Using Event Logs for Local Correction of Process Models
During the life-cycle of an Information System (IS) its actual behavior may not correspond to the original system model. However, to the IS support it is very important to have the latest model that reflects the current system behavior. To correct the model the information from the event log of the system may be used.
In this paper, we consider the problem of process model adjustment (correction) using the information from event log. The input data for this task is the initial process model (a Petri net) and an event log. The result of correction should be a new process model, better reflecting the real IS behavior than the initial model.
The new model could be also built from scratch, for example, with the help of one of the known algorithms for automatic synthesis of the process model from an event log. However, this may lead to crucial changes in the structure of the original model, and it will be difficult to compare the new model with the initial one, hindering its understanding and analysis. Then it is important to keep the initial structure of the model as much as possible.
In this paper we propose a method for process model correction based on the principle of “divide and conquer”. The initial model is decomposed into several fragments. For each of the fragments its conformance to the event log is checked. Fragments, which do not match the log, are replaced by newly synthesized ones. The new model is then assembled from the fragments via transition fusion. The experiments demonstrate that our correction algorithm gives good results when it is used for correcting local discrepancies.
The paper presents the description of the algorithm, the formal justification for its correctness, as well as the results of experimental testing on artificial examples.
This book constitutes the proceedings of the 37th International Conference on Application and Theory of Petri Nets and Concurrency, PETRI NETS 2016, held in Toruń, Poland, in June 2016. Petri Nets 2016 was co-located with the Application of Concurrency to System Design Conference, ACSD 2016. The 16 papers including 3 tool papers with 4 invited talks presented together in this volume were carefully reviewed and selected from 42 submissions. Papers presenting original research on application or theory of Petri nets, as well as contributions addressing topics relevant to the general field of distributed and concurrent systems are presented within this volume.
These are the proceedings of the International Workshop on Petri Nets and Software Engineering (PNSE’13) and the International Workshop on Modeling and Business Environments (ModBE’13) in Milano, Italy, June 24–25, 2013. These are co-located events of Petri Nets 2013, the 34th international conference on Applications and Theory of Petri Nets and Concurrency.
PNSE'13 presents the use of Petri Nets (P/T-Nets, Coloured Petri Nets and extensions) in the formal process of software engineering, covering modelling, validation, and veriﬁcation, as well as their application and tools supporting the disciplines mentioned above.
ModBE’13 provides a forum for researchers from interested communities to investigate, experience, compare, contrast and discuss solutions for modeling in business environments with Petri nets and other modeling techniques.
Resource-driven automata (RDA) are finite automata, sitting in the nodes of a finite system net and asynchronously consuming/producing shared resources through input/output system ports (arcs of the system net). RDAs themselves may be resources for each other, thus allowing the highly flexible structure of the model. It was proved earlier, that RDA-nets are expressively equivalent to Petri nets. In this paper the new formalism of cellular RDAs is introduced. Cellular RDAs are RDA-nets with an infinite regularly structured system net. We build a hierarchy of cellular RDA classes on the basis of restrictions on the underlying grid. The expressive power of several major classes of 1-dimensional grids is studied.
Operational processes leave trails in the information systems supporting them. Such event data are the starting point for process mining – an emerging scientific discipline relating modeled and observed behavior. The relevance of process mining is increasing as more and more event data become available. The increasing volume of such data (“Big Data”) provides both opportunities and challenges for process mining. In this paper we focus on two particular types of process mining: process discovery (learning a process model from example behavior recorded in an event log) and conformance checking (diagnosing and quantifying discrepancies between observed behavior and modeled behavior). These tasks become challenging when there are hundreds or even thousands of different activities and millions of cases. Typically, process mining algorithms are linear in the number of cases and exponential in the number of different activities. This paper proposes a very general divide-and-conquer approach that decomposes the event log based on a partitioning of activities. Unlike existing approaches, this paper does not assume a particular process representation (e.g., Petri nets or BPMN) and allows for various decomposition strategies (e.g., SESE- or passage-based decomposition). Moreover, the generic divide-and-conquer approach reveals the core requirements for decomposing process discovery and conformance checking problems.
BPM 2013 was the 11th conference in a series that provides a prestigious forum for researchers and practitioners in the field of business process management (BPM). The conference was organized by Tsinghua University, China, and took place during August 26–30, 2013, in Beijing, China. Compared to previous editions of BPM, this year we noted a lower focus by authors on topics like process modeling, while we also observed a considerable growth of submissions regarding areas like process mining, conformance/compliance checking, and process model matching. The integrated consideration of processes and data remains popular, and novel viewpoints focus, among others, on data completeness in business processes, the modeling and runtime support of event streaming in business processes, and business process architectures.
Process mining is a relatively new field of computer science which deals with process discovery and analysis based on event logs. In this work we consider the problem of discovering workflow nets with cancellation regions from event logs. Cancellations occur in the majority of real-life event logs. In spite of huge amount of process mining techniques little has been done on cancellation regions discovery. We show that the state-based region algorithm gives labeled Petri nets with overcomplicated control flow structure for logs with cancellations. We propose a novel method to discover cancellation regions from the transition systems built on event logs and show the way to construct equivalent workflow net with reset arcs to simplify the control flow structure.
In this work we consider modeling of services with workflow modules, which are a subclass of Petri nets. The service compatibility problem is to answer the question, whether two Web services fit together, i.e. whether the composed system is sound. We study complementarity of service produced/consumed resources, that is a necessary condition for the service compatibility. Resources, which are produced/consumed by a Web service, are described as a multiset language. We define an algebra of multiset languages and present an algorithm for checking the conformance of resources for two given structured workflow modules.
Recent breakthroughs in process mining research make it possible to discover, analyze, and improve business processes based on event data. The growth of event data provides many opportunities but also imposes new challenges. Process mining is typically done for an isolated well-defined process in steady-state. However, the boundaries of a process may be fluid and there is a need to continuously view event data from different angles. This paper proposes the notion of process cubes where events and process models are organized using different dimensions. Each cell in the process cube corresponds to a set of events and can be used to discover a process model, to check conformance with respect to some process model, or to discover bottlenecks. The idea is related to the well-known OLAP (Online Analytical Processing) data cubes and associated operations such as slice, dice, roll-up, and drill-down. However, there are also significant differences because of the process-related nature of event data. For example, process discovery based on events is incomparable to computing the average or sum over a set of numerical values. Moreover, dimensions related to process instances (e.g. cases are split into gold and silver customers), subprocesses (e.g. acquisition versus delivery), organizational entities (e.g. backoffice versus frontoffice), and time (e.g., 2010, 2011, 2012, and 2013) are semantically different and it is challenging to slice, dice, roll-up, and drill-down process mining results efficiently.
The problem of management of the nonlinear object which is exposed to impact of uncontrollable indignations, is considered in a key of differential game. Synthesis of optimum managements is made with application of transformation of the nonlinear equation of initial object in the differential equation with the parameters depending on a condition. The square-law functional of quality allows to formulate synthesis conditions in the form of need of search of solutions of the equation of Rikkati. The solution of the equation of Rikkati with the parameters depending on a condition, is in a symbolical view with application of algebraic methods that allows to generalize a number of earlier published theoretical results, to receive rather constructive decisions in a number of statements of problems of management.
The article is based upon the fact that the growing demand for master data management systems has not yet produced a commonly accepted metodology for their design and development/ The article offers two mathematical models? that allow a master data management systems designer a way to formally describe their system before development and verify the system quality by measurements? unique to master data management systems.