Bernd Neumayr

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Application integration requires the consideration of instance data and schema data. Instance data in one application may be schema data for another application, which gives rise to multiple instantiation levels. Using deep instantiation, an object may be deeply characterized by representing schema data about objects several instantiation levels below. Deep(More)
Using traditional semantic data modeling, multi-level modeling can be achieved by representing objects in different abstraction hierarchies, namely classification, aggregation and generalization. This, however, leads to accidental complexity, complicating maintenance and extension. Several modeling techniques, like deep instantiation, powertypes and(More)
Understandability, reuse, and maintainability of analytical queries belong to the key challenges of Data Warehousing, especially in settings where a large number of business analysts work together and need to share knowledge. To tackle these challenges we propose Ontology-based OLAP where an ontology acts as superimposed conceptual layer between business(More)
Business analysts frequently use Cockpits or Dashboards as front ends to data warehouses for inspecting and comparing multidimensional data at various levels of detail. These tools, however, perform badly in supporting a business analyst in his or her business intelligence task of understanding and evaluating a business within its environmental context(More)
In recent years there has been growing interest in the use of multi-level modelling approaches to better represent the multiple classification levels that are frequently found in the real world and are needed to effectively engineer languages. Multi-level modelling approaches have not only been successfully used in numerous industrial projects and standards(More)
Ontological metamodeling or multilevel-modeling refers to describing complex domains at multiple levels of abstraction, especially in domains where the borderline between individuals and classes is not clear cut. Punning in OWL2 provides decideable metamodeling support by allowing to use one symbol both as identifier of a class as well as of an individual.(More)
Ontological multi-level modeling refers to describing domain objects at multiple levels of abstraction. Using traditional semantic data modeling, multi-level modeling can be achieved by representing objects in different abstraction hierarchies, classification, aggregation and generalization. Multiple representation, however, leads to accidental complexity,(More)