Process mining techniques generate valuable insights in business processes using automatically generated process execution data. However, despite the extensive opportunities that process mining techniques provide, the garbage in - garbage out principle still applies. Data quality issues are widespread in real-life data and can generate misleading results when used for analysis purposes. Currently, there is no systematic way to perform data quality assessment on process-oriented data. To fill this gap, we introduce DaQAPO - Data Quality Assessment for Process-Oriented data. It provides a set of assessment functions to identify a wide array of quality issues.
We identify two stages in the data quality assessment process:
If the user desires to remove anomalies detected by quality tests, he has the ability to do so.
Before we can perform the first stage - reading data - we must have access to the appropriate data sources and have knowledge of the expected data structure. Our package supports two input data formats:
Two example datasets are included in daqapo. These are
hospital and hospital_events. Below, you can
find their respective structures.
str(hospital)
#> tibble [53 x 7] (S3: tbl_df/tbl/data.frame)
#> $ patient_visit_nr: num [1:53] 510 512 510 512 512 510 517 518 518 518 ...
#> $ activity : chr [1:53] "registration" "Registration" "Triage" "Triage" ...
#> $ originator : chr [1:53] "Clerk 9" "Clerk 12" "Nurse 27" "Nurse 27" ...
#> $ start_ts : chr [1:53] "20/11/2017 10:18:17" "20/11/2017 10:33:14" "20/11/2017 10:34:08" "20/11/2017 10:44:12" ...
#> $ complete_ts : chr [1:53] "20/11/2017 10:20:06" "20/11/2017 10:37:00" "20/11/2017 10:41:48" "20/11/2017 10:50:17" ...
#> $ triagecode : num [1:53] 3 3 3 3 3 NA 3 4 4 4 ...
#> $ specialization : chr [1:53] "TRAU" "URG" "TRAU" "URG" ...str(hospital_events)
#> tibble [106 x 8] (S3: tbl_df/tbl/data.frame)
#> $ patient_visit_nr : num [1:106] 510 510 510 510 510 510 512 512 512 512 ...
#> $ activity : chr [1:106] "registration" "registration" "Triage" "Triage" ...
#> $ originator : chr [1:106] "Clerk 9" "Clerk 9" "Nurse 27" "Nurse 27" ...
#> $ event_lifecycle_state: chr [1:106] "start" "complete" "start" "complete" ...
#> $ timestamp : chr [1:106] "20/11/2017 10:18:17" "20/11/2017 10:20:06" "20/11/2017 10:34:08" "20/11/2017 10:41:48" ...
#> $ triagecode : num [1:106] 3 3 3 3 NA NA 3 3 3 3 ...
#> $ specialization : chr [1:106] "TRAU" "TRAU" "TRAU" "TRAU" ...
#> $ event_matching : num [1:106] 1 1 1 1 1 1 1 1 1 1 ...Both datasets were artificially created merely to illustrate the package’s functionalities.
First of all, data must be read and prepared such that the quality
assessment tests can be executed. Data preparation requires transforming
the dataset to a standardised activity log format. However, earlier we
mentioned two input data formats: an activity log and an event log. When
an event log is available, it needs to be converted to an activity log.
daqapo provides a set of functions, with the aid of
bupaR, to assist the user in this process.
As mentioned earlier, the goal of reading and preparing data is to obtain a standardised activity log format. When your source data is already in this format, preparations come down to the following elements:
POSIXct timestamp formatFor this section, the dataset hospital will be used to
illustrate data preparations. Three main functions help the user to
prepare his/her own dataset:
renameconvert_timestampactivitylogThe activity log object adds a mapping to the data frame to link each
column with its specific meaning. In this regard, the timestamp columns
each represent a different lifecycle state. daqapo must
know which column is which, requiring standardised timestamp names. The
accepted timestamp values are:
The two timestamps required by daqapo are start and
complete.
hospital %>%
rename(start = start_ts,
complete = complete_ts) -> hospitalEach timestamp must also be in the POSIXct format.
hospital %>%
convert_timestamps(c("start","complete"), format = dmy_hms) -> hospitalWhen the timestamps are edited to the desired format, the activity log object can be created along with the required mapping.
hospital %>%
activitylog(case_id = "patient_visit_nr",
activity_id = "activity",
resource_id = "originator",
timestamps = c("start", "complete")) -> hospitalWith event logs, things are a bit more complex. In an event log, each
row represents only a part of an activity instance. Therefore, more
complex data transformations must be executed and several problems could
arise. In this section, we will use an event log variant of the activity
log used earlier, named hospital_events.
