Hyper-relation is a recently proposed formal concept for denormalization of relational databases. Hyper-relations are structures of prejoined relations the schema of which can be represented as a hyper-graph. Instead of entire relations of the tables in the initial normalized schema, hyper-relations prejoin their atomic constituents called base relations, and do that with only those base relations that are really used in the queries being optimized. In this paper, we derive a simple and efficient algorithm for rewriting queries for hyper-relations. The algorithm is based on incremental rewriting procedure that traverses the query expression tree and builds up the transformed query by seamlessly propagating the proposed$\Sigma$operator, composed of selection and nullification of the tuples in a hyper-relation. The algorithm is applicable to a wide range of multiblock (nested) select-project-(outer) join queries.

A formal model of denormalized relational schemas based on outer joins of base relations is presented. The composition of joins is modeled with a hyper-tree, called hyper-schema, with vertexes representing equi-joins, and edges representing joined base relations. The base relations are not the relations defined in the conceptual schema, but their “atoms.” The semantics of hyper-relations is defined in terms of join disjunctions. Three different denormalized outer join forms are also defined, in terms of constraints imposed to hyper-schemas that gradually make the maintenance of hyper-relations simpler and more efficient. The presented model can be used as a formal underpinning for 1) a special, but important case of materialized views, 2) an implementation of the physical, yet relational schema of a highly denormalized database, or 3) different optimization techniques based on denormalization.

Conceptual and relational data models of online transaction processing (OLTP) applications are usually created and maintained following the principle of normalization, which implies avoidance of redundancy. Data retrieval from a disk-based normalized relational database often requires complex and inefficient queries that may cause noticeable performance issues when executed on larger volumes of data. Computer professionals sometimes intentionally trade off the strict normal form to optimize data retrieval queries through error-prone manual tuning and denormalization. We propose a fully automatic optimization approach, based on data redundancy, that relies on a formal cost-benefit model. We prove that finding the optimal level of data redundancy, for given workload statistics, is an NP-Complete optimization problem. A detailed reduction of the problem to binary linear programming is presented in the paper. The proposed optimization approach was evaluated using the TPCE benchmark for OLTP systems. The evaluation has shown that the proposed optimization approach is highly scalable, and that it can be efficiently applied to real-life relational data models.

We outline a methodology for automatic and efficient object-relational mapping (ORM) in the context of model-driven development (MDD) of high-performance information systems specified with executable UML models. Although there are various approaches to performance tuning, we focus here on the persistence layer the relational database. The relational data model is usually designed following the well-known normal forms. However, a fully normalized relational model often does not provide sufficient performance, and improper relational model design can easily lead to a slow and unusable relational database for particular operations. Our ORM approach is intended to exploit smart optimization techniques from the relational paradigm that abandon normalization and its positive effects, and trade them off for better performance. Our ORM approach hence combines the classical denormalization transformations, based on reducing or eliminating expensive database operations by the model restructuring, but applies them to a non-redundant conceptual UML model. In this paper, we also present the first step towards this goal: a catalogue of ORM transformation patterns.

α-algorithm is able to discover a large class of workflow (WF) nets based on the behavior recorded in event logs, with the main limiting assumption that the event log is complete. Our research has been aimed at finding ways of business process models discovering based on examples of traces, i.e., logs of workflow actions that do not meet the requirement of completeness. In this aim, we have modified the existing and introduced a new relation between activities recorded in the event log, which has led to a partial correction of the process models discovering techniques, including the α-algorithm. We have also introduced the notions of causally and weakly complete logs, from which our modified algorithm can produce the same result as the original algorithm from complete logs. The effect of these modifications on the speed of the process model discovering is mostly evident for business processes in which many activities can be performed in parallel. Therefore, this paper presents preliminary results obtained from the investigation of opportunities to discover models of parallel processes based on incomplete event logs.

We present a novel approach to modeling and implementing user interfaces (UI) of large business applications. The approach is based on the concept of capsule, a profiled structured class from UML which models a simple UI component or a coherent UI fragment of logically and functionally coupled components or other fragments with a clear interface. Consequently, the same modeling concept of capsule with internal structure can be reapplied recursively at successively lower levels of detail within a model, starting from high architectural modeling levels down to the lowest levels of modeling simple UI components. The interface of capsules is defined in terms of pins, while the functional coupling of capsules is specified declaratively by simply wiring their pins. Pins and wires transport messages between capsules, ensuring strict encapsulation. The approach includes a method for formal coupling of capsules' behavior with the underlying object space that provides proper impedance matching between the UI and the business logic while preserving clear separation of concerns between them. We also briefly describe an implementation of a framework that supports the proposed method, including a rich library of ready-to-use capsules, and report on our experience in applying the approach in large-scale industrial systems.

Association is one of the key concepts in UML that is intensively used in conceptual modeling. Unfortunately, in spite of the fact that this concept is very old and is inherited from other successful modeling techniques, a fully unambiguous understanding of it, especially in correlation with other newer concepts connected with association ends, such as uniqueness, still does not exist. This paper describes a problem with one widely assumed interpretation of the uniqueness of association ends, the restrictive interpretation, and proposes an alternative, the intentional interpretation. Instead of restricting the association from having duplicate links, uniqueness of an association end in the intentional interpretation modifies the way in which the association end maps an object of the opposite class to a collection of objects of the class at that association end. If the association end is unique, the collection is a set obtained by projecting the collection of all linked objects. In that sense, the uniqueness of an association end modifies the view to the objects at that end, but does not constrain the underlying object structure. This paper demonstrates how the intentional interpretation improves expressiveness of the modeling language and has some other interesting advantages. Finally, this paper gives a completely formal definition of the concepts of association and association ends, along with the related notions of uniqueness, ordering, and multiplicity. The semantics of the UML actions on associations are also defined formally.

One of the most important features of modeling tools is generation of output. The output may be documentation, source code, net list, or any other presentation of the system being constructed. The process of output generation may be considered as automatic creation of a target model from a model in the source modeling domain. This translation does not need to be accomplished in a single step. Instead, a tool may generate multiple intermediate models as other views to the system. These models may be used either as better descriptions of the system, or as a descent down the abstraction levels of the user-defined model, gradually leading to the desired implementation. If the modeling domains have their metamodels defined in terms of object-oriented concepts, the models consist of instances of the abstractions from the metamodels and links between them. A new technique for specifying the mapping between different modeling domains is proposed in the paper. It uses UML object diagrams that show the instances and links of the target model that should be created during automatic translations. The diagrams are extended with the proposed concepts of conditional, repetitive, parameterized, and polymorphic model creation, implemented by the standard UML extensibility mechanisms. Several examples from different engineering domains are provided, illustrating the applicability and benefits of the approach. The first experimental results show that the specifications may lead to better reuse and shorter production time when developing customized output generators.