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...
2018
Lecture Notes in Business Information Processing
Berlin
Springer Verlag
9783319986500
Beheshti, Amin; Schiliro, Francesco; Ghodratnama, Samira; Amouzgar, Farhad; Benatallah, Boualem; Yang, Jian; Sheng, Quan Z.; Casati, Fabio; Motahari-N...espandi
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].
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/226534
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 21
  • ???jsp.display-item.citation.isi??? ND
  • OpenAlex ND
social impact