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In the recent past, different multidimensional data models were introduced to model OLAP (‘Online Analytical Processing’) scenarios. Design problems arise, when the modeled OLAP scenarios become very large and the dimensionality increases, which greatly decreases the support for an efficient ad-hoc data analysis process. Therefore, we extend the classical multidimensional model by grouping functionally dependent attributes within single dimensions, yielding in real orthogonal dimensions, which are easy to create and to maintain on schema design level. During the multidimensional data analysis phase, this technique yields in nested data cubes reflecting an intuitive two-step navigation process: classification-oriented ‘drill-down’/ ‘roll-up’ and description-oriented‘split’/ ‘merge’ operators on data cubes. Thus, the proposed Nested Multidimensional Data Model provides great modeling flexibility during the schema design phase and application-oriented restrictiveness during the data analysis phase.

Schema design is one of the fundamentals in database theory and practice as well. In this paper, we discuss the problem of locally valid dimensional attributes in a classification hierarchy of a typical OLAP scenario. In a first step, we show that the traditional star and snowflake schema approach is not feasible in this very natural case of a hierarchy. Therefore, we sketch two alternative modeling approaches resulting in practical solutions and a seamless extension of the traditional star and snowflake schema approach: In a pure relational approach, we replace each dimension table of a star / snowflake schema by a set of views directly reflecting the classification hierarchy. The second approach takes advantage of the object-relational extensions. Using object-relational techniques in the context for the relational representation of a multidimensional OLAP scenario is a novel approach and promises a clean and smooth schema design.

The concept of multidimensional modeling has proven extremely successful in the area of Online Analytical Processing (OLAP) as one of many applications running on top of a data warehouse installation. Although many different modeling techniques expressed in extended multidimensional data models were proposed in the recent past, we feel that many hot issues are not properly reflected. In this paper we address ten common problems reaching from defects within dimensional structures over multidimensional structures to new analytical requirements and more.

The benefits of Model-Driven Software Development (MDSD) and Domain-Specific Languages (DSLs) wrt. efficiency and quality in software engineering increase the demand for custom languages and the need for efficient methods for language engineering. This motivated the introduction of language families that aim at further reducing the development costs and the maintenance effort for custom languages. The basic idea is to exploit the commonalities and provide means to enable systematic variation among a set of related languages. Current techniques and methodologies for language engineering are not prepared to deal with the particular challenges of language families. First, language engineering processes lack means for a systematic analysis, specification and management of variability as found in language families. Second, technical approaches for a modular specification and realisation of languages suffer from insufficient modularity properties. They lack means for information hiding, for explicit module interfaces, for loose coupling, and for flexible module integration. Our first contribution, Feature-Oriented Language Family Engineering (LFE), adapts methods from Software Product Line Engineering to the domain of language engineering. It extends Feature-Oriented Software Development to support metamodelling approaches used for language engineering and replaces state-of-the-art processes by a variability- and reuse-oriented LFE process. Feature-oriented techniques are used as means for systematic variability analysis, variability management, language variant specification, and the automatic derivation of custom language variants. Our second contribution, Integrative Role-Based Language Composition, extends existing metamodelling approaches with roles. Role models introduce enhanced modularity for object-oriented specifications like abstract syntax metamodels. We introduce a role-based language for the specification of language components, a role-based composition language, and an extensible composition system to evaluate role-based language composition programs. The composition system introduces integrative, grey-box composition techniques for language syntax and semantics that realise the statics and dynamics of role composition, respectively. To evaluate the introduced approaches and to show their applicability, we apply them in three major case studies. First, we use feature-oriented LFE to implement a language family for the ontology language OWL. Second, we employ role-based language composition to realise a component-based version of the language OCL. Third, we apply both approaches in combination for the development of SumUp, a family of languages for mathematical equations.