Finite state transducers over semigroups can be regarded as a formal model of sequential reactive programs. In this paper we introduce a uniform tech- nique for checking eectively functionality, k-valuedness, equivalence and inclusion for this model of computation in the case when a semigroup these transducers op- erate over is embeddable in a decidable group.
Process-aware information systems (PAIS) are systems relying on processes, which involve human and software resources to achieve concrete goals. There is a need to develop approaches for modeling, analysis, improvement and monitoring processes within PAIS. These approaches include process mining techniques used to discover process models from event logs, find log and model deviations, and analyze performance characteristics of processes. The representational bias (a way to model processes) plays an important role in process mining. The BPMN 2.0 (Business Process Model and Notation) standard is widely used and allows to build conventional and understandable process models. In addition to the flat control flow perspective, subprocesses, data flows, resources can be integrated within one BPMN diagram. This makes BPMN very attractive for both process miners and business users. In this paper, we describe and justify robust control flow conversion algorithms, which provide the basis for more advanced BPMN-based discovery and conformance checking algorithms. We believe that the results presented in this paper can be used for a wide variety of BPMN mining and conformance checking algorithms. We also provide metrics for the processes discovered before and after the conversion to BPMN structures. Cases for which conversion algorithms produce more compact or more involved BPMN models in comparison with the initial models are identified.
Process mining is a research area dealing with, inter alia, the construction of models of various types from event logs. Fuzzy maps are an example of such models produced by different process mining tools, such as ProM and Disco. We proposed a new approach to mining fuzzy models which is based on logs representation in the form of relation databases. Fast and effective SQL queries to such logs are made as a part of a DPMine workflow model. Resulting datasets are processed and visualized by a special DPMine component working tightly integrated with VTMine modeling framework. The paper discusses the suggested approach in the context of customization aspects of VTMine framework with an embedded DPM engine.
Process models and graphs are commonly used for modeling and visualization of processes. They may represent sets of objects or events linked with each other in some way. Wide use of models in such languages engenders necessity of tools for creating and editing them. This paper describes the model editor which allows for dealing with classical graphs, Petri nets, finite-state machines and their systems. Additionally, the tool has a list of features like simulation of Petri nets, import and export of models in different storage formats. Carassius is a modular tool which can be extended with, for example, new formalisms. In the paper one can find a detailed description of a couple of layout algorithms that can be used for visualizing Petri nets and graphs. Carassius might be useful for educational and research purposes because of its simplicity, range of features and variety of supported notations.
In this paper we examine how it is possible to control Petri net behavior with the help of transition priorities. Controlling here means forcing a process to behave in a stable way by ascribing priorities to transitions and hence transforming a classic Petri net into a Priority Petri net. For Petri net models stability is often ensured by liveness and boundedness. These properties are crucial in many application areas, e.g. workflow modeling, embedded systems design, and bioinformatics. In this paper we study the problem of transforming a given live, but unbounded Petri net into a live and bounded one by adding priority constraints. We specify necessary conditions for the solvability of this problem and present an algorithm for ascribing priorities to net transitions in such a way that the resulting net becomes bounded while staying live.
Process mining is a relatively new field of computer science, which deals with process discovery and analysis based on event logs. In this paper we consider the problem of models and event logs conformance checking. Conformance checking is intensively studied in the frame of process mining research, but only models and event logs of the same granularity were considered in the literature. Here we present and justify the method of checking conformance between a high-level model (e.g. built by an expert) and a low-level log (generated by a system).
This paper is dedicated to a tool whose aim is to facilitate process mining experiments and evaluation of the repair algorithms. Process mining is a set of approaches which provides solutions and algorithms for discovery and analysis of business process models based on event logs. Process mining has three main areas of interest: model discovery, conformance checking and enhancement. The paper focuses on the latter. The goal of enhancement process is to refine an existing process model in order to make it adhere event logs. The particular approach of enhancement considered in the paper is called decomposed model repair. Although the paper is devoted to the implementation part of the approach, theoretical preliminaries essential for domain understanding are provided. Moreover, a typical use case of the tool is shown as well as guides to extending the tool and enriching it with extra algorithms and functionality. Finally, other solutions which can be used for implementation of repair schemes are considered, pros and cons of using them are mentioned.