Creating logs

Transforming your raw data into an event log object is one of the most challenging tasks in process analysis. On this page, we cover all the possible situations and challenges that you can encounter.

We start with some important terminology:

  • Case: The subject of your process, e.g. a customer, an order, a patient.
  • Activity: A step in your process, e.g. receive order, sent payment, perform MRI SCAN, etc.
  • Activity instance: The execution of a specific step for a specific case.
  • Event: A registration connected to an activity instance, characterized by a single timestamp. E.g. the start of Perform MRI SCAN for Patient X.
  • Resource: A person or machine that is related to the execution of (part of) an activity instance. E.g. the radiologist in charge of our MRI SCAN.
  • Lifecycle status: An indication of the status of an activity instance connect to an event. Typical values are start, complete. Other possible values are schedule, suspend, resume, etc.
  • Trace: A sequence of activities. The activity instances that belong to a case will result to a specific trace when ordered by the time each instance occurred.

Logs: eventlog vs activitylog

bupaR supports two different kinds of log formats, both of which are an extension on R data.frame:

  • eventlog: Event logs are created from data.frame in which each row represents a single event. This means that it has a single timestamp.
  • activitylog: Activity logs are created from data.frame in which each row represents a single activity instances. This means it can has multiple timestamps, stored in different columns.

The data model below shows the difference between these two levels of observations, i.e. activity instances vs events.

The example below shows an excerpt of an event log containing 6 events. It can be seen that each event is linked to a single timestamp. As there can be more events within a single activity instance, each event also needs to be linked to a lifecycle status (here the registration_type). Furthermore, an activity instance identifier (handling_id) is needed to indicated which events belong to the same activity instances.

handling patient employee handling_id registration_type time
Registration 207 r1 207 start 2017-07-20 22:31:31
Triage and Assessment 207 r2 707 start 2017-07-21 01:29:30
Registration 207 r1 207 complete 2017-07-21 01:29:30
Triage and Assessment 207 r2 707 complete 2017-07-21 16:45:28
Blood test 207 r3 1101 start 2017-07-25 08:14:46
Blood test 207 r3 1101 complete 2017-07-25 15:23:36
Transactional lifecycle?
An event is an atomic registration related to an activity instance. It thus contains one (and only one) timestamp. Additionally, the event should include a reference to a lifecycle transition. More specifically, multiple events can describe different lifecycle transitions of a single activity instance. For example, one event might record when a surgery is scheduled, another when it is started, yet another when it is completed, etc.
The standard transactional lifecycle.

The table below show the same data as above, but now using the activitylog format. It can be seen that there are now just 3 rows instead of 6, but each row as 2 timestamps, representing 2 events. The lifecycle status represented by those timestamps is now the column names of those variables.

handling patient employee handling_id complete start
Registration 207 r1 207 2017-07-21 01:29:30 2017-07-20 22:31:31
Triage and Assessment 207 r2 707 2017-07-21 16:45:28 2017-07-21 01:29:30
Blood test 207 r3 1101 2017-07-25 15:23:36 2017-07-25 08:14:46

As these examples show, both formats can often be used for representing the same process data. However, there are some important differences between them:

  • the eventlog format has much more flexibility in terms of lifecycle. There is no limit to the number of events that can occur in a single activity instance. If your data contains lifecycle statuses such as suspend, resume or reassign, they can be recorded multiple times within a single activity instance. In the activitylog format, as each lifecycle gets is own column, it isn’t possible to have two events of the same lifecycle status in a single activity instance.
  • the level of observation in an eventlog is an event. As a result, attribute values can be stored at the event level. In an activitylog, the level of observation is an activity instance. This means that all additional attributes that you have about your process should be at this higher level. For example, an activity instance can only be connected to a single resource in the activitylog format, whereas in an eventlog different events within the same activity instance can have different resources, of different values for any other attribute.
  • because of the limited flexibility, an activitylog is easier to make, and typically closer to the format that your data is already in (see further below on how to construct log objects). As a result of this, there are many situations in which the analysis of an activitylog will be much faster compared to eventlog, where a lot of additional complexity needs to be taken into account.

