Imperative versus Declarative Process Modeling Languages: An Empirical Investigation

Dumas

García‐Bañuelos

et al. 2013

Abstract. A wealth of techniques are available to automatically discover business process models from event logs. However, the bulk of these techniques yield procedural process models that may be useful for detailed analysis, but do not necessarily provide a comprehensible picture of the process. Additionally, barring few exceptions, these techniques do not take into account data attributes associated to events in the log, which can otherwise provide valuable insights into the rules that govern the process. This paper contributes to filling these gaps by proposing a technique to automatically discover declarative process models that incorporate both control-flow dependencies and data conditions. The discovered models are conjunctions of first-order temporal logic expressions with an associated graphical representation (Declare notation). Importantly, the proposed technique discovers underspecified models capturing recurrent rules relating pairs of activities, as opposed to full specifications of process behavior -thus providing a summarized view of key rules governing the process. The proposed technique is validated on a real-life log of a cancer treatment process.

Section: Introductionmentioning

confidence: 99%

Discovering Data-Aware Declarative Process Models from Event Logs

Dumas

García‐Bañuelos

et al. 2013

“…Building on our proposal, it is possible to design experiments that provide declarative, imperative, and hybrid models of the same process, which can be compared with respect to the relative ease of understanding these. There is a rich stream of experimental research that can inspire such experiments [19,20]. In a similar vein, it is interesting to determine how easy it is for modellers to apply hybrid notations.…”

Section: Resultsmentioning

confidence: 99%

“…Recent research has again put into evidence synergies between imperative and declarative approaches [19,21]. Accordingly, hybrid process modelling notations have been proposed.…”

Section: Hybrid Languagesmentioning

confidence: 99%

The Semantics of Hybrid Process Models

Slaats

Schunselaar

et al. 2016

Self Cite

The semantics of hybrid process models Slaats, Tijs; Schunselaar, Dennis M. M.; Maggi, Fabrizio M.; Reijers, Hajo A. Abstract. In the area of business process modelling, declarative notations have been proposed as alternatives to notations that follow the dominant, imperative paradigm. Yet, the choice between an imperative or declarative style of modelling is not always easy to make. Instead, a mixture of these styles is sometimes preferable. This observation has underpinned recent calls for so-called hybrid process modelling notations.In this paper, we present a formal semantics for these. In our proposal, a hybrid process model is hierarchical, where each of its sub-processes may be specified in either an imperative or declarative fashion. The semantics we provide will allow modelling communities to build on the benefits of existing imperative and declarative modelling notations, instead of spending their energy on inventing new ones.

“…Processes working in stable environments are typically highly predictable, i.e., it is easy to determine in advance the way how processes execute and behave (e.g., a process for handling travel requests). Procedural languages, such as BPMN, UML ADs, EPCs, and Petri nets, are suitable for describing such processes because it is easy to explicitly represent all allowed behavior of the process at hand [4,22]. In contrast, processes operating in flexible/turbulent environments are often more complex and less predictable.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Declare Maps Based on Event Correlations

Bose

Aalst

2013

Abstract. Traditionally, most process mining techniques aim at discovering procedural process models (e.g., Petri nets, BPMN, and EPCs) from event data. However, the variability present in less-structured flexible processes complicates the discovery of such procedural models. The "open world" assumption used by declarative models makes it easier to handle this variability. However, initial attempts to automatically discover declarative process models result in cluttered diagrams showing misleading constraints. Moreover, additional data attributes in event logs are not used to discover meaningful causalities. In this paper, we use correlations to prune constraints and to disambiguate event associations. As a result, the discovered process maps only show the more meaningful constraints. Moreover, the data attributes used for correlation and disambiguation are also used to find discriminatory patterns, identify outliers, and analyze bottlenecks (e.g., when do people violate constraints or miss deadlines). The approach has been implemented in ProM and experiments demonstrate the improved quality of process maps and diagnostics.