2,268 research outputs found
Web-based public participation GIS application : a case study on flood emergency management
Scientific summary The increasing prevalence of natural disasters is driving people to pay more and more attention to emergency management. Progress in catastrophe analysis capabilities based on Geographical Information System (GIS) may allow the needs of public participation to be considered. Synchronous data sharing between citizens and emergency workers could effectively promote the process of decision making. This thesis introduces an interactive web-based application which mainly deals with flood risk management in Kamloops in Canada. The application is built for citizens and emergency workers using three layers: (1) the client side is developed in HTML and JavaScript; (2) the web server layer, which connects the users and the database, is implemented in PHP; and (3) the database contains PostgreSQL, GeoServer and OSM. Except the city map, PostgreSQL stores the spatial information with the support of OpenGIS. Generally, the application meets the initial objectives. Citizens can access present shelter information and register their own requirements for shelter, while emergency workers have the power to manage all the shelters and warehouses based on the available flood information and figure out the supply allocation solution based on the response from the public. On the other hand, the application also provides useful routing functions for both citizens and emergency workers, such as searching the available shortest path to a shelter, and computing the optimized allocation routes between all the shelters and warehouses. This practical study proved that Public Participation GIS (PPGIS), combined with IT knowledge, can provide very useful tools for decision making when facing a flood risk.Popularized summary Nowadays, the growing prevalence of natural disasters is driving people to pay more and more attention to emergency management. Progress in catastrophe analysis capabilities based on Geographical Information System (GIS) may allow the needs of public participation to be considered. Synchronous data sharing between citizens and emergency workers could effectively promote the process of decision making. This thesis introduces an interactive web-based application which mainly deals with flood risk management in Kamloops in Canada. The application contains various data sources and adopts spatial database. Citizens can access present shelter information and register their own requirements for shelter, while emergency workers have the power to manage all the shelters and warehouses based on the available flood information and figure out the supply allocation solution based on the response from the public. On the other hand, the application also provides useful routing functions for both citizens and emergency workers, such as searching the available shortest path to a shelter, and computing the optimized allocation routes between all the shelters and warehouses. This practical study proved that Public Participation GIS (PPGIS), combined with IT knowledge, can provide very useful tools for decision making when facing a flood risk
INDIGO - INtegrated Data Warehouse of MIcrobial GenOmes with Examples from the Red Sea Extremophiles.
Background: The next generation sequencing technologies substantially increased the throughput of microbial
genome sequencing. To functionally annotate newly sequenced microbial genomes, a variety of experimental and
computational methods are used. Integration of information from different sources is a powerful approach to enhance
such annotation. Functional analysis of microbial genomes, necessary for downstream experiments, crucially
depends on this annotation but it is hampered by the current lack of suitable information integration and exploration
systems for microbial genomes.
Results: We developed a data warehouse system (INDIGO) that enables the integration of annotations for
exploration and analysis of newly sequenced microbial genomes. INDIGO offers an opportunity to construct complex
queries and combine annotations from multiple sources starting from genomic sequence to protein domain, gene
ontology and pathway levels. This data warehouse is aimed at being populated with information from genomes of
pure cultures and uncultured single cells of Red Sea bacteria and Archaea. Currently, INDIGO contains information
from Salinisphaera shabanensis, Haloplasma contractile, and Halorhabdus tiamatea - extremophiles isolated from
deep-sea anoxic brine lakes of the Red Sea. We provide examples of utilizing the system to gain new insights into
specific aspects on the unique lifestyle and adaptations of these organisms to extreme environments.
Conclusions: We developed a data warehouse system, INDIGO, which enables comprehensive integration of
information from various resources to be used for annotation, exploration and analysis of microbial genomes. It will
be regularly updated and extended with new genomes. It is aimed to serve as a resource dedicated to the Red Sea
microbes. In addition, through INDIGO, we provide our Automatic Annotation of Microbial Genomes (AAMG) pipeline.
The INDIGO web server is freely available at http://www.cbrc.kaust.edu.sa/indigo.IA and AAK were supported from the KAUST CBRC Base Fund of VBB. WBa and VBB were supported from the KAUST Base Funds of VBB. US was supported by the KAUST Base Fund of US. This study was partly supported by the Saudi Economic and Development Company (SEDCO) Research Excellence award to US and VBB. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript
Impliance: A Next Generation Information Management Appliance
ably successful in building a large market and adapting to the changes of the
last three decades, its impact on the broader market of information management
is surprisingly limited. If we were to design an information management system
from scratch, based upon today's requirements and hardware capabilities, would
it look anything like today's database systems?" In this paper, we introduce
Impliance, a next-generation information management system consisting of
hardware and software components integrated to form an easy-to-administer
appliance that can store, retrieve, and analyze all types of structured,
semi-structured, and unstructured information. We first summarize the trends
that will shape information management for the foreseeable future. Those trends
imply three major requirements for Impliance: (1) to be able to store, manage,
and uniformly query all data, not just structured records; (2) to be able to
scale out as the volume of this data grows; and (3) to be simple and robust in
operation. We then describe four key ideas that are uniquely combined in
Impliance to address these requirements, namely the ideas of: (a) integrating
software and off-the-shelf hardware into a generic information appliance; (b)
automatically discovering, organizing, and managing all data - unstructured as
well as structured - in a uniform way; (c) achieving scale-out by exploiting
simple, massive parallel processing, and (d) virtualizing compute and storage
resources to unify, simplify, and streamline the management of Impliance.
Impliance is an ambitious, long-term effort to define simpler, more robust, and
more scalable information systems for tomorrow's enterprises.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
An automated ETL for online datasets
While using online datasets for machine learning is commonplace today, the quality of these datasets impacts on the performance
of prediction algorithms. One method for improving the semantics of new data sources is to map these sources to a common
data model or ontology. While semantic and structural heterogeneities must still be resolved, this provides a well established
approach to providing clean datasets, suitable for machine learning and analysis. However, when there is a requirement for a
close to real time usage of online data, a method for dynamic Extract-Transform-Load of new sources data must be developed.
In this work, we present a framework for integrating online and enterprise data sources, in close to real time, to provide
datasets for machine learning and predictive algorithms. An exhaustive evaluation compares a human built data transformation
process with our system’s machine generated ETL process, with very favourable results, illustrating the value and impact of
an automated approach
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