The right log for the job

Functionalities in bupaR core packages support both formats. 1 As such, the goal of your analysis does not impact the decision. Only the complexity of your data is important to make this decision. The precise format your raw data is in will further define the preparatory steps that are needed. We can distinguish between 3 typical scenarios. The flowchart below helps you on your way.

An activitylog is the best option when each row in your data is an activity instance, or when events belonging to the same activity instance have equal attribute values (e.g. all events are executed by the same resource). When these two criteria do not hold, you can create an eventlog object.

Scenario 1

If each row in your data.frame is already an activity instance, the activitylog format is the best way to go. Consider the data sample below.

patient handling activity_started activity_ended
155 Check-out 2017-06-05 15:58:53 2017-06-05 17:22:16
155 Discuss Results 2017-06-05 14:03:19 2017-06-05 15:58:53
155 Registration 2017-06-03 10:05:28 2017-06-03 14:19:00
155 Triage and Assessment 2017-06-04 06:27:00 2017-06-04 14:23:26
155 X-Ray 2017-06-05 00:12:24 2017-06-05 04:39:38

As each row contains multiple timestamps, i.e. activity_started and activity_ended, it is clear that each row represents an activity instance. Turning this dataset in an activitylog requires the following steps:

  1. Timestamp variables should be named in correspondence with the standard Transactional lifecycle.
  2. Timestamp variables should be of type Date or POSIXct.
  3. Use the activitylog constructor function.
data %>%
    # rename timestamp variables appropriately
    dplyr::rename(start = activity_started, 
           complete = activity_ended) %>%
    # convert timestamps to 
    convert_timestamps(columns = c("start", "complete"), format = ymd_hms) %>%
    activitylog(case_id = "patient",
                activity_id = "handling",
                timestamps = c("start", "complete"))
## # Log of 10 events consisting of:
## 1 trace 
## 1 case 
## 5 instances of 5 activities 
## 0 resources 
## Events occurred from 2017-06-03 10:05:28 until 2017-06-05 17:22:16 
##  
## # Variables were mapped as follows:
## Case identifier:     patient 
## Activity identifier:     handling 
## Resource identifier:     employee 
## Timestamps:      start, complete 
## 
## # A tibble: 5 × 5
##   patient handling              start               complete            .order
##   <chr>   <fct>                 <dttm>              <dttm>               <int>
## 1 155     Check-out             2017-06-05 15:58:53 2017-06-05 17:22:16      1
## 2 155     Discuss Results       2017-06-05 14:03:19 2017-06-05 15:58:53      2
## 3 155     Registration          2017-06-03 10:05:28 2017-06-03 14:19:00      3
## 4 155     Triage and Assessment 2017-06-04 06:27:00 2017-06-04 14:23:26      4
## 5 155     X-Ray                 2017-06-05 00:12:24 2017-06-05 04:39:38      5

Note that in case a resource identifier is available, this information can be added in the activitylog call.

Scenario 2

If each row in your data.frame is an event, but all events that belong to the same activity instance share the same attribute values, the activitylog format is again the best way to go. Consider the data sample below.

patient handling employee handling_id registration_type time
173 Registration r1 173 started 2017-06-18 20:54:33
173 Registration r1 173 completed 2017-06-18 23:45:36
173 Triage and Assessment r2 673 started 2017-06-19 15:40:32
173 Triage and Assessment r2 673 completed 2017-06-20 04:53:58
173 Blood test r3 1083 started 2017-06-21 08:02:20
173 Blood test r3 1083 completed 2017-06-21 12:46:08
173 MRI SCAN r4 1320 started 2017-06-21 18:03:06
173 MRI SCAN r4 1320 completed 2017-06-21 23:36:44
173 Discuss Results r6 1907 started 2017-06-22 08:40:55
173 Discuss Results r6 1907 completed 2017-06-22 12:20:12
173 Check-out r7 2402 started 2017-06-25 17:05:13
173 Check-out r7 2402 completed 2017-06-25 19:29:14

The resource identifier (employee) has been added as an additional attribute. Note that though each row is an event, they can be grouped into activity instances using the handling_id column, which we will call the activity instance id. Using the latter, we can see that the resource attribute is the same within each activity instance, which allows us to create an activitylog. The steps to do so are the following.

