Cancer Research UK Drug Discovery Process Mining
This work addresses inefficiencies in a specific drug discovery organization, but it is incremental as it applies existing process mining tools to new data without introducing novel methodologies.
The study tackled the problem of underutilized event logs in Cancer Research UK's drug discovery process by applying process mining to extract knowledge and model the operational business process, aiming to improve efficiency in resource review and work allocations to accelerate new treatments for patients.
Background. The Drug Discovery Unit (DDU) of Cancer Research UK (CRUK) is using the software Dotmatics for storage and analysis of scientific data during drug discovery process. Whilst the data include event logs, time stamps, activities, and user information are mostly sitting in the database without fully utilising their potential value. Aims. This dissertation aims at extracting knowledge from event logs data which recorded during drug discovery process, to capture the operational business process of the DDU of Cancer Research UK (CRUK) as it was being executed. It provides the evaluations and methodologies of drawing the process mining panoramic models for the drug discovery process. Thus by enabling the DDU to maximise its efficiency in reviewing its resources and works allocations, patients will benefit from more new treatments faster. Conclusion. Management of organisations can be benefit from the process mining methodologies. Disco is excellent for non-experts on management purposes. ProM is great for expert on research purposes. However, the process mining is not once and for all but is a regular operation management process. Indeed, event logs needs to be understand more on the target organisational behaviours and organisational business process. The researchers have to be aware that event logs data are the most important and priority elements in process mining.