TY - GEN
T1 - Iprocess
T2 - 16th International Conference on Business Process Management Forum, BPM Forum 2018
AU - Beheshti, Amin
AU - Schiliro, Francesco
AU - Ghodratnama, Samira
AU - Amouzgar, Farhad
AU - Benatallah, Boualem
AU - Yang, Jian
AU - Sheng, Quan Z.
AU - Casati, Fabio
AU - Motahari-Nezhad, Hamid Reza
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
KW - Data-driven business processes
KW - Knowledge-intensive business processes
KW - Process Data Analytics
KW - Process data science
UR - http://www.scopus.com/inward/record.url?scp=85052638665&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85052638665&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-98651-7_7
DO - 10.1007/978-3-319-98651-7_7
M3 - Conference contribution
AN - SCOPUS:85052638665
SN - 9783319986500
T3 - Lecture Notes in Business Information Processing
SP - 108
EP - 126
BT - Business Process Management Forum - BPM Forum 2018, Proceedings
A2 - Montali, Marco
A2 - Weske, Mathias
A2 - vom Brocke, Jan
A2 - Weber, Ingo
PB - Springer Verlag
Y2 - 9 September 2018 through 14 September 2018
ER -