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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Abstract. Continuously improved business processes are a central success factor for companies. Yet, existing data analytics do not fully exploit the data generated during process execution. Particularly, they miss prescriptive techniques to transform analysis results into improvement actions. In this paper, we present the data-mining-driven concept of recommendation-based business process optimization on top of a holistic process warehouse. It prescriptively generates action recommendations during process execution to avoid a predicted metric deviation. We discuss data mining techniques and data structures for real-time prediction and recommendation generation and present a proof of concept based on a prototypical implementation in manufacturing.

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