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Building an Environmental Information System for Personalized Content Delivery

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Citizens are increasingly aware of the influence of environmental and meteorological conditions on the quality of their life. This results in an increasing demand for personalized environmental information, i.e., information that is tailored to citizens’ specific context and background. In this work we describe the development of an environmental information system that addresses this demand in its full complexity. Specifically, we aim at developing a system that supports submission of user generated queries related to environmental conditions. From the technical point of view, the system is tuned to discover reliable data in the web and to process these data in order to convert them into knowledge, which is stored in a dedicated repository. At run time, this information is transferred into an ontology-structured knowledge base, from which then information relevant to the specific user is deduced and communicated in the language of their preference.
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... In this paper, we aim to describe the general architecture of the PESCaDO system, focusing especially on the fusion of extracted information [20], [21]. First, we discover environmental nodes (i.e. ...
... We present here an overview of the general architecture of the PESCaDO system. For a more detailed description, the reader is referred to [20], [21]. ...
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... The purpose of the PESCaDO project is to address the above point, by (i) taking as a starting point plain text queries in human languages or interactive user input through a web interface, in a way that transcends simple keyword-based searches (ii) infer the implied context and semantic meaning of the user's query (e.g., place, time, activity, type of information desired) by using advanced semantic and ontological textual analysis tools, and (iii) coordinate the data flow from a number of heterogeneous (text, images, feeds, binary files) data sources (Environmental Node Orchestration) in order to produce a response by taking into account all available data, as described in Wanner et al. [2011]. ...
... Some of the basic service modules used in PESCaDO can be summed up, in terms of functionality, in the following principal types (more details may be found in Wanner et al. [2010], and Wanner et al. [2011]): ...
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The PESCaDO project (http://www.pescado-project.eu/) aims at providing tailored environmental information to EU citizens. For this purpose, PESCaDO delivers personalized environmental information, based on coordinating the data flow from multiple sources. After the necessary discovery, indexing and parsing of those sources, the harmonization and retrieval of data is achieved through Node Orchestration and the creation of unified and accurate responses to user queries by using the Fusion service, which assimilates input data into a coherent data block according to their imprecision and relevance in respect to the user defined query. Environmental nodes are selected from open-access web resources of various types, and from the direct usage of data from monitoring stations. Forecasts of models are made available through the synergy with the AirMerge Image parsing engine and its chemical weather database. In the presented paper, elements of the general architecture of AirMerge, and the Fusion service of PESCaDO are exposed as an example of the modus operandi of environmental information fusion for the atmospheric environment.
... In this paper, we aim to describe the general architecture of the PESCaDO system, focusing especially on the fusion of extracted information [20], [21]. First, we discover environmental nodes (i.e. ...
... We present here an overview of the general architecture of the PESCaDO system. For a more detailed description, the reader is referred to [20], [21]. ...
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The PESCaDO system (Personal Environmental Service Configuration and Delivery Orchestration) aims at providing accurate and timely information about local air quality and weather conditions in Europe. The system receives environment related queries from end users, discovers reliable environmental multimedia data in the web from different providers and processes these data in order to convert them into information and knowledge. Finally, the system uses the produced information to provide the end user a personalized response. In this paper, we present the general architecture of the above mentioned system, focusing on the extraction and fusion of multimedia environmental data. The main research contribution of the proposed system is a novel information fusion method based on statistical regression modelling that uses as input data land use and population density masks, historic track-record of data providers as well as an array of atmospheric measurements at various locations. An implementation of this fusion model has been successfully tested against two selected datasets on air pollutant concentrations and ambient air temperatures.
... SanMiguel et al. used an ontology for combining image processing algorithms with knowledge about objects and events [24]. Wanner et al. proposed an environmental information system in which environmental data, e.g., temperature measurements for a city, are stored in an OWL knowledge base [27]. ...
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... The goal of the PESCaDO EU project [27] is to develop a multilingual web-service platform providing personalized environmental information and decision support. The backbone of the PESCaDO platform [28] is an environmental ontology-based knowledge base where all the information relevant for a user request are dynamically instantiated. The ontology formalizes a variety of aspects related to the application context: environmental data, data sources, user requests, user profiles, warnings and recommendations triggered by environmental conditions, and so on. ...
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... Moraru and Mladeni c (2012) propose a framework that resembles the Wavellite measurement and observation layers. Wanner et al. (2011) present an environmental information system in which environmental data, e.g. temperature measurements for a city, are stored in an OWL knowledge base. ...
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Reference Model for the ORCHESTRA Architecture Version 2.1. OGC Best Practices Document
  • T Usländer
Usländer, T. (ed.): Reference Model for the ORCHESTRA Architecture Version 2.1. OGC Best Practices Document 07-097, http://portal.opengeospatial.org/files/?artifact\_id=23286 (2007)
Specification of the Sensor Service Architecture
  • T Usländer
Usländer, T.: Specification of the Sensor Service Architecture, Version 3.0 (Rev. 3.1). OGC Discussion Paper 09-132r1. Deliverable D2.3.4 of the European Integrated Project SANY, FP6-IST-033564, http://portal.opengeospatial.org/files/?artifact\_id=35888\&version=1 (2009)