BPM 2017 International Workshops, Barcelona, Spain, September 10-11, 2017, Revised Papers
E-government web services are becoming increasingly popular among citizens of various countries. Usually, to receive a service, the user has to perform a sequence of steps. This sequence of steps forms a service rendering process. Using process mining techniques this process can be discovered from the information system’s event logs. A discovered process model of a real user behavior can assist in the analysis of service usability. Thus, for popular and well-designed services this process model will coincide with a reference process model of the expected user behavior. While for other services the observed real behavior and the modeled expected behavior can differ significantly. The main aim of this work is to suggest an approach for the comparison of process models and evaluate its applicability when applied to real-life e-government services.
Process mining techniques relate observed behavior to modeled behavior, e.g., the automatic discovery of a process model based on an event log. Process mining is not limited to process discovery and also includes conformance checking and model enhancement. Conformance checking techniques are used to diagnose the deviations of the observed behavior as recorded in the event log from some process model. Model enhancement allows to extend process models using additional perspectives, conformance and performance information. In recent years, BPMN (Business Process Model and Notation) 2.0 has become a de facto standard for modeling business processes in industry. This paper presents the BPMN support current in ProM. ProM is the most known and used open-source process mining framework. ProM’s functionalities of discovering, analyzing and enhancing BPMN models are discussed. Support of the BPMN 2.0 standard will help ProM users to bridge the gap between formal models (such as Petri nets, causal nets and others) and process models used by practitioners.
This book constitutes the proceedings of the First Asia Pacific Conference on Business Process Management held in Beijing, China, in August 2013.
In all, 19 contributions from seven countries were submitted. Following an extensive review process by an international Program Committee, seven full papers and one short paper were accepted for publication in this book and presentation at the conference. In addition, a keynote by Wil van der Aalst is also included.
To large organizations, business intelligence (BI) promises the capability of collecting and analyzing internal and external data to generate knowledge and value, thus providing decision support at the strategic, tactical, and operational levels. BI is now impacted by the “Big Data” phenomena and the evolution of society and users. In particular, BI applications must cope with additional heterogeneous (often Web-based) sources, e.g., from social networks, blogs, competitors’, suppliers’, or distributors’ data, governmental or NGO-based analysis and papers, or from research publications. In addition, they must be able to provide their results also on mobile devices, taking into account location-based or time-based environmental data. The lectures held at the Third European Business Intelligence Summer School (eBISS), which are presented here in an extended and refined format, cover not only established BI and BPM technologies, but extend into innovative aspects that are important in this new environment and for novel applications, e.g., pattern and process mining, business semantics, Linked Open Data, and large-scale data management and analysis. Combining papers by leading researchers in the field, this volume equips the reader with the state-of-the-art background necessary for creating the future of BI. It also provides the reader with an excellent basis and many pointers for further research in this growing field.
Since their inception in 1962, Petri nets have been used in a wide variety of application domains. Although Petri nets are graphical and easy to understand, they have formal semantics and allow for analysis techniques ranging from model checking and structural analysis to process mining and performance analysis. Over time Petri nets emerged as a solid foundation for Business Process Management (BPM) research. The BPM discipline develops methods, techniques, and tools to support the design, enactment, management, and analysis of operational business processes. Mainstream business process modeling notations and workflow management systems are using token-based semantics borrowed from Petri nets. Moreover, state-of-the-art BPM analysis techniques are using Petri nets as an internal representation. Users of BPM methods and tools are often not aware of this. This paper aims to unveil the seminal role of Petri nets in BPM.
Key performance indicators (KPI) present an effective, high-precision framework for continuous monitoring of target achievements. The KPI framework has proved itself to be a functional tool within different economic sectors in series of large and mid-size companies. Notwithstanding all ad- vantages and effects of the implementation of KPI framework, it cannot be used for assessment and monitoring of operational risk level that was accepted for achievement of defined targets. Switching towards a risk-balanced approach in process management requires the definition and implementation of framework for monitoring the compliance of company risk-profile with its risk-tolerance. This requirement results in a key risk indicators KRI framework. The article provides an example of deve- loping a set of KRI for internal information system.
The article describes the features of an enterprise’s business process management that concerns ad-hoc processes. The analysis of the possible implementation problems in ECM system is shown and ways of overcoming.
This article is devoted to study of management evolution in approaches for Business Process Management (BPM). Research is based on the taking into account 2 factors: standardization of approaches for BPM; and scope of enterprise as reflexive management. Authors used own consulting experience and intermediate results of the scientific researches carried out in State University HSE.
Case Management is the management of collaborative processes that coordinate content, knowledge, and resources to progress a business to achieve a particular goal, where the path of execution is often unpredictable and where human judgment has significant influence for determination of how the end goal can be achieved. The key characteristics of Case Management include: information complexity, knowledge-intensive, and variability. The knowledge-driven economy brings new challenges for business. Markets and business-processes are becoming more global, customers are more demanding, and product life cycles are shortening. The complexity of technologies, including Information Communication Technologies (ICT), is increasing. So while the knowledge economy represents new opportunities, certain actions are needed to support and take advantage of these developments. This evolution can be enhanced by the adoption of Case Management that has reduced the cost of gathering and disseminating knowledge. The contribution of Advanced Case Management (ACM) to innovation has been achieved most notably by reducing transaction costs between companies and other actors, especially in areas such as information search and buying. The main goal of this theoretical study is to evaluate the role of contemporary information systems (IS) and technologies for supporting Case Management as tool for forming corporate knowledge. Attention of many scientists and researchers in this subject field is focused on the study of Customer Relationship Management (CRM), Business Process Management (BPM), and Enterprise Content Management (ECM) or Electronic Document and Record Management (EDRMS) systems. But these technologies and systems are not sufficient to address the key problems. Enterprise Content Management and Business Process Management (BPM) with specific support for knowledge intensive processes can be discussed as more appropriate solution to Case Management successful implementation and use. BPM-based Case Management can take into account unpredictable or uncertain nature of cases and effectively combine processes and knowledge. It can consider as innovation in Data Management.
I give the explicit formula for the (set-theoretical) system of Resultants of m+1 homogeneous polynomials in n+1 variables