Rethinking the Input for Process Mining: Insights from the XES Survey and Workshop

Abstract

Although the popularity and adoption of process mining techniques grew rapidly in recent years, a large portion of effort invested in process mining initiatives is still consumed by event data extraction and transformation rather than process analysis. The IEEE Task Force on Process Mining conducted a study focused on the challenges faced during event data preparation (from source data to event log). This paper presents findings from the online survey with 289 participants spanning the roles of practitioners, researchers, software vendors, and end-users. These findings were presented at the XES 2.0 workshop co-located with the 3rd International Conference on Process Mining. The workshop also hosted presentations from various stakeholder groups and a discussion panel on the future of XES and the input needed for process mining. This paper summarises the main findings of both the survey and the workshop. These outcomes help us to accelerate and improve the standardisation process, hopefully leading to a new standard widely adopted by both academia and industry.

Foundations of Process Event Data

“…It is pointed out that this is not a comprehensive list of all possible event log data sources. In an online survey with 289 participants spanning the roles of practitioners, researchers, software vendors, and end-users, SAP ECC (R/3), SAP S/4 HANA, and Salesforce are selected as the top three most analyzed source systems for process mining analysis [57].…”

“…The XES survey also uncovered the top tools that are currently being used by the process mining community for the preparing of event logs [57]. There is also ongoing work by the IEEE Task force on reinventing the IEEE XES standard to address several identified data related challenges in the XES survey [57], in particular, to capture the semantics of event data and to support complex data structures.…”

“…But it appears that the more the quote is used, the more relevant it becomes. In process mining, while the problem has been acknowledged in both scientific literature and in practice [57], there is still a need for further research into the development of a comprehensive framework to address the problem of bad quality data leading to incorrect analysis results [58]. We also need to have a better understanding of the root-causes of such data quality issues [5,24].…”

“…As a process mining professional, it is imperative that we measure the quality of an event log respective to the type of process mining analysis being considered [58]. The data pre-processing task is recognized to be one of the most time-consuming aspects of a process mining study with many spending 60-80% of their efforts while some spending up to 90% of their total efforts on this step [57].…”

Data Preprocessing Method and API for Mining Processes from Cloud-Based Application Event Logs

“…Preprocessing is required to reduce and simplify event logs [11][12]. Preprocessing such logs before applying process mining techniques usually takes up between 40% and 80% of the total effort for most people [18]. Preprocessing is so important that the IEEE Task Force on Process Mining is currently surveying the community regarding challenges and solutions for improving event data preparation for process mining (see https://bit.ly/TFPM-survey2001).…”