Clemens Schreiber , Amine Abbad-Andaloussi , Barbara Weber
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引用次数: 0
Abstract
Process model comprehension is essential for a variety of technical and managerial tasks. To facilitate comprehension, process models are often divided into subprocesses when they reach a certain size. However, depending on the task type this can either support or impede comprehension. To investigate this hypothesis, we conduct a comprehensive eye-tracking study, where we test two different types of comprehension tasks. These are local tasks focusing on a single subprocess, thereby benefiting from abstraction (i.e., irrelevant information is hidden), and global tasks comprising multiple subprocesses, thereby also benefiting from abstraction but impeded by fragmentation (i.e., relevant information is distributed across multiple fragments). Our subsequent analysis at task (coarse-grained) and phase (fine-grained) levels confirms the opposing effects of abstraction and fragmentation. For global tasks, we observe lower task comprehension, higher cognitive load, as well as more complex search and inference behaviors, when compared to local ones. An additional qualitative analysis of search and inference phases, based on process maps and time series, provides additional insights into the evolution of information processing and confirms the differences between the two task types. The fine-grained analysis at the phase level is based on a novel research method, allowing to clearly separate information search from information inference. We provide an extensive validation of this research method. The outcome of this work provides a more thorough understanding of the effects of fragmentation, in the context of modularized process models, at a coarse-grained level as well as at a fine-grained level, allowing for the development of task- and user-centric support, and opening up future research opportunities to further investigate information processing during process comprehension.
期刊介绍:
Information systems are the software and hardware systems that support data-intensive applications. The journal Information Systems publishes articles concerning the design and implementation of languages, data models, process models, algorithms, software and hardware for information systems.
Subject areas include data management issues as presented in the principal international database conferences (e.g., ACM SIGMOD/PODS, VLDB, ICDE and ICDT/EDBT) as well as data-related issues from the fields of data mining/machine learning, information retrieval coordinated with structured data, internet and cloud data management, business process management, web semantics, visual and audio information systems, scientific computing, and data science. Implementation papers having to do with massively parallel data management, fault tolerance in practice, and special purpose hardware for data-intensive systems are also welcome. Manuscripts from application domains, such as urban informatics, social and natural science, and Internet of Things, are also welcome. All papers should highlight innovative solutions to data management problems such as new data models, performance enhancements, and show how those innovations contribute to the goals of the application.