hospital_events
#> # A tibble: 106 x 8
#> patient_visit_nr activity originator event_lifecycle~ timestamp triagecode
#> <dbl> <chr> <chr> <chr> <chr> <dbl>
#> 1 510 registrati~ Clerk 9 start 20/11/20~ 3
#> 2 510 registrati~ Clerk 9 complete 20/11/20~ 3
#> 3 510 Triage Nurse 27 start 20/11/20~ 3
#> 4 510 Triage Nurse 27 complete 20/11/20~ 3
#> 5 510 Clinical e~ Doctor 7 start 20/11/20~ NA
#> 6 510 Clinical e~ Doctor 4 complete 20/11/20~ NA
#> 7 512 Registrati~ Clerk 12 start 20/11/20~ 3
#> 8 512 Registrati~ Clerk 12 complete 20/11/20~ 3
#> 9 512 Triage Nurse 27 start 20/11/20~ 3
#> 10 512 Triage Nurse 27 complete 20/11/20~ 3
#> # ... with 96 more rows, and 2 more variables: specialization <chr>,
#> # event_matching <dbl>The same principle regarding the timestamps apply. Therefore, the
POSIXct format must be applied in advance. Additionally,
the event log object also requires an activity instance id. If needed,
one can be created manually as illustrated below.
The following functions form the building blocks of the required data preparation, but not all must be called to obtain a fully prepared activity log at all times:
convert_timestampsassign_instance_idcheck/fix_resource_inconsistenciesstandardize_lifecycleeventlogto_activityloghospital_events %>%
bupaR::convert_timestamps(c("timestamp"), format = dmy_hms) %>%
bupaR::mutate(event_matching = paste(patient_visit_nr, activity, event_matching)) %>%
bupaR::eventlog(case_id = "patient_visit_nr",
activity_id = "activity",
activity_instance_id = "event_matching",
timestamp = "timestamp",
resource_id = "originator",
lifecycle_id = "event_lifecycle_state") %>%
fix_resource_inconsistencies() %>%
bupaR::to_activitylog() -> hospital_events
#> Warning in validate_eventlog(eventlog): The following activity instances are
#> connected to more than one resource: 510 Clinical exam 1,518 Registration 1,518
#> Registration 2,518 Registration 3
#> *** OUTPUT ***
#> A total of 4 activity executions in the event log are classified as inconsistencies.
#> They are spread over the following cases and activities:
#> # A tibble: 4 x 5
#> patient_visit_nr activity event_matching complete start
#> <dbl> <chr> <chr> <chr> <chr>
#> 1 510 Clinical exam 510 Clinical exam 1 Doctor 4 Doctor 7
#> 2 518 Registration 518 Registration 1 Clerk 9 Clerk 6
#> 3 518 Registration 518 Registration 2 Clerk 12 Clerk 9
#> 4 518 Registration 518 Registration 3 Clerk 3 Clerk 12
#> Inconsistencies solved succesfully.The table below summarizes the different data quality assessment
tests available in daqapo, after which each test will be
briefly demonstrated.
| Function name | Description | Output |
|---|---|---|
| detect_activity_frequency_violations | Function that detects activity frequency anomalies per case | Summary in console + Returns activities in cases which are executed too many times |
| detect_activity_order_violations | Function detecting violations in activity order | Summary in console + Returns detected orders which violate the specified order |
| detect_attribute_dependencies | Function detecting violations of dependencies between attributes (i.e. condition(s) that should hold when (an)other condition(s) hold(s)) | Summary in console + Returns rows with dependency violations |
| detect_case_id_sequence_gaps | Function detecting gaps in the sequence of case identifiers | Summary in console + Returns case IDs which should be expected to be present |
| detect_conditional_activity_presence | Function detection violations of conditional activity presence (i.e. activity/activities that should be present when (a) particular condition(s) hold(s)) | Summary in console + Returns cases violating conditional activity presence |
| detect_duration_outliers | Function detecting duration outliers for a particular activity | Summary in console + Returns rows with outliers |
| detect_inactive_periods | Function detecting inactive periods, i.e. periods of time in which no activity executions/arrivals are recorded | Summary in console + Returns periods of inactivity |
| detect_incomplete_cases | Function detecting incomplete cases in terms of the activities that need to be recorded for a case | Summary in console + Returns traces in which the mentioned activities are not present |
| detect_incorrect_activity_names | Function returning the incorrect activity labels in the log | Summary in console + Returns rows with incorrect activities |
| detect_missing_values | Function detecting missing values at different levels of aggregation | Summary in console + Returns rows with NAs |
| detect_multiregistration | Function detecting the registration of a series of events in a short time period for the same case or by the same resource | Summary in console + Returns rows with multiregistration on resource or case level |
| detect_overlaps | Checks if a resource has performed two activities in parallel | Data frame containing the activities, the number of overlaps and average overlap in minutes |
| detect_related_activities | Function detecting missing related activities, i.e. activities that should be registered because another activity is registered for a case | Summary in console + Returns cases violating related activities |
| detect_similar_labels | Function detecting potential spelling mistakes | Table showing similarities for each label |
| detect_time_anomalies | Funtion detecting activity executions with negative or zero duration | Summary in console + Returns rows with negative or zero durations |
| detect_unique_values | Function listing all distinct combinations of the given log attributes | Summary in console + Returns all unique combinations of values in given columns |
| detect_value_range_violations | Function detecting violations of the range of acceptable values | Summary in console + Returns rows with value range infringements |
hospital %>%
detect_activity_frequency_violations("Registration" = 1,
"Clinical exam" = 1)
#> *** OUTPUT ***
#> For 3 cases in the activity log (13.6363636363636%) an anomaly is detected.
