TY - GEN
T1 - iCOP
T2 - 16th International Conference on Service-Oriented Computing, ICSOC 2018
AU - Schiliro, Francesco
AU - Beheshti, Amin
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 - 2019/1/1
Y1 - 2019/1/1
N2 - Analyzing data-driven and knowledge intensive business processes is a key endeavor for today’s enterprises. Recently, the Internet of Things (IoT) has been widely adopted for the implementation and integration of data-driven business processes within and across enterprises. For example, in law enforcement agencies, various IoT devices such as CCTVs, police cars and drones are augmented with Internet-enabled computing devices to sense the real world. This in turn, has the potential to change the nature of data-driven and knowledge intensive processes, such as criminal investigation, in policing. In this paper, we present a framework and a set of techniques to assist knowledge workers (e.g., a criminal investigator) in knowledge intensive processes (e.g., criminal investigation) to benefit from IoT-enabled processes, collect large amounts of evidences and dig for the facts in an easy way. We focus on a motivating scenario in policing, where a criminal investigator will be augmented by smart devices to collect data and to identify devices around the investigation location and communicate with them to understand and analyze evidences. We present iCOP, IoT-enabled COP assistant system, to enable IoT in policing and to accelerate the investigation process.
AB - Analyzing data-driven and knowledge intensive business processes is a key endeavor for today’s enterprises. Recently, the Internet of Things (IoT) has been widely adopted for the implementation and integration of data-driven business processes within and across enterprises. For example, in law enforcement agencies, various IoT devices such as CCTVs, police cars and drones are augmented with Internet-enabled computing devices to sense the real world. This in turn, has the potential to change the nature of data-driven and knowledge intensive processes, such as criminal investigation, in policing. In this paper, we present a framework and a set of techniques to assist knowledge workers (e.g., a criminal investigator) in knowledge intensive processes (e.g., criminal investigation) to benefit from IoT-enabled processes, collect large amounts of evidences and dig for the facts in an easy way. We focus on a motivating scenario in policing, where a criminal investigator will be augmented by smart devices to collect data and to identify devices around the investigation location and communicate with them to understand and analyze evidences. We present iCOP, IoT-enabled COP assistant system, to enable IoT in policing and to accelerate the investigation process.
KW - Data Analytics
KW - Internet of things
KW - Knowledge Lake
KW - Law enforcement
KW - Policing
KW - Process data science
UR - http://www.scopus.com/inward/record.url?scp=85064853997&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-17642-6_42
DO - 10.1007/978-3-030-17642-6_42
M3 - Conference proceeding contribution
AN - SCOPUS:85064853997
SN - 9783030176419
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 447
EP - 452
BT - Service-Oriented Computing – ICSOC 2018 Workshops
A2 - Liu, Xiao
A2 - Mrissa, Michael
A2 - Zhang, Liang
A2 - Benslimane, Djamal
A2 - Ghose, Aditya
A2 - Wang, Zhongjie
A2 - Bucchiarone, Antonio
A2 - Zhang, Wei
A2 - Zou, Ying
A2 - Yu, Qi
PB - Springer-VDI-Verlag GmbH & Co. KG
CY - Switzerland
Y2 - 12 November 2018 through 15 November 2018
ER -