Semantic XML Views Based on Geographical Context

XML has become a standard for the storage and exchange of data. The widespread use of XML has made queries executed over related, but distinct, XML data sources increasingly relevant. With the growing popularity of XML a wide variety of schemas may be applied to each document. In particular XML data that describes geographical/spatial information often needs to deal with a large number of complex elements and yet, during access and retrieval only a particular set of relevant information -- such as a local area -- is required. For this reason, we must search for ways to increase the performance of data processing and access. Parallelism is an attractive way in which to achieve this aim. With multi-processor or multi-core systems becoming the standard, the idea of parallelism is emerging as a significantly important concept. Methods for XML data processing have been designed and implemented with varying degrees of success. However, these approaches deliver datasets that can contain irrelevant information for the purposes of the user as they do not take the context into account while parsing. This can result in a decrease in the efficiency of the traversal of the data sets when querying. As such, this paper presents a framework the parallel processing of XML documents with a geographical context to filter and group spatial information. In this paper we propose an algorithm and a possible system implementation and identify a potential scale-up methodology.