Converting Raw Data to Event Log - converters

Question

I do research in the field of Health-PM and facing an unstructured big data which needs a preprocessing phase for converting to suitable event log.
I've just googled and understood no ProM plug-in, stand-alone code, or script has developed specially for this task. Except Celonis, which has claimed developed an event log convertor. I'm also writing an event log generator code for my specific case study.
I just want to know, is there any business solution, case study or article on this topic which investigated this issue?
Thanks.
Soureh

Answer 1

What do you exactly mean with unstructured? Is this a bad-structured table like the example you provided, or is it data that is not structured at all (e.g. a hard disk with files)?
In the first situation, Celonis indeed provide an option to extract events based on tables using Vertica SQL. In their free SNAP environment you can learn how to do that.
In the latter, I quess that at least semi-structured data is needed to extract events on large scale, otherwise your script has no clue where to look for.

Answer 2

Good question! Many process mining papers mention that most of the existing information systems are PAIS (process-aware information system) hence, qualified to perform process mining on them. This is true, BUT, it does not mean you can get the data out-of-the-box!
What's the solution? You may transform the existing data (typically from a relational database of your business solution, e.g., an ERP or HIS system) into an event log that process mining can understand.
It works like this: you look into the table containing, e.g., patient registration data. You need the patient ID of this table and the timestamp of registration for each ID. You create an empty table for your event log, typically called "Activity_Table". You consider giving a name to each activity depending on the business context. In our example "Patient Registration" would be a sound name. You insert all the patient IDs with their respective timestamp into the Activity_Table followed by the same activity name for all rows, i.e., "Patient Registration". The result looks like this:
|Patient-ID | Activity | timestamp |
|:----------|:--------------------:| -------------------:|
| 111 |"Patient Registration"| 2021.06.01 14:33:49 |
| 112 |"Patient Registration"| 2021.06.18 10:03:21 |
| 113 |"Patient Registration"| 2021.07.01 01:20:00 |
| ... | | |
Congrats! you have an event log with one activity. The rest is just the same. You create the same table for every important action that has a timestamp in your database, e.g., "Diagnose finished", "lab test requested", "treatment A finished".
|Patient-ID | Activity | timestamp |
|:----------|:-----------------:| -------------------:|
| 111 |"Diagnose finished"| 2021.06.21 18:03:19 |
| 112 |"Diagnose finished"| 2021.07.02 01:22:00 |
| 113 |"Diagnose finished"| 2021.07.01 01:20:00 |
| ... | | |
Then you UNION all these mini tables and sort it based on Patient-ID and then by timestamp:
|Patient-ID | Activity | timestamp |
|:----------|:--------------------:| -------------------:|
| 111 |"Patient Registration"| 2021.06.01 14:33:49 |
| 111 |"Diagnose finished" | 2021.06.21 18:03:19 |
| 112 |"Patient Registration"| 2021.06.18 10:03:21 |
| 112 |"Diagnose finished" | 2021.07.02 01:22:00 |
| 113 |"Patient Registration"| 2021.07.01 01:20:00 |
| 113 |"Diagnose finished" | 2021.07.01 01:20:00 |
| ... | | |
If you notice, the last two rows have the same timestamp. This is very common when working with real data. To avoid this, we need an extra column called "sorting" which helps the process mining algorithm to understand the "normal" order of activities with the same timestamp according to the nature of the underlying business. In this case, we can easily know that registration happens before diagnosis hence, we assign a low value (e.g., 1) to all "Patient Registration" activities. The table might look like this:
|Patient-ID | Activity | timestamp |Order |
|:----------|:--------------------:|:-------------------:| ----:|
| 111 |"Patient Registration"| 2021.06.01 14:33:49 | 1 |
| 111 |"Diagnose finished" | 2021.06.21 18:03:19 | 2 |
| 112 |"Patient Registration"| 2021.06.18 10:03:21 | 1 |
| 112 |"Diagnose finished" | 2021.07.02 01:22:00 | 2 |
| 113 |"Patient Registration"| 2021.07.01 01:20:00 | 1 |
| 113 |"Diagnose finished" | 2021.07.01 01:20:00 | 2 |
| ... | | | |
Now, you have an event log that process mining algorithms undertand!
Side note:
there has been many attempts to automate event log extraction process. The works of "Eduardo González López de Murillas" are really interesting if you want to follow this topic. I could also recommend this open-access paper by Eduardo et al. 2018:
"Connecting databases with process mining: a meta model and toolset" (https://link.springer.com/article/10.1007/s10270-018-0664-7)