By Dr. James Llinas (auth.), Vasily V. Popovich, Manfred Schrenk, Kyrill V. Korolenko P.E. (eds.)
This quantity includes the papers awarded on the foreign Workshop “Inf- mation Fusion and Geographical info platforms” (IF&GIS’07) held in St. Petersburg, Russia, in the course of may perhaps 27-29, 2007. The workshop was once geared up via the St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences (SPIIRAS). examine within the Geosciences box translates the idea that of knowledge Fusion as a synonym of an strategy that allows study into all of the difficulties and concerns which program-makers and clinical researchers are confronted with. hence, subject matters which are to be coated throughout the workshop relate to matters corresponding to harmonization, - tegration and data fusion. even as the spectrum of difficulties - der dialogue exceeds the present bounds of constructing GIS functions. it is a major sleek pattern on account that GIS know-how is extra usually used as an interface in help and selection making structures. consequently, it really is tricky to think about Geoinformation technology with out contemplating comparable medical instructions akin to Ontology, synthetic clever structures and scenario Management.
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Additional info for Information Fusion and Geographic Information Systems: Proceedings of the Third International Workshop
1 General Framework In order to scope the issues of situation modeling, it is beneficial to see the following components of situation modeling: the structural, dynamic and representational components of situation modeling . The structural component identifies the world, systems and individual objects, where we observe the happenings of events and evolving situations as situational transitions. Particularly, we identify the following structural objects: x Entities: things, which have independent meaning in our area of interest; x Attributes of the entities with associated attribute value domains and constraints; x Classes of entities: the abstractions of the sets of entities that share common features like attributes, relations, operations, and behavior; x Relations between the entities, including class, component, containment, location, and other domain-specific relations.
1 Cluster Analysis The term of cluster analysis, pioneered by Tryon in 1939, comprises over 100 various objects’ classification algorithms. To be analyzed all objects are described against similar characteristics’ scale. Objects are united in clusters depending on their similarity. Characteristics’ values form an attribute space. Cluster analysis methods could be divided into two groups: hierarchical and nonhierarchical . The hierarchical clustering main point is either a sequential integration of smaller clusters in bigger ones or splitting of bigger clusters into smaller ones.
We will consider entities as dynamic timedependent objects with their time of creation t’, time of clear t”, and corresponding lifespan G=(t’, t”). e. a(t), tG. 24 G. Jakobson, J. Buford, L. Lewis The value of an attribute at a particular time can be determined by evoking the get_value function. A new value to the attribute is created by the assign_value function, whenever an external event arrives, or by system clock as a scheduled new value assignment, or by any other rule/action specific to the application domain.