Automated decision-making in administrative law holds the promise of considerable efficiency gains, enhanced consistency, and increased transparency. This extended abstract distills a qualitative study based on semi-structured expert interviews that adopts a grounded theory approach to investigate the preconditions, constraints, and opportunities for embedding the concept of Digital Twin of Administrative Law (DTAL) into legislative and administrative processes while safeguarding the Rule of Law.

In air traffic flow management (ATFM), temporarily reduced capacity in the European air traffic network leads to the Network Manager imposing a regulation, meaning that flights are assigned new arrival times on a first-planned, first-served basis. Some flights, however, are more important for airlines and the airport than others due to various reasons, e.g., different numbers of affected passengers across flights. Therefore, optimization of the assignment of flights to available arrival times based on airline and airport preferences has the potential to considerably improve overall efficiency. In the ATFM setting, with its multiple, often competing stakeholders, the inputs for the optimization, e.g., costs of delay, are sensitive information, which must be protected. Furthermore, solutions must be found within the available time frame, which for the flight prioritization problem in ATFM is only in the order of minutes. The privacy-preserving implementation of multi-objective optimization algorithms has considerable computational overhead, which may lead to the optimization not finishing within the deadline. To alleviate this problem, we propose the separation of the search for solutions and the evaluation of the solutions, with only the evaluation requiring a privacy-preserving implementation. Our experimental results suggest good convergence under limited time while protecting sensitive inputs.

Solving real-world optimization problems often requires collaboration among multiple stakeholders. In air traffic flow management, for example, airlines must work together to prioritize individual flights in cases of reduced capacity in the air traffic network. However, when diverse parties are required to share sensitive information to collaboratively conduct optimization, trust becomes an issue. To alleviate those issues, privacy-preserving computation can be utilized to protect the confidential information of participants, which comes with a trade-off in terms of runtime performance. In time-critical contexts, privacy-preserving implementations of deterministic optimization algorithms may not be able to produce a result before the deadline. In this paper, we investigate the effectiveness of using variants of local search algorithms for the search of solutions to an optimization problem in conjunction with multi-party computation for the evaluation of those solutions. We argue that the proposed method using local search algorithms achieves good results in terms of the quality of the found solution while considerably reducing the run time with respect to a privacy-preserving deterministic solution.

Users of a business intelligence (BI) system employ an approach referred to as online analytical processing (OLAP) to view multidimensional data from different perspectives. Query languages, e.g., SQL or MDX, allow for flexible querying of multidimensional data but query formulation is often time-consuming and cognitively challenging for many users. Alternatives to using a query language, e.g., graphical OLAP clients, parameterized reports, or dashboards, are often not a full-blown alternative to using a query language. Experience in cooperative research projects with industry led to the following observations regarding the use of OLAP queries in practice. First, within the same organization, similar OLAP queries are repeatedly composed from scratch in order to satisfy similar information needs. Second, across different organizations and even domains, OLAP queries with similar structures are repeatedly composed from scratch. Finally, vague requirements regarding frequently composed OLAP queries in the early stages of a project potentially lead to rushed development in later stages, which can be alleviated by following best practices for OLAP query composition. In engineering, knowledge about best-practice solutions to frequently arising challenges is often documented and represented using patterns. In that spirit, an OLAP pattern describes a generic solution for composing a query that allows a BI user to satisfy a certain type of information need given fragments of a conceptual model. This paper introduces a formal definition of OLAP patterns as well as an expressive, flexible, and generally applicable definition language.

An enterprise database contains a global, integrated, and consistent representation of a company's data. Multi-level modeling facilitates the definition and maintenance of such an integrated conceptual data model in a dynamic environment of changing data requirements of diverse applications. Multi-level models transcend the traditional separation of class and object with clabjects as the central modeling primitive, which allows for a more flexible and natural representation of many real-world use cases. In deep instantiation, the number of instantiation levels of a clabject or property is indicated by a single potency. Dual deep modeling (DDM) differentiates between source potency and target potency of a property or association and supports the flexible instantiation and refinement of the property by statements connecting clabjects at different modeling levels. DDM comes with multiple generalization of clabjects, subsetting/specialization of properties, and multi-level cardinality constraints. Examples are presented using a UML-style notation for DDM together with UML class and object diagrams for the representation of two-level user views derived from the multi-level model. Syntax and semantics of DDM are formalized and implemented in F-Logic, supporting the modeler with integrity checks and rich query facilities.

The increasing number, complexity, and variability of business rules in today's enterprises introduces the need for their effective and flexible management. In several research fields, such as the semantic web, library science, and data tailoring, effective organization of knowledge is enabled by contexts. We previously proposed a static structure model for context-based business rule management. To enable flexibility, we complement this model by atomic and composed modification operations. Each atomic modification operation is associated with one of four roles: rule repository administrator, rule developer, user, and domain expert. This enables effective separation of tasks and responsibilities promoting efficient rule management. Composed modification operations describe combinations of atomic modification operations relevant in practice. We apply the proposed approach to the real-world use case of classifying aeronautical messages.

Reference models for data analysis with data warehouses may consist of multidimensional reference models and analysis graphs. Multidimensional reference models are best-practice domain-specific data models for online analytical processing. Analysis graphs are reference models of analysis processes for event-driven data analysis. Small and medium-sized enterprises (SMEs) as well as large multinational companies may benefit from the use of reference models for data analysis. The availability of multidimensional reference models lowers the obstacles that inhibit SMEs from using business intelligence (BI) technology. Multinational companies may define multidimensional reference models for increased compliance among subsidiaries and departments. Furthermore, the definition of analysis graphs facilitates the handling of business events for both SMEs and large companies. Modelers may customize the chosen reference models, tailoring the models to the specific needs of the individual company or local subsidiary. Customizations may consist of additions, omissions, and modifications with respect to the reference model. In this paper, we propose a metamodel and customization approach for multidimensional reference models and analysis graphs. We specifically address the explicit modeling of key performance indicators as well as the definition of analysis situations and analysis graphs.

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.

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.