Organizations generate vast amounts of unstructured textual data – a valuable source of information that frequently remains underutilized for process mining. However, textual descriptions often record exceptions and manual activities absent from structured data, and therefore, enable a better understanding of deviations from the expected business process behavior. Importantly, unstructured sources typically retain the object-centric characteristics of real-world processes – information that gets flattened or lost in case-centric event logs. Yet, existing approaches primarily target structured data sources or produce case-centric event logs. To address this gap, we present an automated approach to derive object-centric event logs directly from unstructured textual descriptions. The approach comprises two subcomponents: a collector that identifies events and objects (including their attributes and relationships), and a refiner that consolidates and cleans the extracted information. We instantiate each subcomponent in heuristic and generative implementations and create four pairwise combinations of collector and refiner instances to assess the effectiveness of heuristic natural language processing and generative artificial intelligence techniques. We compare these variants quantitatively and qualitatively in a controlled, artificial setting based on synthesized texts and demonstrate the practical utility on two naturally occurring corpora (fire status updates and a legal judgment). Our results show that the configurations with a generative collector achieve the highest extraction quality. In particular, the fully generative variant produces coherent and standardized event and object labels. Overall, this study fills a notable research gap by enabling the incorporation of textual information into process mining applications. • Proposes an approach to extract object-centric event logs from textual descriptions. • Develops the approach using the Design Science Research methodology. • Implements the approach using heuristic NLP and generative AI techniques. • Evaluates the approach on synthetic and naturalistic textual descriptions. • Confirms the approach’s practical utility on fire status updates and a legal judgment.

Process mining analyzes process execution data to derive insights that support operational process improvement. However, event logs often suffer from poor data quality, typically resulting from process deficiencies, which can lead to inaccurate or misleading insights. To mitigate this risk, domain experts and process analysts engage in data validation during event data preparation to assess whether an event log is fit for its intended analytical purpose. Yet, current practices often fail to sufficiently align event logs with their analytical objectives, commonly formalized as analysis questions. This misalignment impedes the detection of data quality issues, which frequently vary across application domains and analytical contexts. Generative artificial intelligence offers promising capabilities in this regard, including adaptability to diverse contexts, the ability to interpret complex data, and the generation of context-aware recommendations. To leverage this potential, we adopt the Design Science Research paradigm to iteratively develop Artificial Intelligence-Assisted Data Validation For Domain Experts (AID4DE) that integrates domain knowledge — rooted in experts’ practical engagement with operational processes — with generative artificial intelligence support to facilitate interaction with complex event log data. We instantiate AID4DE as an open-source software prototype and evaluate it through a three-phase approach: a competing artifact analysis, 14 semi-structured expert interviews, and a user study involving 18 information systems researchers. Our results show that AID4DE is both applicable and effective in supporting domain experts in data validation, enabling the systematic externalization of domain knowledge and rigorous assessment of event log’s fitness for purpose. • Improved data validation for domain experts through artificial intelligence support. • Artificial intelligence derives event log understanding from semantic visual analysis. • Contextual guidance improves experts’ understanding and validation of event log data. • Instantiated prototype evaluated as useful and applicable in a real-world setting. • Artificial intelligence and domain knowledge support fitness for purpose evaluation.

A major goal in process mining is to analyze processes to determine possible improvements. However, event logs often bear substantial complexity, posing challenges for process analysts. Consequently, analysts often split event logs into more serviceable groups. While tool support is a crucial enabler for this task, and many approaches for event log analysis are available, a gap remains regarding tools and methods for organizing and structuring event logs. To address this gap, we propose the Case Group Explorer, an approach to support event log grouping using interaction and visualization computationally. We instantiate our artifact as a software prototype and evaluate it through a competing artifact analysis, the application on several event logs, and a user study involving 13 practitioners. Thus, we contribute by creating design knowledge for event log exploration and process group analysis at the intersection of process analysis and visual analytics.

Over the last years, the use of Internet of Things (IoT) systems in healthcare has increased due to technological advancements and increased availability of data. Sensor-based monitoring of physiological parameters, in particular, promises rich opportunities to promote overall health and self-management of patients suffering from chronic diseases. As such, neurogenic bladder patients lack sensation and control over their bladder while they could regain sovereignty over their bladder management through monitoring their physiological parameters. In this paper, we aim to develop a wearable IoT-based bladder level monitoring system for managing neurogenic bladder dysfunctions. We develop a set of design principles taking a stance from behaviour theory and implement the design principles in a software architecture following a design science research approach. Further, we evaluate and revise the developed artefact and implement a prototype of the software architecture. Our research contributes to IS research through prescriptive knowledge for IoT-based bladder level monitoring systems that can be transferred and generalised to similar areas of application. Further, we contribute to behaviour theory as we theorise a new type of trigger that we call a hybrid trigger.

A fundamental requirement for the successful application of process mining are event logs of high data quality that can be constructed from structured data stored in organizations’ core information systems. However, a substantial amount of data is processed outside these core systems, particularly in organizations doing consumer business with many customer interactions per day, which generate high amounts of unstructured text data. Although Natural Language Processing (NLP) and machine learning enable the exploitation of text data, these approaches remain challenging due to the required high amount of labeled training data. Recent advances in NLP mitigate this issue by providing pre-trained and ready-to-use language models for various tasks such as Natural Language Inference (NLI). In this paper, we develop an approach that utilizes NLI to derive topics and process activities from customer service conversations and that represents them in a standardized XES event log. To this end, we compute the probability that a sentence describing the topic or the process activity can be inferred from the customer’s inquiry or the agent’s response using NLI. We evaluate our approach utilizing an existing corpus of more than 500,000 customer service conversations of three companies on Twitter. The results show that NLI helps construct event logs of high accuracy for process mining purposes, as our successful application of three different process discovery algorithms confirms.

The Internet of Things (IoT) is recognised as one of the most disruptive technologies in the market as it integrates physical objects into the networked society. As such, the IoT also transforms established business-to-customer interactions. Remote patient monitoring, predictive maintenance, and automatic car repair are examples of evolving business-to-thing (B2T) interactions. However, the IoT is hardly covered by theoretical investigations. To complement the predominant technical and engineering focus of IoT research, we developed and evaluated a taxonomy of B2T interaction patterns. Thereby, we built on sociomateriality as justificatory knowledge. We demonstrated the taxonomy’s applicability and usefulness based on simple and complex real-life objects (i.e., Nest, RelayRides, and Uber). Our taxonomy contributes to the descriptive knowledge on the IoT as it enables the classification of B2T interactions and facilitates sense-making as well as theory-led design. When combining weak and strong sociomateriality, we found that the IoT enables and requires a new perspective on material agency by considering smart things as independent actors.