#> The anomalies are spread over the following cases:
#> # A tibble: 3 x 3
#> patient_visit_nr activity n
#> <dbl> <chr> <int>
#> 1 518 Registration 3
#> 2 512 Clinical exam 2
#> 3 535 Registration 2hospital %>%
detect_activity_order_violations(activity_order = c("Registration", "Triage", "Clinical exam",
"Treatment", "Treatment evaluation"))
#> Warning in detect_activity_order_violations.activitylog(., activity_order =
#> c("Registration", : Some activity instances within the same case overlap. Use
#> detect_overlaps to investigate further.
#> Warning in detect_activity_order_violations.activitylog(., activity_order
#> = c("Registration", : Not all specified activities occur in each case. Use
#> detect_incomplete_cases to investigate further.
#> Selected timestamp parameter value: both
#> *** OUTPUT ***
#> It was checked whether the activity order Registration - Triage - Clinical exam - Treatment - Treatment evaluation is respected.
#> This activity order is respected for 22 (100%) of the cases and not for0 (0%) of the cases.hospital %>%
detect_attribute_dependencies(antecedent = activity == "Registration",
consequent = startsWith(originator,"Clerk"))
#> *** OUTPUT ***
#> The following statement was checked: if condition(s) ~activity == "Registration" hold(s), then ~startsWith(originator, "Clerk") should also hold.
#> This statement holds for 12 (85.71%) of the rows in the activity log for which the first condition(s) hold and does not hold for 2 (14.29%) of these rows.
#> For the following rows, the first condition(s) hold(s), but the second condition does not:
#> # Log of 10 events consisting of:
#> 2 traces
#> 4 cases
#> 5 instances of 1 activity
#> 5 resources
#> Events occurred from 2017-11-21 18:10:17 until 2017-11-22 18:37:00
#>
#> # Variables were mapped as follows:
#> Case identifier: patient_visit_nr
#> Activity identifier: activity
#> Resource identifier: originator
#> Timestamps: start, complete
#>
#> # A tibble: 5 x 8
#> patient_visit_nr activity originator start complete
#> <dbl> <chr> <chr> <dttm> <dttm>
#> 1 528 Registrat~ Nurse 6 2017-11-21 18:10:17 2017-11-21 18:15:04
#> 2 535 Registrat~ Clerk 3 2017-11-22 10:04:57 2017-11-22 10:06:46
#> 3 536 Registrat~ Clerk 9 2017-11-22 10:26:41 2017-11-22 10:32:56
#> 4 535 Registrat~ Clerk 6 2017-11-22 11:05:42 2017-11-22 11:11:11
#> 5 534 Registrat~ <NA> 2017-11-22 18:35:00 2017-11-22 18:37:00
#> # ... with 3 more variables: triagecode <dbl>, specialization <chr>,
#> # .order <int>hospital %>%
detect_case_id_sequence_gaps()
#> *** OUTPUT ***
#> It was checked whether there are gaps in the sequence of case IDs
#> From the 27 expected cases in the activity log, ranging from 510 to 536, 5 (18.52%) are missing.
