3,867 research outputs found
AT-GIS: highly parallel spatial query processing with associative transducers
Users in many domains, including urban planning, transportation, and environmental science want to execute analytical queries over continuously updated spatial datasets. Current solutions for largescale spatial query processing either rely on extensions to RDBMS, which entails expensive loading and indexing phases when the data changes, or distributed map/reduce frameworks, running on resource-hungry compute clusters. Both solutions struggle with the sequential bottleneck of parsing complex, hierarchical spatial data formats, which frequently dominates query execution time. Our goal is to fully exploit the parallelism offered by modern multicore CPUs for parsing and query execution, thus providing the performance of a cluster with the resources of a single machine. We describe AT-GIS, a highly-parallel spatial query processing system that scales linearly to a large number of CPU cores. ATGIS integrates the parsing and querying of spatial data using a new computational abstraction called associative transducers(ATs). ATs can form a single data-parallel pipeline for computation without requiring the spatial input data to be split into logically independent blocks. Using ATs, AT-GIS can execute, in parallel, spatial query operators on the raw input data in multiple formats, without any pre-processing. On a single 64-core machine, AT-GIS provides 3× the performance of an 8-node Hadoop cluster with 192 cores for containment queries, and 10× for aggregation queries
Data modelling for emergency response
Emergency response is one of the most demanding phases in disaster management. The fire brigade, paramedics, police and municipality are the organisations involved in the first response to the incident. They coordinate their work based on welldefined policies and procedures, but they also need the most complete and up-todate information about the incident, which would allow a reliable decision-making.\ud
There is a variety of systems answering the needs of different emergency responders, but they have many drawbacks: the systems are developed for a specific sector; it is difficult to exchange information between systems; the systems offer too much or little information, etc. Several systems have been developed to share information during emergencies but usually they maintain the nformation that is coming from field operations in an unstructured way.\ud
This report presents a data model for organisation of dynamic data (operational and situational data) for emergency response. The model is developed within the RGI-239 project ‘Geographical Data Infrastructure for Disaster Management’ (GDI4DM)
The Foundational Model of Anatomy Ontology
Anatomy is the structure of biological organisms. The term also denotes the scientific
discipline devoted to the study of anatomical entities and the structural and
developmental relations that obtain among these entities during the lifespan of an
organism. Anatomical entities are the independent continuants of biomedical reality on
which physiological and disease processes depend, and which, in response to etiological
agents, can transform themselves into pathological entities. For these reasons, hard copy
and in silico information resources in virtually all fields of biology and medicine, as a
rule, make extensive reference to anatomical entities. Because of the lack of a
generalizable, computable representation of anatomy, developers of computable
terminologies and ontologies in clinical medicine and biomedical research represented
anatomy from their own more or less divergent viewpoints. The resulting heterogeneity
presents a formidable impediment to correlating human anatomy not only across
computational resources but also with the anatomy of model organisms used in
biomedical experimentation. The Foundational Model of Anatomy (FMA) is being
developed to fill the need for a generalizable anatomy ontology, which can be used and
adapted by any computer-based application that requires anatomical information.
Moreover it is evolving into a standard reference for divergent views of anatomy and a
template for representing the anatomy of animals. A distinction is made between the FMA
ontology as a theory of anatomy and the implementation of this theory as the FMA
artifact. In either sense of the term, the FMA is a spatial-structural ontology of the
entities and relations which together form the phenotypic structure of the human
organism at all biologically salient levels of granularity. Making use of explicit
ontological principles and sound methods, it is designed to be understandable by human
beings and navigable by computers. The FMA’s ontological structure provides for
machine-based inference, enabling powerful computational tools of the future to reason
with biomedical data
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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