  1. Lifecycle variable should be named in correspondence with the standard Transactional lifecycle.
  2. Timestamp variable should be of type Date or POSIXct.
  3. Use the eventlog constructor function.
  4. Convert to activitylog using to_activitylog for reduced memory usage and improved performance.
data %>%
    # recode lifecycle variable appropriately
    dplyr::mutate(registration_type = forcats::fct_recode(registration_type, 
                                                          "start" = "started",
                                                          "complete" = "completed")) %>%
    convert_timestamps(columns = "time", format = ymd_hms) %>%
    eventlog(case_id = "patient",
                activity_id = "handling",
                activity_instance_id = "handling_id",
                lifecycle_id = "registration_type",
                timestamp = "time",
                resource_id = "employee") %>%
    to_activitylog() -> tmp_act

Note that the resource identifier is optional, and can be left out of the eventlog call if such an attribute does not exist in your data. If the activity instance id does not exist, some heuristics are available to generate it: Missing activity instance id.

Scenario 3

If each row is an event, and events of the same activity instance have differing attribute values, the flexibility of eventlog objects is required. Consider the data sample below.

patient handling employee handling_id registration_type time
101 Registration r6 101 started 2017-04-16 06:38:58
101 Registration r6 101 completed 2017-04-16 09:12:01
101 Triage and Assessment r1 601 started 2017-04-16 22:46:00
101 Triage and Assessment r2 601 completed 2017-04-17 10:17:27
101 X-Ray r7 1529 started 2017-04-18 01:05:58
101 X-Ray r2 1529 completed 2017-04-18 05:22:16
101 Discuss Results r1 1835 started 2017-04-22 08:15:23
101 Discuss Results r5 1835 completed 2017-04-22 11:14:53
101 Check-out r5 2330 started 2017-04-23 00:16:15
101 Check-out r7 2330 completed 2017-04-23 02:55:23

In this example, different resources (employees) sometimes perform the start and complete event of the same activity instance. Therefore, we resort to the eventlog format which has no problems storing this. The steps to take are the following:

  1. Lifecycle variable should be named in correspondence with the standard Transactional lifecycle.
  2. Timestamp variable should be of type Date or POSIXct.
  3. Use the eventlog constructor function.
data %>%
    # recode lifecycle variable appropriately
    dplyr::mutate(registration_type = forcats::fct_recode(registration_type, 
                                                          "start" = "started",
                                                          "complete" = "completed")) %>%
    convert_timestamps(columns = "time", format = ymd_hms) %>%
    eventlog(case_id = "patient",
                activity_id = "handling",
                activity_instance_id = "handling_id",
                lifecycle_id = "registration_type",
                timestamp = "time",
                resource_id = "employee") 
## Warning in validate_eventlog(eventlog): The following activity instances are
## connected to more than one resource: 1529,1835,2330,601
## # Log of 10 events consisting of:
## 1 trace 
## 1 case 
## 5 instances of 5 activities 
## 5 resources 
## Events occurred from 2017-04-16 06:38:58 until 2017-04-23 02:55:23 
##  
## # Variables were mapped as follows:
## Case identifier:     patient 
## Activity identifier:     handling 
## Resource identifier:     employee 
## Activity instance identifier:    handling_id 
## Timestamp:           time 
## Lifecycle transition:        registration_type 
## 
## # A tibble: 10 × 7
##    patient handling   employee handling_id registration_type time               
##    <chr>   <fct>      <fct>    <chr>       <fct>             <dttm>             
##  1 101     Registrat… r6       101         start             2017-04-16 06:38:58
##  2 101     Registrat… r6       101         complete          2017-04-16 09:12:01
##  3 101     Triage an… r1       601         start             2017-04-16 22:46:00
##  4 101     Triage an… r2       601         complete          2017-04-17 10:17:27
##  5 101     X-Ray      r7       1529        start             2017-04-18 01:05:58
##  6 101     X-Ray      r2       1529        complete          2017-04-18 05:22:16
##  7 101     Discuss R… r1       1835        start             2017-04-22 08:15:23
##  8 101     Discuss R… r5       1835        complete          2017-04-22 11:14:53
##  9 101     Check-out  r5       2330        start             2017-04-23 00:16:15
## 10 101     Check-out  r7       2330        complete          2017-04-23 02:55:23
## # ℹ 1 more variable: .order <int>