#> These missing case numbers are:
#> # A tibble: 2 x 3
#> from to n_missing
#> <dbl> <dbl> <dbl>
#> 1 511 511 1
#> 2 513 516 4hospital %>%
detect_conditional_activity_presence(condition = specialization == "TRAU",
activities = "Clinical exam")
#> *** OUTPUT ***
#> The following statement was checked: if condition(s) ~specialization == "TRAU" hold(s), then activity/activities Clinical exam should be recorded
#> The condition(s) hold(s) for 2 cases. From these cases:
#> - the specified activity/activities is/are recorded for 2 case(s) (100%)
#> - the specified activity/activities is/are not recorded for 0 case(s) (0%)hospital %>%
detect_duration_outliers(Treatment = duration_within(bound_sd = 1))
#> *** OUTPUT ***
#> Outliers are detected for following activities
#> Treatment Lower bound: 5.06 Upper bound: 22.2
#> A total of 1 is detected (1.89% of the activity executions)
#> For the following activity instances, outliers are detected:
#> # Log of 2 events consisting of:
#> 1 trace
#> 1 case
#> 1 instance of 1 activity
#> 1 resource
#> Events occurred from 2017-11-21 18:26:04 until 2017-11-21 18:55:00
#>
#> # Variables were mapped as follows:
#> Case identifier: patient_visit_nr
#> Activity identifier: activity
#> Resource identifier: originator
#> Timestamps: start, complete
#>
#> # A tibble: 1 x 14
#> patient_visit_nr activity originator start complete
#> <dbl> <chr> <chr> <dttm> <dttm>
#> 1 523 Treatment Nurse 17 2017-11-21 18:26:04 2017-11-21 18:55:00
#> # ... with 9 more variables: triagecode <dbl>, specialization <chr>,
#> # .order <int>, duration <dbl>, mean <dbl>, sd <dbl>, bound_sd <dbl>,
#> # lower_bound <dbl>, upper_bound <dbl>hospital %>%
detect_duration_outliers(Treatment = duration_within(lower_bound = 0, upper_bound = 15))
#> *** OUTPUT ***
#> Outliers are detected for following activities
#> Treatment Lower bound: 0 Upper bound: 15
#> A total of 1 is detected (1.89% of the activity executions)
#> For the following activity instances, outliers are detected:
#> # Log of 2 events consisting of:
#> 1 trace
#> 1 case
#> 1 instance of 1 activity
#> 1 resource
#> Events occurred from 2017-11-21 18:26:04 until 2017-11-21 18:55:00
#>
#> # Variables were mapped as follows:
#> Case identifier: patient_visit_nr
#> Activity identifier: activity
#> Resource identifier: originator
#> Timestamps: start, complete
#>
#> # A tibble: 1 x 14
#> patient_visit_nr activity originator start complete
#> <dbl> <chr> <chr> <dttm> <dttm>
#> 1 523 Treatment Nurse 17 2017-11-21 18:26:04 2017-11-21 18:55:00
#> # ... with 9 more variables: triagecode <dbl>, specialization <chr>,
#> # .order <int>, duration <dbl>, mean <dbl>, sd <dbl>, bound_sd <dbl>,
#> # lower_bound <dbl>, upper_bound <dbl>hospital %>%
detect_inactive_periods(threshold = 30)
#> Selected timestamp parameter value: both
#> Selected inactivity type:arrivals
#> *** OUTPUT ***
#> Specified threshold of 30 minutes is violated 9 times.
#> Threshold is violated in the following periods:
#> period_start period_end time_gap
#> 1 2017-11-20 10:20:06 2017-11-21 11:35:16 1515.16667
#> 2 2017-11-21 11:22:16 2017-11-21 11:59:41 37.41667
#> 3 2017-11-21 12:05:52 2017-11-21 13:43:16 97.40000
#> 4 2017-11-21 14:06:09 2017-11-21 15:12:17 66.13333
#> 5 2017-11-21 15:18:19 2017-11-21 16:42:08 83.81667
#> 6 2017-11-21 17:06:10 2017-11-21 18:02:10 56.00000
#> 7 2017-11-21 18:15:04 2017-11-22 10:04:57 949.88333
#> 8 2017-11-22 10:32:56 2017-11-22 16:30:00 357.06667
#> 9 2017-11-22 17:00:00 2017-11-22 18:00:00 60.00000hospital %>%
detect_incomplete_cases(activities = c("Registration","Triage","Clinical exam","Treatment","Treatment evaluation"))
#> *** OUTPUT ***
#> It was checked whether the activities Clinical exam, Registration, Treatment, Treatment evaluation, Triage are present for cases.
#> These activities are present for 4 (39.62%) of the cases and are not present for 18 (60.38%) of the cases.
#> Note: this function only checks the presence of activities for a particular case, not the completeness of these entries in the activity log or the order of activities.
#> For cases for which the aforementioned activities are not all present, the following activities are recorded (ordered by decreasing frequeny of occurrence):
#> # A tibble: 9 x 3
#> activity n case_ids
#> <chr> <int> <chr>
#> 1 Triage 11 510 - 512 - 517 - 521 - 524 - 525 - 526 - 527 - 52~
#> 2 Registration 9 512 - 518 - 518 - 518 - 521 - 522 - 527 - 528 - 534
#> 3 Clinical exam 5 512 - 510 - 527 - 528 - 512
#> 4 Treatment evaluation 2 529 - 532
#> 5 0 1 533
#> 6 Trage 1 520
#> 7 Treatment 1 532
#> 8 Triaga 1 522
#> 9 registration 1 510hospital %>%
detect_incorrect_activity_names(allowed_activities = c("Registration","Triage","Clinical exam","Treatment","Treatment evaluation"))
#> *** OUTPUT ***
#> 4 out of 9 (44.44% ) activity labels are identified to be incorrect.
