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 t...
Iprocess: Enabling iot platforms in data-driven knowledge-intensive processes / Beheshti, Amin; Schiliro, Francesco; Ghodratnama, Samira; Amouzgar, Farhad; Benatallah, Boualem; Yang, Jian; Sheng, Quan Z.; Casati, Fabio; Motahari-Nezhad, Hamid Reza. - 329:(2018), pp. 108-126. ( 16th International Conference on Business Process Management Forum, BPM Forum 2018 aus 2018) [10.1007/978-3-319-98651-7_7].
Iprocess: Enabling iot platforms in data-driven knowledge-intensive processes
Benatallah, Boualem;Casati, Fabio;
2018-01-01
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 t...I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione



