starts with extracting roads from satellite imagery by utilizing deep learning. Then, it uniquely labels the regions, roads, and structures using some graph- and proximity-based algorithms. We also extend our addressing scheme to (i) cover inaccessible areas following similar design principles; (ii) be inclusive and flexible for changes on the ground; and (iii) lead as a pioneer for a unified street-based global geodatabase. We present our results on an example of a developed city and multiple undeveloped cities. We also compare productivity on the basis of current ad hoc and new complete addresses. We conclude by contrasting our generative addresses to current industrial and open solutions. Keywords: road extraction; remote sensing; satellite imagery; machine learning; supervised learning; generative schemes; automatic geocodin
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