Supporting process model validation through natural language generation

The design and development of process-aware information systems is often supported by specifying requirements as business process models. Although this approach is generally accepted as an effective strategy, it remains a fundamental challenge to adequately validate these models given the diverging skill set of domain experts and system analysts. As domain experts often do not feel confident in judging the correctness and completeness of process models that system analysts create, the validation often has to regress to a discourse using natural language. In order to support such a discourse appropriately, so-called verbalization techniques have been defined for different types of conceptual models. However, there is currently no sophisticated technique available that is capable of generating natural-looking text from process models. In this paper, we address this research gap and propose a technique for generating natural language texts from business process models. A comparison with manually created process descriptions demonstrates that the generated texts are superior in terms of completeness, structure, and linguistic complexity. An evaluation with users further demonstrates that the texts are very understandable and effectively allow the reader to infer the process model semantics. Hence, the generated texts represent a useful input for process model validation

25 Desafíos de la Modelación de Procesos Semánticos

Process modeling has become an essential part of many organizations for documenting, analyzing and redesigning their business operations and to support them with suitable information systems. In order to serve this purpose, it is important for process models to be well grounded in for- mal and precise semantics. While behavioural semantics of process models are well understood, there is a considerable gap of research into the semantic aspects of their text labels and natural lan- guage descriptions. The aim of this paper is to make this research gap more transparent. To this end, we clarify the role of textual content in process models and the challenges that are associated with the interpretation, analysis, and improvement of their natural language parts. More specifically, we discuss particular use cases of semantic process modeling to identify 25 challenges. For each cha- llenge, we identify prior research and discuss directions for addressing themEl modelado de procesos se ha convertido en una parte esencial de muchas organizaciones para documentar, analizar, y rediseñar sus operaciones de negocios y apoyarlos con información apropiada. Para cumplir este fin, es importante para estos que estén completos dentro de una semántica formal y precisa. Mientras la semántica del comportamiento del modelado de procesos se entiende bien, hay una considerable laguna en la investigación entre los aspectos semánticos de sus rótulos textuales, y las descripciones en lenguaje natural. El objetivo de este artículo es hacer esta laguna en la investigación más transparente. Con este fin, clarificamos el papel del contenido textual en los modelos de proceso, y los retos relacionados con la interpretación, el análisis, y desarrollo de sus partes en lenguaje natural. De forma más específica, debatimos los casos particulares del uso del modelado de procesos semánticos para identificar 25 retos. Para cada reto, identificamos antes de la investigación y debatimos las direcciones para dirigirnos a ellos

Towards a Science of Checklists

Checklists are in use in many work domains, including aviation, manufacturing, quality control, and healthcare. Despite their adoption, the literature shows both breadth and persistence of problems with the organizational usage of checklists. In this paper, we conduct a structured literature survey to analyze checklists from the perspective of informational artifacts. Our contribution is a respective conceptualization of checklists and a rigorous analysis of their problems. As we will argue, these insights help to consider how the capabilities of IT systems can be leveraged to improve checklists and address their problematic aspects. We present our work as a basis for IT-oriented research into a relevant yet under-examined information practice in organizational work routines

Data-Based Process Variant Analysis

Processes in healthcare are complex and data-intensive. Process mining uses data recorded during process execution to obtain an understanding of the actual execution of a process. Due to the complexity of healthcare processes, it is useful to consider and analyse the process execution of certain cohorts, such as old and young patients, separately. While such analysis is facilitated by process variant analysis techniques, existing approaches for process variant analysis only consider a comparison based on the control flow and performance perspectives. Given the large amount of event data attributes available in healthcare settings, we propose the first data-based process variant analysis approach. Our approach allows comparing process variants based on differences in event data attributes by building on statistical tests. We applied our approach on the MIMIC-IV real-world data set on hospitalizations in the US, where we demonstrate that the approach is feasible and can actually provide relevant medical insights

Methodology of Algorithm Engineering

Research on algorithms has drastically increased in recent years. Various sub-disciplines of computer science investigate algorithms according to different objectives and standards. This plurality of the field has led to various methodological advances that have not yet been transferred to neighboring sub-disciplines. The central roadblock for a better knowledge exchange is the lack of a common methodological framework integrating the perspectives of these sub-disciplines. It is the objective of this paper to develop a research framework for algorithm engineering. Our framework builds on three areas discussed in the philosophy of science: ontology, epistemology and methodology. In essence, ontology describes algorithm engineering as being concerned with algorithmic problems, algorithmic tasks, algorithm designs and algorithm implementations. Epistemology describes the body of knowledge of algorithm engineering as a collection of prescriptive and descriptive knowledge, residing in World 3 of Popper's Three Worlds model. Methodology refers to the steps how we can systematically enhance our knowledge of specific algorithms. The framework helps us to identify and discuss various validity concerns relevant to any algorithm engineering contribution. In this way, our framework has important implications for researching algorithms in various areas of computer science

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