#> These activity labels are:
#> registration - Trage - Triaga - 0
#> Given this information, 4 of 53 (7.55%) rows in the activity log are incorrect. These are the following:
#> # Log of 8 events consisting of:
#> 4 traces
#> 4 cases
#> 4 instances of 4 activities
#> 4 resources
#> Events occurred from 2017-11-20 10:18:17 until 2017-11-22 18:37:00
#>
#> # Variables were mapped as follows:
#> Case identifier: patient_visit_nr
#> Activity identifier: activity
#> Resource identifier: originator
#> Timestamps: start, complete
#>
#> # A tibble: 4 x 8
#> patient_visit_nr activity originator start complete
#> <dbl> <chr> <chr> <dttm> <dttm>
#> 1 510 registrat~ Clerk 9 2017-11-20 10:18:17 2017-11-20 10:20:06
#> 2 520 Trage Nurse 17 2017-11-21 13:43:16 2017-11-21 13:39:00
#> 3 522 Triaga Nurse 5 2017-11-21 15:15:25 2017-11-21 15:18:04
#> 4 533 0 <NA> 2017-11-22 18:35:00 2017-11-22 18:37:00
#> # ... with 3 more variables: triagecode <dbl>, specialization <chr>,
#> # .order <int>hospital %>%
detect_missing_values(column = "activity")
#> Selected level of aggregation:overview
#> Warning in detect_missing_values.activitylog(., column = "activity"): Ignoring
#> provided column argument at overview level.
#> *** OUTPUT ***
#> Absolute number of missing values per column:
#>
#> patient_visit_nr 0
#> activity 0
#> originator 2
#> start 1
#> complete 0
#> triagecode 1
#> specialization 0
#> .order 0
#> Relative number of missing values per column (expressed as percentage):
#>
#> patient_visit_nr 0.000000
#> activity 0.000000
#> originator 3.773585
#> start 1.886792
#> complete 0.000000
#> triagecode 1.886792
#> specialization 0.000000
#> .order 0.000000
#> Overview of activity log rows which are incomplete:
#> # Log of 7 events consisting of:
#> 3 traces
#> 4 cases
#> 4 instances of 3 activities
#> 2 resources
#> Events occurred from NA until NA
#>
#> # Variables were mapped as follows:
#> Case identifier: patient_visit_nr
#> Activity identifier: activity
#> Resource identifier: originator
#> Timestamps: start, complete
#>
#> # A tibble: 4 x 8
#> patient_visit_nr activity originator start complete
#> <dbl> <chr> <chr> <dttm> <dttm>
#> 1 510 Clinical ~ Doctor 7 2017-11-20 11:35:01 2017-11-20 11:36:09
#> 2 533 0 <NA> 2017-11-22 18:35:00 2017-11-22 18:37:00
#> 3 534 Registrat~ <NA> 2017-11-22 18:35:00 2017-11-22 18:37:00
#> 4 512 Clinical ~ Doctor 7 NA 2017-11-20 11:33:57
#> # ... with 3 more variables: triagecode <dbl>, specialization <chr>,
#> # .order <int>
## column heeft hier geen zin?!hospital %>%
detect_missing_values(level_of_aggregation = "activity")
#> Selected level of aggregation:activity
#> *** OUTPUT ***
#> Absolute number of missing values per column (per activity):
#> # A tibble: 9 x 8
#> activity patient_visit_nr originator start complete triagecode specialization
#> <chr> <int> <int> <int> <int> <int> <int>
#> 1 0 0 1 0 0 0 0
#> 2 Clinical~ 0 0 1 0 1 0
#> 3 Registra~ 0 1 0 0 0 0
#> 4 Trage 0 0 0 0 0 0
#> 5 Treatment 0 0 0 0 0 0
#> 6 Treatmen~ 0 0 0 0 0 0
#> 7 Triaga 0 0 0 0 0 0
#> 8 Triage 0 0 0 0 0 0
#> 9 registra~ 0 0 0 0 0 0
#> # ... with 1 more variable: .order <int>
#> Relative number of missing values per column (per activity, expressed as percentage):
#> # A tibble: 9 x 8
#> activity patient_visit_nr originator start complete triagecode specialization
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 0 0 1 0 0 0 0
#> 2 Clinical~ 0 0 0.111 0 0.111 0
#> 3 Registra~ 0 0.0714 0 0 0 0
#> 4 Trage 0 0 0 0 0 0
#> 5 Treatment 0 0 0 0 0 0
#> 6 Treatmen~ 0 0 0 0 0 0
#> 7 Triaga 0 0 0 0 0 0
#> 8 Triage 0 0 0 0 0 0
