Mathias Weske

Alessandro Gianola, Zeeshan Hameed, Marco Montali et al.

Object-centric process mining examines how processes interact with multiple co-evolving objects, and has gained great interest in recent years. However, object-centric event logs (OCELs) leave object relationships underspecified in several respects, especially if relationships are dynamic, i.e., they change over time. In this paper, we identify and formally define for the first time assumptions that allow to represent and manipulate dynamic relationships in OCELs in a semantically unambiguous way. We evaluate existing logs to show that our assumptions are often satisfied, ensuring full transparency of relationship semantics.

Timotheus Kampik, Mathias Weske

This paper presents the results of an industry expert survey about event log generation in process mining. It takes academic assumptions as a starting point and elicits practitioner's assessments of statements about process execution, process scoping, process discovery, and process analysis. The results of the survey shed some light on challenges and perspectives around event log generation, as well as on the relationship between process models and process execution, and derive challenges for event log generation from it. The responses indicate that particularly relevant challenges exist around data integration and quality, and that process mining can benefit from a systematic integration with more traditional and wide-spread business intelligence approaches.

Luise Pufahl, Sven Ihde, Fabian Stiehle et al.

For delivering products or services to their clients, organizations execute manifold business processes. During such execution, upcoming process tasks need to be allocated to internal resources. Resource allocation is a complex decision-making problem with high impact on the effectiveness and efficiency of processes. A wide range of approaches was developed to support research allocation automatically. This systematic literature survey provides an overview of approaches and categorizes them regarding their resource allocation goals and capabilities, their use of models and data, their algorithmic solutions, and their maturity. Rule-based approaches were identified as dominant, but heuristics and learning approaches also play a relevant role.

Jan Ladleif, Mathias Weske

First come, first served: Critical choices between alternative actions are often made based on events external to an organization, and reacting promptly to their occurrence can be a major advantage over the competition. In Business Process Management (BPM), such deferred choices can be expressed in process models, and they are an important aspect of process engines. Blockchain-based process execution approaches are no exception to this, but are severely limited by the inherent properties of the platform: The closed-world environment prevents direct access to external entities and data, and the passive runtime based entirely on atomic transactions impedes continual monitoring and detection of events. In this paper we provide an in-depth examination of the semantics of deferred choice, and transfer them to environments such as the blockchain. We introduce and compare several oracle architectures able to satisfy certain requirements, and show that they can be implemented using state-of-the-art blockchain technology.

Stephan Haarmann, Marco Montali, Mathias Weske

Traditionally, business process management focuses on structured, imperative processes. With the increasing importance of knowledge work, semi-structured processes are entering center stage. Existing approaches to modeling knowledge-intensive business processes use data objects but fail to sufficiently take into account data object cardinalities. Hence, they cannot guarantee that cardinality constraints are respected, nor use such constraints to handle concurrency and multiple activity instances during execution. This paper extends an existing case management approach with data object associations and cardinality constraints. The results facilitate a refined data access semantics, lower and upper bounds for process activities, and synchronized processing of multiple data objects. The execution semantics is formally specified using colored Petri nets. The effectiveness of the approach is shown by a compiler translating case models to colored Petri nets and by a dedicated process execution engine.

Jan Ladleif, Mathias Weske

The traceable execution of business processes and choreographies using smart contracts is one prominent application of blockchain technology in Business Process Management (BPM). Existing approaches support a large set of patterns, modeling languages, and blockchain architectures, which cover a wide range of practical scenarios. However, they largely neglect the important aspect of time, a crucial part of process and choreography models manifested in deadlines, delays, and other temporal constraints. We argue that this deficit is due to inherent limitations of smart contracts---in particular the absence of a natural notion of measuring time---on popular blockchain platforms used in research and practice. We introduce a set of time measures available on blockchain platforms to alleviate these issues, and systematically compare their properties. We also give hints as to their suitability for facilitating various temporal constraints commonly found in process models.

Jan Mendling, Ingo Weber, Wil van der Aalst et al.

Blockchain technology promises a sizable potential for executing inter-organizational business processes without requiring a central party serving as a single point of trust (and failure). This paper analyzes its impact on business process management (BPM). We structure the discussion using two BPM frameworks, namely the six BPM core capabilities and the BPM lifecycle. This paper provides research directions for investigating the application of blockchain technology to BPM.