Advancements and Challenges in Object-Centric Process Mining: A Systematic Literature Review

Recent years have seen the emergence of object-centric process mining techniques. Born as a response to the limitations of traditional process mining in analyzing event data from prevalent information systems like CRM and ERP, these techniques aim to tackle the deficiency, convergence, and divergence issues seen in traditional event logs. Despite the promise, the adoption in real-world process mining analyses remains limited. This paper embarks on a comprehensive literature review of object-centric process mining, providing insights into the current status of the discipline and its historical trajectory

OC-PM: Analyzing Object-Centric Event Logs and Process Models

Object-centric process mining is a novel branch of process mining that aims to analyze event data from mainstream information systems (such as SAP) more naturally, without being forced to form mutually exclusive groups of events with the specification of a case notion. The development of object-centric process mining is related to exploiting object-centric event logs, which includes exploring and filtering the behavior contained in the logs and constructing process models which can encode the behavior of different classes of objects and their interactions (which can be discovered from object-centric event logs). This paper aims to provide a broad look at the exploration and processing of object-centric event logs to discover information related to the lifecycle of the different objects composing the event log. Also, comprehensive tool support (OC-PM) implementing the proposed techniques is described in the paper

Towards the Discovery of Object-Aware Processes

There has been a huge body of research in order to reduce manual efforts in creating executable process models through the automated discovery of process models from the event logs created by information systems. Regarding activity-centric processes, such event logs comprise case ids and events related to the execution of process activities. However, there exist alternative process management paradigms, such as object-aware processes, for which existing algorithms fail to discover a sound model. These algorithms do not treat data as first-class citizens, but solely rely on the information from event logs. In consequence, existing discovery algorithms are insufficient for discovering object-aware processes. To address this issue, discovery algorithms need to consider additional data sources (e.g., existing forms). This paper discusses the need for dedicated discovery techniques in object-aware processes

An investigation of discovering business processes from operational databases

Process discovery techniques aim to discover process models from event-logs. An event-log records process activities carried out on related data items and the timestamp where the event occurred. While the event-log is explicitly recorded in the process-awareness information systems such as modern ERP and CRM systems, other in-house information systems may not record event-log, but an operational database. This raises the need to develop process discovery solutions from operational databases. Meanwhile, process models can be represented from various perspectives, e.g. functional, behavioural, organisational, informational and business context perspectives. However, none of the existing techniques supports to discover process models from different perspectives using operational databases. This paper aims to deal with these gaps by proposing process expressive artefacts based on process perspectives adopted in the literature, as well as discussing how these artefacts can be extracted from data components of a typical operational database

Extracting event data from databases to unleash process mining

Increasingly organizations are using process mining to understand the way that operational processes are executed. Process mining can be used to systematically drive innovation in a digitalized world. Next to the automated discovery of the real underlying process, there are process-mining techniques to analyze bottlenecks, to uncover hidden inefficiencies, to check compliance, to explain deviations, to predict performance, and to guide users towards "better" processes. Dozens (if not hundreds) of process-mining techniques are available and their value has been proven in many case studies. However, process mining stands or falls with the availability of event logs. Existing techniques assume that events are clearly defined and refer to precisely one case (i.e. process instance) and one activity (i.e., step in the process). Although there are systems that directly generate such event logs (e.g., BPM/WFM systems), most information systems do not record events explicitly. Cases and activities only exist implicitly. However, when creating or using process models "raw data" need to be linked to cases and activities. This paper uses a novel perspective to conceptualize a database view on event data. Starting from a class model and corresponding object models it is shown that events correspond to the creation, deletion, or modification of objects and relations. The key idea is that events leave footprints by changing the underlying database. Based on this an approach is described that scopes, binds, and classifies data to create "flat" event logs that can be analyzed using traditional process-mining techniques