#> 9 registra~ 0 0 0 0 0 0
#> # ... with 1 more variable: .order <dbl>
#> Overview of activity log rows which are incomplete:
#> # Log of 7 events consisting of:
#> 3 traces
#> 4 cases
#> 4 instances of 3 activities
#> 2 resources
#> Events occurred from NA until NA
#>
#> # Variables were mapped as follows:
#> Case identifier: patient_visit_nr
#> Activity identifier: activity
#> Resource identifier: originator
#> Timestamps: start, complete
#>
#> # A tibble: 4 x 8
#> patient_visit_nr activity originator start complete
#> <dbl> <chr> <chr> <dttm> <dttm>
#> 1 510 Clinical ~ Doctor 7 2017-11-20 11:35:01 2017-11-20 11:36:09
#> 2 533 0 <NA> 2017-11-22 18:35:00 2017-11-22 18:37:00
#> 3 534 Registrat~ <NA> 2017-11-22 18:35:00 2017-11-22 18:37:00
#> 4 512 Clinical ~ Doctor 7 NA 2017-11-20 11:33:57
#> # ... with 3 more variables: triagecode <dbl>, specialization <chr>,
#> # .order <int>hospital %>%
detect_missing_values(
level_of_aggregation = "column",
column = "triagecode")
#> Selected level of aggregation:column
#> *** OUTPUT ***
#> Absolute number of missing values in columntriagecode:1
#> Relative number of missing values in columntriagecode(expressed as percentage):1.88679245283019
#>
#> Overview of activity log rows in whichtriagecodeis missing:
#> # Log of 2 events consisting of:
#> 1 trace
#> 1 case
#> 1 instance of 1 activity
#> 1 resource
#> Events occurred from 2017-11-20 11:35:01 until 2017-11-20 11:36:09
#>
#> # Variables were mapped as follows:
#> Case identifier: patient_visit_nr
#> Activity identifier: activity
#> Resource identifier: originator
#> Timestamps: start, complete
#>
#> # A tibble: 1 x 8
#> patient_visit_nr activity originator start complete
#> <dbl> <chr> <chr> <dttm> <dttm>
#> 1 510 Clinical ~ Doctor 7 2017-11-20 11:35:01 2017-11-20 11:36:09
#> # ... with 3 more variables: triagecode <dbl>, specialization <chr>,
#> # .order <int>hospital %>%
detect_multiregistration(threshold_in_seconds = 10)
#> Selected level of aggregation: resource
#> Selected timestamp parameter value: complete
#> *** OUTPUT ***
#> Multi-registration is detected for 4 of the 12 resources (33.33%). These resources are:
#> Doctor 7 - Nurse 27 - Nurse 5 - NA
#> For the following rows in the activity log, multi-registration is detected:
#> # Log of 17 events consisting of:
#> 5 traces
#> 7 cases
#> 9 instances of 5 activities
#> 4 resources
#> Events occurred from NA until NA
#>
#> # Variables were mapped as follows:
#> Case identifier: patient_visit_nr
#> Activity identifier: activity
#> Resource identifier: originator
#> Timestamps: start, complete
#>
#> # A tibble: 9 x 8
#> originator patient_visit_nr activity start complete
#> <chr> <dbl> <chr> <dttm> <dttm>
#> 1 Doctor 7 512 Clinical ~ 2017-11-20 11:27:12 2017-11-20 11:33:57
#> 2 Doctor 7 512 Clinical ~ NA 2017-11-20 11:33:57
#> 3 Nurse 27 536 Triage 2017-11-22 15:15:39 2017-11-22 15:25:01
#> 4 Nurse 27 536 Treatment 2017-11-22 15:15:41 2017-11-22 15:25:03
#> 5 Nurse 5 524 Triage 2017-11-21 17:04:03 2017-11-21 17:06:05
#> 6 Nurse 5 525 Triage 2017-11-21 17:04:13 2017-11-21 17:06:08
#> 7 Nurse 5 526 Triage 2017-11-21 17:04:15 2017-11-21 17:06:10
#> 8 <NA> 533 0 2017-11-22 18:35:00 2017-11-22 18:37:00
#> 9 <NA> 534 Registrat~ 2017-11-22 18:35:00 2017-11-22 18:37:00
#> # ... with 3 more variables: triagecode <dbl>, specialization <chr>,
#> # .order <int>hospital %>%
detect_overlaps()
#> # A tibble: 7 x 4
#> activity_a activity_b n avg_overlap_mins
#> <chr> <chr> <int> <dbl>
#> 1 Clinical exam Treatment 2 8.17
#> 2 Registration Clinical exam 1 1.9
#> 3 Registration Triaga 1 2.65
