Ob jectivity in Process Descriptions

Abstract

Process models are central artifacts for many business process management activities. They are often manually crafted, which means that modelers capture many details in the way they consider appropriate { but the problem also applies to discovered models. We, therefore, argue that we need objectivity of granularity level, objectivity of perspective, and objectivity of terminology to enable broader use of models, like comparing processes. This is currently not available, which is a roadblock for automatic analysis, empirical research, and generally use for purposes that di er from the initial model creation purpose.

1 Introduction

Process models are central artefacts for many business process management (BPM) activities and provide a foundation for the design, documentation, analysis, automation, and optimization of business processes [ 1 ]. Traditionally, process models have been manually created and kept up to date by modelers. Nowadays, increasingly process discovery techniques from the eld of process mining are used to automatically derive models from discrete event data. Depending on the degree of BPM adoption, organizations might establish collections consisting of thousands of process models.

In essence, process models provide concise and selective representations of business processes, as they abstract from many details and express speci c aspects, whose relevance depends on the models' purpose, through a few elements for which short labels provide brief natural language descriptions. Moreover, independent of whether a process model is created manually or through discovery, it provides a selective view. In the case of manual creation, this selectivity stems from the fact that modelers express their own perception in a way they deem appropriate. Although discovery algorithms follow precise rules to transform data into process models, selectivity arises when information needs are translated into operations that extract, preprocess, and analyze the data [ 2 ].

To create process models, modelers can rely on notations such as BPMN, EPCs, Petri Nets, etc., which de ne the types of elements that can be used to describe processes. They can also resort to guidelines that outline how to apply those notations so that the resulting models are of a high quality, e.g., those Copyright © 2021 for this paper by its authors. Use permitted Creative Commons License Attribution 4.0 International (CC BY 4.0). under in [ 3, 4 ]. However, the creation of models is more art than science. That is because available notations, methods, and tools abstract from the model content and do not provide guidance for how to handle selectivity when capturing processes. This freedom during modeling exacerbates the e ective utilization of models within the BPM lifecycle, especially when model usage and interpretation are negatively impacted by the absence or ambiguous description of important aspects. For example, the authors in [ 5 ] attempted to consolidate a set of process models, but were challenged by the versatile labeling of similar activities. Similarly, the empirical study in [ 6 ] demonstrated that modelers tend to express aspects via natural language although appropriate modeling elements for those aspects are available. In this regard, it is important to stress that discovered models are not necessarily easier to understand than manually created models [ 7 ].

Next, we summarize fundamental challenges surrounding this problem and discuss their impact on existing work. We then outline possible future directions.

2 Existing Work and Challenges

The core of the problem can be traced back to models being concise, selective and arguably subjective process representations, or in other words to a lack of objectivity in the following senses: Objectivity of levels of granularity: So far, there are no objective levels of granularity for describing a business process. If we accept that a process is something that can be decomposed into subprocesses [ 8 ], which may also be referred to as activities, tasks, steps, phases, stages, etc., then we observe that processes have been described and analyzed at the macro level (developments of companies over decades [ 9 ] or careers of famous musicians [ 10 ]), meso level (order-to-cash processes [ 11 ] or healthcare pathways [ 12 ]), and micro level (keystroke sequences [ 13 ] or scrolling of a computer user [ 14 ]). Modelers can choose di erent levels of granularity depending on what they deem appropriate for a given modeling purpose. With this observation, we do not mean to suggest that all models should be created on the same level of granularity; how to possibly react to the situation observed will be described in the next section.

Objectivity of perspectives: So far, there are no objective perspectives for describing business processes. One speci c instance of this problem is the discussion of local and global views of business processes [ 15 ] and the usage of pools (blackbox or whitebox) versus lanes in BPMN [ 11 ]. Modelers construct views and system boundaries around passages of a process that they deem relevant for a given task at hand.

Objectivity of terminology: So far, there are no objectively de ned terms available for describing business processes and the elements in process models. This so-called vocabulary problem is fundamental and not speci c to business processes [ 16, 17 ]. Even if we refer to the same matter, we can use homonyms and synonyms [ 18 ] and describe activities from the perspective of what they aim towards, how they are done, or what they achieve [ 19 ]. Modelers are free to choose terminology in process models based on what they deem appropriate in a speci c context.

