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New techniques for handling quality and uncertainty in spatial mining


The huge amount of spatial data may contain hidden information of significant importance for the experts of domain (e.g. astronomy, seismology, geology, medicine etc.) that could help them to further understand the data and make decisions. Ultimately we s eek to discover patterns in the data as opposed to knowledge about the data itself. Thus spatial mining techniques will be developed to discover the potential interesting characteristics and patterns that exist in large spatial databases. An important chal lenge in the context of spatial data management and decision-making is the handling of uncertainty. The spatial data and relationships in nature are not crisp. This implies that it is not a Boolean decision whether an object belongs in a specific area, or which its relationship is with other objects (sets of objects). Also there are relatively few efforts in the context of knowledge discovery that have devoted data analysis techniques in order to handle uncertainty. It is then obvious that there are inheren t features of spatial data that are not taken into account during the mining process and knowledge that is partially extracted. Then there is a need for defining a data model that represent the uncertainty in spatial data analysis process. Moreover, the re quirement for exploiting the inherent uncertainty of data in the quality assessment of mining results and decision-making arises. Also spatial data are commonly viewed as infinite sequence of data. Thus issues related to the effective mining of such data a nd the quality of the mining results due to their frequent updates are emerged. In the context of this project we will address the above issues of uncertainty representation and quality assessment in the context of spatial mining. A spatial mining framewor k will be defined in conjunction with quality assessment and decision support techniques through the life cycle of spatial and time-evolving data.

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