  The Internet of Things (IoT) 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; as the knowledge workers (e.g., police
investigators) will need to collect, understand and relate the big data
(scattered across various systems) to process analysis. In this thesis, we
analyze the state of the art in knowledge-intensive and data-driven processes.
We present a scalable and extensible IoT-enabled process data analytics
pipeline to enable analysts ingest data from IoT devices, extract knowledge
from this data and link them to process execution data. 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, to communicate with them to understand and analyze
evidence. We design and implement a system (namely iCOP, IoT-enabled COP) to
assist investigators collect large amounts of evidence and dig for the facts in
an easy way.