These challenges have implications for various semantic application scenarios of business process models [ 20 ], e.g., for process model matching where algorithms are designed that automatically identify correspondences between models, i.e, activities that represent similar functionality. Process model matching has turned out to be a fundamentally hard problem and provides a perfect example for illustrating the consequences of the lack of objectivity in process modeling.

Despite the substantial attention that process model matching received, the solutions approaches that were developed have not yet yielded satisfactory and practically usable performance, as prominently demonstrated in the process model matching contests in 2013 [ 21 ] and 2015 [ 22 ]. Here, matching techniques were compared in a competitive setting and overall achieved a moderate e ectiveness. This performance is a result of a low recall, i.e., matchers only identify a small portion of the existing correspondences. Generally, the most plausible strategy to lift recall is by sacri cing precision, i.e., by allowing matchers to propose a substantial amount of incorrect correspondences. This performance is a direct result of the lack of objectivity with which models are created. That is, when implementing matchers, developers can only resort to general-purpose, o -the-shelf knowledge bases and techniques, but the matchers themselves need to interpret less objective process models with heterogeneous labeling styles, domain terminology, etc. [ 23 ]. Moreover, the same control ow can be expressed in various ways. This means that the control ow relationships have a limited explanatory power for correspondences, as con rmed by empirical evidence [ 24 ].

A promising direction for improving the e ectiveness is to learn from user feedback [ 25 ]. However, such a setup in the end means that instead of algorithms, it is the model creators and users who have to make sense of the models. In this regard, several studies, e.g., in [ 26, 27 ], demonstrated that humans also face challenges when interpreting models, often arriving at diverging views regarding the existing correspondences between the same pair of process models.

3 Future Directions

The creation and interpretation of models in general and of process models in particular has been an active research area for decades, resulting in a broad range of notations, practices, (anti-)patterns, and tools. Contrasting those e orts and outcomes with the severity of the problems around objectivity, it is hard to devise speci c ideas for advancing the body of knowledge in this direction. Part of the problem is that selectivity is not only a bug, but to a degree also a feature: each model is created for a purpose, like documentation or performance analysis. What should be part of the model and what can be abstracted from depends on this very purpose, and impacts granularity level, perspective, and vocabulary. While vocabulary for a given context could be objecti ed through use of ontologies, dictionaries, or glossaries, this is not the case for the perspective and granularity dimensions, given their dependence on the purpose. A rst step to addressing the problem could be the generation of taxonomies for these dimensions, and mapping of process models to taxonomy elements.

In general, research into this topic could bene t from more publicly available data in terms of large process model collections, protocols of how individuals translate processes into models, or records of how process model collections evolve over time. This is not to say that there have not been attempts to establish collections of real-world data, an endeavour that is often hampered by contractual obligations. For example, the SAP reference model has been studied in many publications; the process model matching contests [ 22, 21 ] provided process model collections along with gold standards that de ne the correspondence relationships in the models; Signavio's BPM Academic Initiative is providing access to models that users of the platform contributed to the initiative [ 28 ]; the annual Business Process Intelligence Challenge provides real-world event logs and publishes the contestants' analysis reports which contain protocols and interpretations for process discovery results; and the BPM conference is encouraging researchers to adopt open science principles and to submit resource papers.

The availability of extensive data collections could then be used to study similarities between process models and, in general, how they can be systematically made more comparable. For example, based on manually identi ed correspondence relationships, qualitative content analysis and data mining could help to better understand the di erent ways in which concrete aspects can be expressed and to derive objective ways for modeling those aspects, potentially using new paradigms. In this regard, it would be bene cial to forgo the common practice of relying on binary correspondence relationships. Instead, more insights might be derived when diverging views of multiple analysts are considered, and with more detailed information regarding the nature of correspondence relationships, e.g., in terms of similarity scores, classi cations, or open-ended descriptions.

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