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In this paper, we address the data management aspect of large-scale pervasive computing systems. We aim at building an infrastructure that simultaneously supports many kinds of context-aware applications, ranging from room level up to nation level. This all-embracing approach gives rise to synergetic benefits like data reuse and sensor sharing. We identify major classes of context data and detail on their characteristics relevant for efficiently managing large amounts of it. Based on that, we argue that for largescale systems it is beneficial to have special-purpose servers that are optimized for managing a certain class of context data. In the Nexus project we have implemented five servers for different classes of context data and a very flexible federation middleware integrating all these servers. For each of them, we highlight in which way the requirements of the targeted class of data are tackled and discuss our experiences.

Semantic Web technologies, most notably RDF, are wellsuited to cope with typical challenges in spatial data management including analyzing complex relations between entities, integrating heterogeneous data sources and exploiting poorly structured data, e.g., from web communities. Also, RDF can easily represent spatial relationships, as long as the location information is symbolic, i.e., represented by places that have a name. What is widely missing is support for geographic and geometric information, such as coordinates or spatial polygons, which is needed in many applications that deal with sensor data or map data. This calls for efficient data management systems which are capable of querying large amounts of RDF data and support spatial query predicates. We present a native RDF triple store implementation with deeply integrated spatial query functionality. We model spatial features in RDF as literals of a complex geometry type and express spatial predicates as SPARQL filter functions on this type. This makes it possible to use W3C's standardized SPARQL query language as-is, i.e., without any modifications or extensions for spatial queries. We evaluate the characteristics of our system on very large data volumes.

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