Hybrid Quantized Resource Descriptions for Geospatial Source Selection

Location nowadays is an important aspect of the Web. One scenario in this respect are archives or collections of geo-tagged media items. More concretely, we can think of collections in the arts and humanities available via OAI-PMH (a protocol for metadata harvesting) on the web or web accessible personal media archives maintained in a peer-to-peer manner. In such scenarios for search problems, source selection becomes an important aspect. For example, we would like to access only those collections containing media items in a certain geospatial region (maybe we are interested in images from Shanghai only). Here, the geospatial search criterion allows for a high selectivity. What is needed in such a scenario are expressive and nevertheless compact representations or descriptions of the ``geospatial footprint'' of each collection. A minimum bounding rectangle would be a trivial but not very accurate option. Generally, summarization techniques for this purpose can be distinguished into three categories, geometric approaches, space partitioning approaches and hybrid approaches. In this work, we present novel hybrid techniques, which mostly apply a set of approximating minimum area rectangles for subspace description together with quantization techniques in order to increase the selectivity of the summaries and, at the same time, keep the storage requirements small.