Note that we need an eventlog irrespective of which attribute values are differing, i.e. it can be resources, but also any additional variables you have in your data set. For the special case of resource values, it might be that a different resource executing events in the same activity instance is a data quality issue. If so, some functions can help you to identify this issue: Inconsistent Resources.

Again, if the activity instance id does not exist, some heuristics are available to generate it: Missing activity instance id.

Typical problems

Missing activity instance id

In order to be able to correlate events which belong to the same activity instance, an activity instance identifier is required. For example, in the data shown below, it is possible that a patient has gone through different surgeries, each with their own start- and complete event. The activity instance identifier will then allow to distinguish which events belong together and which do not. It is important to note that this instance identifier should be unique, also among different cases and activities.

patient activity timestamp status activity_instance
John Doe check-in 2017-05-10 08:33:26 complete 1
John Doe surgery 2017-05-10 08:53:16 start 2
John Doe surgery 2017-05-10 09:25:19 complete 2
John Doe treatment 2017-05-10 10:01:25 start 3
John Doe treatment 2017-05-10 10:35:18 complete 3
John Doe surgery 2017-05-10 10:41:35 start 4
John Doe surgery 2017-05-10 11:05:56 complete 4
John Doe check-out 2017-05-11 14:52:36 complete 5

If the activity instance identifier is not available you can use the assign_instance_id() function, which uses an heuristic to create the missing identifier. Alternatively, you can try to create the identifier on your own using dplyr::mutate() and other manipulation functions.

Large Datasets and Validation

By default, bupaR validates certain properties of the activity instances that is supplied when creating an event log:

  • a single activity instance identifier must not be connected to multiple cases,
  • a single activity instance identifier must not be connected to multiple activity labels,

However, these checks are not efficient and may lead to considerable performance issues for large data frames. It is possible to deactivate the validation in case you already know that your data fulfills all the requirements, using the argument validate = FALSE when creating the eventlog. Note that when the activity instance id was created with the assign_instance_id() function, you can assume the above properties hold.

Inconsistent Resources

Each event can contain the notion of a resource. It can be so that different events belonging to the same activity instance are executed by different resources, as in the eventlog below.

patient handling employee handling_id registration_type time .order
64 Registration r6 64 start 2017-03-09 14:27:30 1
64 Registration r3 64 complete 2017-03-09 15:34:48 2
64 Triage and Assessment r1 564 start 2017-03-10 05:23:06 3
64 Triage and Assessment r1 564 complete 2017-03-10 18:10:32 4
64 Blood test r6 1030 start 2017-03-11 19:31:13 5
64 Blood test r7 1030 complete 2017-03-12 02:44:53 6
64 MRI SCAN r2 1267 start 2017-03-12 10:04:03 7
64 MRI SCAN r4 1267 complete 2017-03-12 14:40:37 8
64 Discuss Results r4 1798 start 2017-03-12 22:27:56 9
64 Discuss Results r2 1798 complete 2017-03-13 00:45:14 10
64 Check-out r3 2293 start 2017-03-14 18:45:54 11
64 Check-out r7 2293 complete 2017-03-14 20:03:19 12

