An attribute is obligatory for a class in a Knowledge Base (KB), if all instances of the class have the attribute in the real world. For example, has­Birth­Date is an obligatory attribute for the class Person, while has ­ Spouse is not. In this paper, we propose a new way to model incompleteness in KBs. From this model, we derive a method to automatically determine obligatory attributes -- using only the data from the KB. Our algorithm can detect such attributes with a precision of up to 90%.

Statistical analysts have long been struggling with evergrowing data volumes. While specialized data management systems such as relational databases would be able to handle the data, statistical analysis tools are far more convenient to express complex data analyses. An integration of these two classes of systems has the potential to overcome the data management issue while at the same time keeping analysis convenient. However, one must keep a careful eye on implementation overheads such as serialization. In this paper, we propose the in-process integration of data management and analytical tools. Furthermore, we argue that a zero-copy integration is feasible due to the omnipresence of C-style arrays containing native types. We discuss the general concept and present a prototype of this integration based on the columnar relational database MonetDB and the R environment for statistical computing. We evaluate the performance of this prototype in a series of micro-benchmarks of common data management tasks.