1,485 research outputs found
What is Business Process Automation Anyway?
Many organizations strive to increase the level of automation in their business processes. While automation historically was mainly concerned with automating physical labor, current automation efforts mostly focus on automation in a digital manner, thus targeting work that is related to the interaction between humans and computers. This type of automation, commonly referred to as business process automation, has many facets. Yet, academic literature mainly focuses on Robotic Process Automation, a specific automation capability. Recognizing that leading vendors offer automation capabilities going way beyond that, we use this paper to develop a detailed understanding of business process automation in industry. To this end, we conduct a structured market analysis of the 18 predominant vendors of business process automation solutions as identified by Gartner. As a result, we provide a comprehensive overview of the business process automation capabilities currently offered by industrial vendors. We show which types and facets of automation exist and which aspects represent promising directions for the future
Trace Clustering for User Behavior Mining
Business information systems support a large variety of business processes and tasks, yet organizations rarely understand how users interact with these systems. User Behavior Mining aims to address this by applying process mining techniques to UI logs, i.e., detailed records of interactions with a system\u27s user interface. Insights gained from this type of data hold great potential for usability engineering and task automation, but the complexity of UI logs can make them challenging to analyze. In this paper, we explore trace clustering as a means to structure UI logs and reduce this complexity. In particular, we apply different trace clustering approaches to a real-life UI log and show that the cluster-level process models reveal useful information about user behavior. At the same time, we find configurations in which trace clustering fails to generate satisfactory partitions. Our results also demonstrate that recently proposed representation learning techniques for process traces can be effectively employed in a realistic setting
A probabilistic evaluation procedure for process model matching techniques
Process model matching refers to the automatic identification of corresponding activities between two process models. It represents the basis for many advanced process model analysis techniques such as the identification of similar process parts or process model search. A central problem is how to evaluate the performance of process model matching techniques. Current evaluation methods require a binary gold standard that clearly defines which correspondences are correct. The problem is that often not even humans can agree on a set of correct correspondences. Hence, evaluating the performance of matching techniques based on a binary gold standard does not take the true complexity of the matching problem into account and does not fairly assess the capabilities of a matching technique. In this paper, we propose a novel evaluation procedure for process model matching techniques. In particular, we build on the assessments of multiple annotators to define the notion of a non-binary gold standard. In this way, we avoid the problem of agreeing on a single set of correct correspondences. Based on this non-binary gold standard, we introduce probabilistic versions of precision, recall, and F-measure as well as a distance-based performance measure. We use a dataset from the Process Model Matching Contest 2015 and a total of 16 matching systems to assess and compare the insights that can be obtained by using our evaluation procedure. We find that our probabilistic evaluation procedure allows us to gain more detailed insights into the performance of matching systems than a traditional evaluation based on a binary gold standard
Challenges and opportunities of applying natural language processing in business process management
The Business Process Management (BPM) field focuses in the coordination of labor so that organizational processes are smoothly executed in a way that products and services are properly delivered. At the same time, NLP has reached a maturity level that enables its widespread application in many contexts, thanks to publicly available frameworks. In this position paper, we show how NLP has potential in raising the benefits of BPM practices at different levels. Instead of being exhaustive, we show selected key challenges were a successful application of NLP techniques would facilitate the automation of particular tasks that nowadays require a significant effort to accomplish. Finally, we report on applications that consider both the process perspective and its enhancement through NLP.Peer ReviewedPostprint (published version
From OCEL to DOCEL -- Datasets and Automated Transformation
Object-centric event data represent processes from the point of view of all
the involved object types. This perspective has gained interest in recent years
as it supports the analysis of processes that previously could not be
adequately captured, due to the lack of a clear case notion as well as an
increasing amount of output data that needs to be stored. Although publicly
available event logs are crucial artifacts for researchers to develop and
evaluate novel process mining techniques, the currently available
object-centric event logs have limitations in this regard. Specifically, they
mainly focus on control-flow and rarely contain objects with attributes that
change over time, even though this is not realistic, as the attribute values of
objects can be altered during their lifecycle. This paper addresses this gap by
providing two means of establishing object-centric datasets with dynamically
evolving attributes. First, we provide event log generators, which allow
researchers to generate customized, artificial logs with dynamic attributes in
the recently proposed DOCEL format. Second, we propose and evaluate an
algorithm to convert OCEL logs into DOCEL logs, which involves the detection of
event attributes that capture evolving object information and the creation of
dynamic attributes from these. Through these contributions, this paper supports
the advancement of object-centric process analysis by providing researchers
with new means to obtain relevant data to use during the development of new
techniques
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