Abstract
The Internet of Things (IoT), the network of physical objects augmented with Internet-enabled computing devices to enable those objects sense the real world, has the potential to transform many industries. This includes harnessing real-time intelligence to improve risk-based decision making and supporting adaptive processes from core to edge. For example, modern police investigation processes are often extremely complex, data-driven and knowledge-intensive. In such processes, it is not sufficient to focus on data storage and data analysis; and the knowledge workers (e.g., investigators) will need to collect, understand and relate the big data (scattered across various systems) to process analysis: in order to communicate analysis findings, supporting evidences and to make decisions. In this paper, we present a scalable and extensible IoT-Enabled Process Data Analytics Pipeline (namely iProcess) to enable analysts ingest data from IoT devices, extract knowledge from this data and link them to process (execution) data. We introduce the notion of process Knowledge Lake and present novel techniques to summarize the linked IoT and process data to construct process narratives. This enables us to put the first step towards enabling storytelling with process data.
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Beheshti, A. et al. (2018). iProcess: Enabling IoT Platforms in Data-Driven Knowledge-Intensive Processes. In: Weske, M., Montali, M., Weber, I., vom Brocke, J. (eds) Business Process Management Forum. BPM 2018. Lecture Notes in Business Information Processing, vol 329. Springer, Cham. https://doi.org/10.1007/978-3-319-98651-7_7
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DOI: https://doi.org/10.1007/978-3-319-98651-7_7
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