#> 4 Registration Triage 1 1.93
#> 5 Triage Clinical exam 2 5.63
#> 6 Triage Registration 1 0.817
#> 7 Triage Treatment 1 9.33hospital %>%
detect_similar_labels(column_labels = "activity", max_edit_distance = 3)
#> Warning in detect_similar_labels.activitylog(., column_labels = "activity", :
#> Not all provided columns are of type character or factor and will be ignored:
#> patient_visit_nr,start,complete,.order
#> # A tibble: 16 x 3
#> column_labels labels similar_to
#> <chr> <chr> <chr>
#> 1 activity registration Registration
#> 2 activity Registration registration
#> 3 activity Triage Trage - Triaga
#> 4 activity Trage Triage - Triaga
#> 5 activity Triaga Triage - Trage
#> 6 originator Clerk 9 Clerk 12 - Clerk 6 - Clerk 3
#> 7 originator Clerk 12 Clerk 9 - Clerk 6 - Clerk 3
#> 8 originator Nurse 27 Nurse 17 - Nurse 5 - Nurse 6
#> 9 originator Doctor 7 Doctor 4 - Doctor 1
#> 10 originator Nurse 17 Nurse 27 - Nurse 5 - Nurse 6
#> 11 originator Clerk 6 Clerk 9 - Clerk 12 - Clerk 3
#> 12 originator Doctor 4 Doctor 7 - Doctor 1
#> 13 originator Clerk 3 Clerk 9 - Clerk 12 - Clerk 6
#> 14 originator Nurse 5 Nurse 27 - Nurse 17 - Nurse 6
#> 15 originator Nurse 6 Nurse 27 - Nurse 17 - Nurse 5
#> 16 originator Doctor 1 Doctor 7 - Doctor 4hospital %>%
detect_time_anomalies()
#> Selected anomaly type: both
#> *** OUTPUT ***
#> For 5 rows in the activity log (9.43%), an anomaly is detected.
#> The anomalies are spread over the activities as follows:
#> # A tibble: 3 x 3
#> activity type n
#> <chr> <chr> <int>
#> 1 Registration negative duration 3
#> 2 Clinical exam zero duration 1
#> 3 Trage negative duration 1
#> Anomalies are found in the following rows:
#> # Log of 10 events consisting of:
#> 3 traces
#> 3 cases
#> 5 instances of 3 activities
#> 5 resources
#> Events occurred from 2017-11-21 11:22:16 until 2017-11-21 19:00:00
#>
#> # Variables were mapped as follows:
#> Case identifier: patient_visit_nr
#> Activity identifier: activity
#> Resource identifier: originator
#> Timestamps: start, complete
#>
#> # A tibble: 5 x 10
#> patient_visit_nr activity originator start complete
#> <dbl> <chr> <chr> <dttm> <dttm>
#> 1 518 Registrat~ Clerk 12 2017-11-21 11:45:16 2017-11-21 11:22:16
#> 2 518 Registrat~ Clerk 6 2017-11-21 11:45:16 2017-11-21 11:22:16
#> 3 518 Registrat~ Clerk 9 2017-11-21 11:45:16 2017-11-21 11:22:16
#> 4 520 Trage Nurse 17 2017-11-21 13:43:16 2017-11-21 13:39:00
#> 5 528 Clinical ~ Doctor 1 2017-11-21 19:00:00 2017-11-21 19:00:00
#> # ... with 5 more variables: triagecode <dbl>, specialization <chr>,
#> # .order <int>, duration <dbl>, type <chr>hospital %>%
detect_unique_values(column_labels = "activity")
#> *** OUTPUT ***
#> Distinct entries are computed for the following columns:
#> activity
#> # Log of 105 events consisting of:
#> 14 traces
#> 22 cases
#> 53 instances of 9 activities
#> 12 resources
#> Events occurred from NA until NA
#>
#> # Variables were mapped as follows:
#> Case identifier: patient_visit_nr
#> Activity identifier: activity
#> Resource identifier: originator
#> Timestamps: start, complete
#>
#> # A tibble: 53 x 6
#> activity patient_visit_nr originator start complete
#> <chr> <dbl> <chr> <dttm> <dttm>
#> 1 registra~ 510 Clerk 9 2017-11-20 10:18:17 2017-11-20 10:20:06
#> 2 Registra~ 512 Clerk 12 2017-11-20 10:33:14 2017-11-20 10:37:00
#> 3 Triage 510 Nurse 27 2017-11-20 10:34:08 2017-11-20 10:41:48
#> 4 Triage 512 Nurse 27 2017-11-20 10:44:12 2017-11-20 10:50:17
#> 5 Clinical~ 512 Doctor 7 2017-11-20 11:27:12 2017-11-20 11:33:57
#> 6 Clinical~ 510 Doctor 7 2017-11-20 11:35:01 2017-11-20 11:36:09
