Multiple levels of classification naturally occur in many domains. Several multi-level modeling approaches account for this, and a subset of them attempt to provide their users with sanity-checking mechanisms in order to guard them against conceptually ill-formed models. Historically, the respective multi-level well-formedness schemes have either been overly restrictive or too lax. Orthogonal Ontological Classification has been proposed as a foundation for sound multi-level modeling that combines the selectivity of strict schemes with the flexibility afforded by laxer schemes. In this article, we present the second iteration of a formalization of Orthogonal Ontological Classification, which we empirically validated to demonstrate some of its hitherto only postulated claims using an implementation in ConceptBase . We discuss the expressiveness of the formal language used, ConceptBase ’s evaluation efficiency, and the usability of our realization based on a digital twin example model.

We show three interrelated tools intended to improve different aspects of the quality of data warehouse solutions. Firstly, the deductive object manager ConceptBase is intended to enrich the semantics of data warehouse solutions by including an explicit enterprise-centered concept of quality. The positive impact of precise multidimensional data models on the client interface is demonstrated by CoDecide, an Internet-based toolkit for the flexible visualization of multiple, interrelated data cubes. Finally, MIDAS is a hybrid data mining system which analyses multi-dimensional data to further enrich the semantics of the meta database, using a combination of neural network techniques, fuzzy logic and machine learning.

A logical database schema, e.g. a relational one, is the implementation of a specification, e.g. an entity-relationship diagram. Upcoming new data models require a cost-effective method for mapping from one data model to the other. We present an approach where the mapping process is divided into three parts. The first part reformulates the source and target data models into a so-called meta model. The second part classifies the input schema into the meta model, yielding a data model-independent representation. The third part synthesizes the output schema in terms of the target data model. The meta model, the data models as well as the schemas are all represented in the logic-based formalism of O-Telos. Its ability to quantify across data model concepts is the key to classifying schema elements independently of their data model. A prototype has been implemented on top of the deductive object base manager ConceptBase for the mapping of relational schemas to entity-relationship diagrams. From this, a C++-based tool has been derived as part of a commercial CASE environment for database applications.

The WibQuS project investigates distributed computer and communication support for Total Quality Management in industrial organizations. An interdisciplinary study of this task reveals three aspects of intelligent cooperation required from such a system which we call conceptual, technical, and social integration. We use the repository standard IRDS to characterize these aspects. A solution concept derived from this characterization employs advanced conceptual modeling techniques to derive interoperability among technical subsystems and coordination technology for the human subsystem semi-automatically while taking important social factors into account.