In many class-based object-oriented systems the association between as instance and a class is exclusive and permanent. Therefore these systems have serious difficulties in representing objects taking on different roles over time. Such objects must be reclassified any time they evolve (e.g., if a person becomes a student and later an employee). Class hierarchies must be planned carefully and may grow exponentially if entities may take on serveral independent roles. The problem is even more servere for object-oriented databases than for common object-oriented programming. Databases store objects over longer periods, during which the represented entities evolve. This article shows how class-based object-oriented systems can be extended to handle evolving objects well. Class hierarchies are complemented by role hierarchies, whose nodes represent role types an object classified in the root may take on. At any point in time, an entity is represented by an instance of the root and an instance of every role type whose role it currently plays. In a natural way, the approach extends traditional object-oriented concepts, such as classification, object identity, specialization, inheritance, and polymorphism in a natural way. The practicability of the approach is demonstrated by an implementation in Smalltalk. Smalltalk was chosen because it is widely known, which is not true for any particular class-based object-oriented database programming language. Roles can be provided in Smalltalk by adding a few classes. There is no need to modify the semantics of Smalltalk itself. Role hierarchies are mapped transparently onto ordinary classes. The presented implementation can easily be ported to object-oriented database programming languages based on Smalltalk, such as Gemstone's OPAL hierarchies are complemented by role hierarchies, whose nodes represent role types an object classified in the root may take on. At any point in time, an entity is represented by an instance of the root and an instance of every role type whose role in currently plays.

Object-oriented design methodologies represent the behavior of instances of an object type not merely by a set of operations, but also by providing an overall description on how instances evolve over time. Such a description is often referred to as "object life cycle."Object-oriented systems organize object types in hierarchies in which subtypes inherit and specialize the structure and behavior of their supertypes. Past experience has shown that unrestricted use of inheritance mechanisms leads to system architectures that are hard to understand and to maintain, since arbitrary differences between supertype and subtype are possible. Evidently, this is not a desirable state of affairs and the behavior of a subtype should specialize the behavior of its supertype according to some clearly defined consistency criteria. Such criteria have been formulated in terms of type systems for semantic data models and object-oriented programming languages. But corresponding criteria for the specialization of object life cycles have so far not been thoroughly investigated.This paper defines such criteria in the realm of Object Behavior Diagrams, which have been originally developed for the design of object-oriented databases. Its main contributions are necessary and sufficient rules for checking behavior consistency between object life cycles of object types in specialization hierarchies with multiple inheritance.

No abstract

No abstract

Digitalization of agricultural technology has led to the emergence of precision dairy farming, which strives for the simultaneous improvement of productivity as well as animal well-being in dairy farming through advanced use of technology such as movement sensors and milking parlors to monitor, control, and improve dairy production processes. The data warehouse serves as the appropriate technology for effective and efficient data management, which is paramount to the success of precision dairy farming. This paper presents a joint effort between industry and academia on the experimental development of an active semantic data warehouse to support business intelligence and business analytics in precision dairy farming. The research follows an action research approach, deriving lessons for theory and practice from a set of actions taken in the course of the project. Among these actions are the development of a loading stage to facilitate data integration, the definition of an analysis view as well as the introduction of semantic OLAP patterns to facilitate analysis, and analysis rules to automate periodic analyses. The large volumes of generated sensor data in precision dairy farming required careful decision-making concerning the appropriate level of detail of the data stored in the data warehouse. Semantic technologies played a key role in rendering analysis accessible to end users.