#> 7 Triage 517 Nurse 17 2017-11-21 11:35:16 2017-11-21 11:39:00
#> 8 Registra~ 518 Clerk 12 2017-11-21 11:45:16 2017-11-21 11:22:16
#> 9 Registra~ 518 Clerk 6 2017-11-21 11:45:16 2017-11-21 11:22:16
#> 10 Registra~ 518 Clerk 9 2017-11-21 11:45:16 2017-11-21 11:22:16
#> # ... with 43 more rows, and 1 more variable: .order <int>hospital %>%
detect_unique_values(column_labels = c("activity", "originator"))
#> *** OUTPUT ***
#> Distinct entries are computed for the following columns:
#> activity - originator
#> # Log of 105 events consisting of:
#> 14 traces
#> 22 cases
#> 53 instances of 9 activities
#> 12 resources
#> Events occurred from NA until NA
#>
#> # Variables were mapped as follows:
#> Case identifier: patient_visit_nr
#> Activity identifier: activity
#> Resource identifier: originator
#> Timestamps: start, complete
#>
#> # A tibble: 53 x 6
#> activity originator patient_visit_nr start complete
#> <chr> <chr> <dbl> <dttm> <dttm>
#> 1 registra~ Clerk 9 510 2017-11-20 10:18:17 2017-11-20 10:20:06
#> 2 Registra~ Clerk 12 512 2017-11-20 10:33:14 2017-11-20 10:37:00
#> 3 Triage Nurse 27 510 2017-11-20 10:34:08 2017-11-20 10:41:48
#> 4 Triage Nurse 27 512 2017-11-20 10:44:12 2017-11-20 10:50:17
#> 5 Clinical~ Doctor 7 512 2017-11-20 11:27:12 2017-11-20 11:33:57
#> 6 Clinical~ Doctor 7 510 2017-11-20 11:35:01 2017-11-20 11:36:09
#> 7 Triage Nurse 17 517 2017-11-21 11:35:16 2017-11-21 11:39:00
#> 8 Registra~ Clerk 12 518 2017-11-21 11:45:16 2017-11-21 11:22:16
#> 9 Registra~ Clerk 6 518 2017-11-21 11:45:16 2017-11-21 11:22:16
#> 10 Registra~ Clerk 9 518 2017-11-21 11:45:16 2017-11-21 11:22:16
#> # ... with 43 more rows, and 1 more variable: .order <int>hospital %>%
detect_value_range_violations(triagecode = domain_numeric(from = 0, to = 5))
#> $triagecode
#> $type
#> [1] "numeric"
#>
#> $from
#> [1] 0
#>
#> $to
#> [1] 5
#>
#> attr(,"class")
#> [1] "value_range" "list"
#> *** OUTPUT ***
#> The domain range for column triagecode is checked.
#> Values allowed between 0 and 5
#> The values fall within the specified domain range for 46 (86.79%) of the rows in the activity log and outside the domain range for 7 (13.21%) of these rows.
#>
#> The following rows fall outside the specified domain range for indicated column:
#> # Log of 14 events consisting of:
#> 5 traces
#> 6 cases
#> 7 instances of 5 activities
#> 4 resources
#> Events occurred from 2017-11-20 11:35:01 until 2017-11-23 18:33:00
#>
#> # Variables were mapped as follows:
#> Case identifier: patient_visit_nr
#> Activity identifier: activity
#> Resource identifier: originator
#> Timestamps: start, complete
#>
#> # A tibble: 7 x 9
#> column_checked patient_visit_nr activity originator start
#> <chr> <dbl> <chr> <chr> <dttm>
#> 1 triagecode 510 Clinical exam Doctor 7 2017-11-20 11:35:01
#> 2 triagecode 529 Treatment eval~ Doctor 1 2017-11-22 16:30:00
#> 3 triagecode 530 Triage Nurse 17 2017-11-22 18:00:00
#> 4 triagecode 531 Triage Nurse 17 2017-11-22 18:05:00
#> 5 triagecode 532 Treatment Nurse 17 2017-11-22 18:15:00
#> 6 triagecode 532 Treatment eval~ Doctor 7 2017-11-22 18:27:00
#> 7 triagecode 533 0 <NA> 2017-11-22 18:35:00
#> # ... with 4 more variables: complete <dttm>, triagecode <dbl>,
#> # specialization <chr>, .order <int>Hasselt University, Research group Business Informatics | Research Foundation Flanders (FWO). niels.martin@uhasselt.be↩︎
Hasselt University, Research group Business Informatics. greg.vanhoudt@uhasselt.be↩︎
Hasselt University, Research group Business Informatics. gert.janssenswillen@uhasselt.be↩︎