Declarative Event-Based Workflow as Distributed Dynamic Condition Response Graphs

We present Dynamic Condition Response Graphs (DCR Graphs) as a declarative, event-based process model inspired by the workflow language employed by our industrial partner and conservatively generalizing prime event structures. A dynamic condition response graph is a directed graph with nodes representing the events that can happen and arrows representing four relations between events: condition, response, include, and exclude. Distributed DCR Graphs is then obtained by assigning roles to events and principals. We give a graphical notation inspired by related work by van der Aalst et al. We exemplify the use of distributed DCR Graphs on a simple workflow taken from a field study at a Danish hospital, pointing out their flexibility compared to imperative workflow models. Finally we provide a mapping from DCR Graphs to Buchi-automata.Comment: In Proceedings PLACES 2010, arXiv:1110.385

Modeling Composite Web Services by Using a Logic-based Language

Abstract. In order to answer the complex service requirements of the user, composite web services have to be constructed correctly and effectively. Various approaches and formalism have been used for web service composition and integration. The semantic modeling of composite services is necessary for automatic discovery, integration and execution. For this purpose, ontology languages and ontologies have been defined. OWL-S is a OWL-based ontology of services, in which composite processes can be modeled. For reasoning and verification on the composite services, logic-based formalisms have an important role. Concurrent Constraint Transaction Logic is a formalism that provides means for modeling, verification and scheduling of composite web services. In this work, we describe how OWL-S and CCTR can be used together for modeling a complex service and constraints, and make reasoning and verification on this model under the given set of constraints.

Confidence-based Concept Discovery in Multi-Relational Data Mining

Abstract—Multi-relational data mining has become popular due to the limitations of propositional problem definition in structured domains and the tendency of storing data in relational databases. Several relational knowledge discovery systems have been developed employing various search strategies, heuristics, language pattern limitations and hypothesis evaluation criteria, in order to cope with intractably large search space and to be able to generate highquality patterns. In this work, a new ILP-based concept discovery method is described in which userdefined specifications are relaxed. Moreover, this new method directly works on relational databases. In addition to this, a new confidence-based pruning is used in this technique. A set of experiments are conducted to test the performance of the new method

Discovering patterns for architecture simulation by using sequence mining

The goal of computer architecture research is to design and build high performance systems that make effective use of resources such as space and power. The design process typically involves a detailed simulation of the proposed architecture followed by corrections and improvements based on the simulation results. Both simulator development and result analysis are very challenging tasks due to the inherent complexity of the underlying systems. The motivation of this work is to apply episode mining algorithms to a new domain, architecture simulation, and to prepare an environment to make predictions about the performance of programs in different architectures. We describe our tool called Episode Mining Tool (EMT), which includes three temporal sequence mining algorithms, a preprocessor, and a visual analyzer. We present empirical analysis of the episode rules that were mined from datasets obtained by running detailed micro-architectural simulations