Christoph G. Schütz

Dual Deep Instantiation and Its ConceptBase Implementation

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 instantiation still demands a clear separation of instantiation levels: the source and target objects of a relationship must be at the same instantiation level. This separation is inadequate in the context of application integration. Dual deep instantiation (DDI), on the other hand, allows for relationships that connect objects at different instantiation levels. The depth of the characterization may be specified separately for each end of the relationship. In this paper, we present and implement set-theoretic predicates and axioms for the representation of conceptual models with DDI.

Semantic Enrichment of OLAP Cubes: Multi-dimensional Ontologies and Their Representation in SQL and OWL

A multi-dimensional ontology (MDO) enriches an OLAP cube with concepts that represent business terms in the context of data analysis. The formal representation of the meaning of business terms facilitates the unambiguous interpretation of query results as well as the sharing of knowledge among business analysts. In contrast to traditional ontologies, an MDO captures the multi-dimensional, hierarchical world view of business analysts. In this paper, we introduce a translation of MDO concepts to SQL in order to allow for the querying of a closed-world OLAP cube. We introduce a representation in OWL in order to determine subsumption hierarchies of MDO concepts using off-the-shelf reasoners.

Ontology-Driven Business Intelligence for Comparative Data Analysis

In this tutorial, we present an ontology-driven business intelligence approach for comparative data analysis which has been developed in a joint research project, Semantic Cockpit (semCockpit), of academia, industry, and prospective users from public health insurers. In order to gain new insights into their businesses, companies perform comparative data analysis by detecting striking differences between different, yet similar, groups of data. These data groups consist of measure values which quantify real-world facts. Scores compare the measure values of different data groups. semCockpit employs techniques from knowledge-based systems, ontology engineering, and data warehousing in order to support business analysts in their analysis tasks. Concept definitions complement dimensions and facts by capturing relevant business terms which are used in the definition of measures and scores. Furthermore, domain ontologies serve as semantic dimensions and judgement rules externalize previous insights. Finally, we sketch a vision of analysis graphs and associated guidance rules to represent analysis processes.

Customization of Domain-Specific Reference Models for Data Warehouses

The availability of reference models for data warehouses lowers the obstacles that inhibit small and medium-sized enterprises (SMEs) from using business intelligence technology. Service providers may offer to companies a set of pre-configured reference models for various industries. Modelers may then customize the chosen reference model, tailoring the model to the specific needs of the individual company. This customization may consist of additions, omissions, and modifications with respect to the reference model. In this paper, we propose a metamodel and customization approach for reference multidimensional models. We emphasize simplicity and understandability in order to facilitate the introduction of business intelligence solutions in SMEs. Furthermore, we specifically address the explicit modeling of key performance indicators and their calculation rules as well as the definition of reference data marts for report building.

Business model ontologies in OLAP cubes

Business model ontologies capture the complex interdependencies between business objects. The analysis of the hence formalized knowledge eludes traditional OLAP systems which operate on numeric measures. Many real-world facts, however, do not boil down to a single number but are more accurately represented by business model ontologies. In this paper, we adopt business model ontologies for the representation of non-numeric measures in OLAP cubes. We propose modeling guidelines and adapt traditional OLAP operations for ontology-valued measures.

Multilevel Business Artifacts

The representation of many real-world scenarios in conceptual models benefits from the use of multilevel abstraction hierarchies. Product models, for example, are typically grouped into product categories which, in turn, constitute the company’s range of products. Multilevel abstraction hierarchies often reflect the organizational structure of a company and the different information needs of the various departments. Current modeling techniques, however, lack extensive support for the representation of multilevel abstraction hierarchies in business process models. The explicit consideration of multilevel abstraction hierarchies in business process models might improve the alignment of processes across different organizational entities. In this paper, we introduce the concept of the multilevel business artifact (MBA) for representing multilevel abstraction hierarchies of both data and process models. An MBA encapsulates in a single object the data and process models of various levels, thereby expanding consequently the idea of business artifacts to the realm of multilevel abstraction hierarchies.

Multilevel business process modeling: motivation, approach, design issues, and applications

Conceptual models organize the data as well as the processes from a business domain using a human-readable, yet formal representation language. For static data models, various modeling approaches have been proposed to represent complex multilevel abstraction hierarchies. For process models, though, current modeling approaches do not provide a flexible and powerful formalism for representing complex multilevel abstraction hierarchies. In this paper, we motivate the need for multilevel business process models, present our initial approach, and sketch future research in this area.

Incremental integration of data warehouses: the hetero-homogeneous approach

As organizations grow, decision support systems face a surging demand for information; information that must be integrated into the corporate data warehouse. Incremental integration allows for a gradual evolution of data warehouse schemas. Heterogeneous schemas complicate the integration task. Traditional modeling approaches require heterogeneities in the data warehouse schema to be eliminated, or they lack expressiveness when capturing heterogeneous information. When dwelling on homogeneous schemas, however, valuable information is lost. In contrast, the hetero-homogeneous modeling approach preserves the clarity of homogeneous schemas while offering the flexibility that is needed to model large companies with diverse sub-entities. In this paper, we demonstrate the application of well-accepted object-oriented design principles to data warehousing. We examine the incremental integration of data warehouses, and we sketch the fundamentals of our prototype implementation. Following the hetero-homogeneous approach, we preserve schema heterogeneities as much as possible while avoiding common pitfalls of heterogeneous data warehouse modeling.

Exploiting Semantic Activity Labels to Facilitate Consistent Specialization of Abstract Process Activities

Designing business processes from scratch is an intricate and challenging task for process modellers. For this reason, the reuse of process patterns has become an integral part of process modelling in order to deal with recurring design issues in a given domain when modelling new business processes and variants thereof. The specialization of abstract process activities remains a key issue in process pattern reuse. Depending on the intended purpose of process pattern reuse, the specialization of abstract process activities typically ranges from the substitution of abstract process activities with sub-processes to the substitution of activity labels with specialized labels. The specialization of abstract process activities through label specialization has been hardly investigated so far in the business process community. The approach presented in this paper achieves consistent specialization of abstract process activities by ensuring consistent specialization of activity labels through exploitation of semantic activity labels as introduced in previous work. Semantic activity labels encode the linguistic meaning of process activities and thereby facilitate the establishment of consistency criteria based on the implicit semantics captured by activity labels.