If you have a large dataset, and want to have an overview of the activity instances that have more than one resource connected to them, you can use the detect_resource_inconsistences() function.

log %>%
    detect_resource_inconsistencies()
## # A tibble: 5 × 5
##   patient handling        handling_id complete start
##   <chr>   <fct>           <chr>       <chr>    <chr>
## 1 64      Blood test      1030        r7       r6   
## 2 64      Check-out       2293        r7       r3   
## 3 64      Discuss Results 1798        r2       r4   
## 4 64      MRI SCAN        1267        r4       r2   
## 5 64      Registration    64          r3       r6

If you want to remove these inconsistencies, a quick fix is to merge the resource labels together with fix_resource_inconsistencies(). Note that this is not needed for eventlog, but it is for activitylog. While the creation of the eventlog will emit a warning when resource inconsistencies exist, this should mostly be seen as a data quality warning. That said, there might be analysis related to the counting of resources where such inconsistencies might lead to odd results.

log %>%
    fix_resource_inconsistencies()
## *** OUTPUT ***
## A total of 5 activity executions in the event log are classified as inconsistencies.
## They are spread over the following cases and activities:
## # A tibble: 5 × 5
##   patient handling        handling_id complete start
##   <chr>   <fct>           <chr>       <chr>    <chr>
## 1 64      Blood test      1030        r7       r6   
## 2 64      Check-out       2293        r7       r3   
## 3 64      Discuss Results 1798        r2       r4   
## 4 64      MRI SCAN        1267        r4       r2   
## 5 64      Registration    64          r3       r6
## Inconsistencies solved succesfully.
## # Log of 12 events consisting of:
## 1 trace 
## 1 case 
## 6 instances of 6 activities 
## 6 resources 
## Events occurred from 2017-03-09 14:27:30 until 2017-03-14 20:03:19 
##  
## # Variables were mapped as follows:
## Case identifier:     patient 
## Activity identifier:     handling 
## Resource identifier:     employee 
## Activity instance identifier:    handling_id 
## Timestamp:           time 
## Lifecycle transition:        registration_type 
## 
## # A tibble: 12 × 7
##    patient handling   employee handling_id registration_type time               
##    <chr>   <fct>      <chr>    <chr>       <fct>             <dttm>             
##  1 64      Registrat… r3 - r6  64          start             2017-03-09 14:27:30
##  2 64      Registrat… r3 - r6  64          complete          2017-03-09 15:34:48
##  3 64      Triage an… r1       564         start             2017-03-10 05:23:06
##  4 64      Triage an… r1       564         complete          2017-03-10 18:10:32
##  5 64      Blood test r7 - r6  1030        start             2017-03-11 19:31:13
##  6 64      Blood test r7 - r6  1030        complete          2017-03-12 02:44:53
##  7 64      MRI SCAN   r4 - r2  1267        start             2017-03-12 10:04:03
##  8 64      MRI SCAN   r4 - r2  1267        complete          2017-03-12 14:40:37
##  9 64      Discuss R… r2 - r4  1798        start             2017-03-12 22:27:56
## 10 64      Discuss R… r2 - r4  1798        complete          2017-03-13 00:45:14
## 11 64      Check-out  r7 - r3  2293        start             2017-03-14 18:45:54
## 12 64      Check-out  r7 - r3  2293        complete          2017-03-14 20:03:19
## # ℹ 1 more variable: .order <int>

Read more:

Create Logs Adjust Logs Public Logs Xes Files Inspect Logs Data Quality

  1. Currently both eventlog and activitylog are supported by the packages bupaR, edeaR and processmapR. The daqapo package only supports activitylog, while all other packages only support eventlog. While the goal is to extend support for both to all packages, you can in the meanwhile always convert the format of your log using the functions to_eventlog() and to_activitylog().↩︎

Copyright © 2025 bupaR - Hasselt University