Flexible Querying of Volunteered Geographic Information for Risk Management

The paper presents an approach to manage volunteered geographic information (VGI) to point out anomalous conditions of the environment to help administrators in charge of the governance and maintenance of the territory to plan mitigation and safeguard interventions. To this end they can formulate flexible queries on the VGI reports to analyze their contents. The novelty of the proposal is the search framework of VGI reports designed to support distinct needs, among which the assessment of VGI quality which is an important issue in such applications. Flexible queries are formulated and evaluated within a fuzzy database approach.

[1]  Henri Prade,et al.  An Introduction to the Fuzzy Set and Possibility Theory-Based Treatment of Flexible Queries and Uncertain or Imprecise Databases , 1996, Uncertainty Management in Information Systems.

[2]  E. Hand,et al.  Citizen science: People power , 2010, Nature.

[3]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[4]  Robert Jeansoulin,et al.  Fundamentals of Spatial Data Quality , 2006 .

[5]  Gerard Salton,et al.  Automatic Text Processing: The Transformation, Analysis, and Retrieval of Information by Computer , 1989 .

[6]  Gloria Bordogna,et al.  Managing uncertainty in location-based queries , 2009, Fuzzy Sets Syst..

[7]  David P. Anderson,et al.  BOINC: a system for public-resource computing and storage , 2004, Fifth IEEE/ACM International Workshop on Grid Computing.

[8]  Michael F. Goodchild,et al.  Assuring the quality of volunteered geographic information , 2012 .

[9]  Michael F. Goodchild,et al.  Citizens as Voluntary Sensors: Spatial Data Infrastructure in the World of Web 2.0 , 2007, Int. J. Spatial Data Infrastructures Res..

[10]  Gerald Salton,et al.  Automatic text processing , 1988 .

[11]  Frank O. Ostermann,et al.  Validation and relevance assessment of volunteered geographic information in the case of forest fires , 2010 .

[12]  José Galindo,et al.  Handbook of Research on Fuzzy Information Processing in Databases , 2008, Handbook of Research on Fuzzy Information Processing in Databases.

[13]  Leszek Litwin,et al.  Geoinformation Metadata in INSPIRE and SDI , 2011 .

[14]  S. Griffis EDITOR , 1997, Journal of Navigation.

[15]  Guy De Tré,et al.  Fuzziness in database management systems: Half a century of developments and future prospects , 2015, Fuzzy Sets Syst..

[16]  G. Gartner,et al.  Lecture Notes in Geoinformation and Cartography , 2006 .

[17]  C. Lintott,et al.  Galaxy Zoo: Exploring the Motivations of Citizen Science Volunteers. , 2009, 0909.2925.

[18]  P. Cochat,et al.  Et al , 2008, Archives de pediatrie : organe officiel de la Societe francaise de pediatrie.

[19]  Pavel Zezula,et al.  M-tree: An Efficient Access Method for Similarity Search in Metric Spaces , 1997, VLDB.

[20]  Otis Gospodnetic,et al.  Lucene in Action , 2004 .

[21]  Gloria Bordogna,et al.  Recent Issues on Fuzzy Databases , 2000 .

[22]  Gloria Bordogna,et al.  A linguistic decision making approach to assess the quality of volunteer geographic information for citizen science , 2014, Inf. Sci..

[23]  Filip Devos,et al.  Time Management in Fuzzy and Uncertain Object-Oriented Databases , 2000 .

[24]  J. Hielkema,et al.  GeoNetwork opensourceInternationally Standardized Distributed SpatialInformation Management , 2007 .