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Filtering cases Gert Janssenswillen Creator of bupaR DataCamp - PowerPoint PPT Presentation

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DataCamp Business Process Analytics in R BUSINESS PROCESS ANALYTICS IN R Filtering cases Gert Janssenswillen Creator of bupaR DataCamp Business Process Analytics in R DataCamp Business Process Analytics in R DataCamp Business Process

  1. DataCamp Business Process Analytics in R BUSINESS PROCESS ANALYTICS IN R Filtering cases Gert Janssenswillen Creator of bupaR

  2. DataCamp Business Process Analytics in R

  3. DataCamp Business Process Analytics in R

  4. DataCamp Business Process Analytics in R

  5. DataCamp Business Process Analytics in R

  6. DataCamp Business Process Analytics in R

  7. DataCamp Business Process Analytics in R

  8. DataCamp Business Process Analytics in R

  9. DataCamp Business Process Analytics in R

  10. DataCamp Business Process Analytics in R Categories of Case Filters Performance Control-flow characteristics Time period

  11. DataCamp Business Process Analytics in R Performance filters

  12. DataCamp Business Process Analytics in R Performance filters

  13. DataCamp Business Process Analytics in R Filter by absolute interval filter_throughput_time(log, interval = c(5,10))

  14. DataCamp Business Process Analytics in R Filter by absolute interval filter_throughput_time(log, interval = c(5,10))

  15. DataCamp Business Process Analytics in R Filter by Relative Percentage filter_throughput_time(log, percentage = 0.5)

  16. DataCamp Business Process Analytics in R Adjusting filter configurations Negate the filter Cases shorter than 5 days, or longer than 10 days filter_throughput_time(log, interval = c(5,10), units = "days", reverse =TRUE) The 50% longest cases filter_throughput_time(log, percentage = 0.5, reverse = TRUE) Use half-open intervals Select cases with throughput time longer than 5 days. filter_throughput_time(log, interval = c(5,NA), units = "days")

  17. DataCamp Business Process Analytics in R Control-flow filters Activity presence/absence Precendence requirements Start and/or End points Frequency of the trace

  18. DataCamp Business Process Analytics in R Time filters Select cases that started in a specific time window ended in a specific time window are contained in a specific time window intersect , i.e. had at least on activity in a specific time window

  19. DataCamp Business Process Analytics in R BUSINESS PROCESS ANALYTICS IN R Let's practice!

  20. DataCamp Business Process Analytics in R BUSINESS PROCESS ANALYTICS IN R Filtering events Gert Janssenswillen Creator of bupaR

  21. DataCamp Business Process Analytics in R Categories of event filters Trim filters Frequency filters Label filters General Attribute filters

  22. DataCamp Business Process Analytics in R Trim to time period filter_time_period(log, interval = ymd(c("20180110","20180122")), filter_method = "trim")

  23. DataCamp Business Process Analytics in R Trim to start and end points filter_trim(start_activities = "blues")

  24. DataCamp Business Process Analytics in R Trim to start and end points filter_trim(start_activities = "blues", end_activities = "greens")

  25. DataCamp Business Process Analytics in R Trim to start and end points

  26. DataCamp Business Process Analytics in R Filter by frequencies Activity frequency filter_activity_frequency(log, interval = c(50,100)) filter_activity_frequency(log, percentage = 0.8) Resource frequency filter_resource_frequency(log, interval = c(60,900)) filter_resource_frequency(log, percentage = 0.6)

  27. DataCamp Business Process Analytics in R Filter by labels filter_activity(log, activities = c("reds","oranges","purples")))

  28. DataCamp Business Process Analytics in R Filter by conditions filter(log, cost > 1000, priority == "High", ...) Any condition using data attributes can be used Multiple conditions can be combined using &, |, !, etc.

  29. DataCamp Business Process Analytics in R BUSINESS PROCESS ANALYTICS IN R Let's practice!

  30. DataCamp Business Process Analytics in R BUSINESS PROCESS ANALYTICS IN R Aggregating events Gert Janssenswillen Creator of bupaR

  31. DataCamp Business Process Analytics in R Aggregation types Is-A Part-of

  32. DataCamp Business Process Analytics in R Is-a aggregation

  33. DataCamp Business Process Analytics in R Is-a aggregation act_unite(log, "New name" = c("Old Variant 1","Old Variant 2","Old Variant 3"), ...)

  34. DataCamp Business Process Analytics in R Part-of aggregation

  35. DataCamp Business Process Analytics in R Part-of aggregation act_collapse(log, "Sub process" = c("Part 1","Part 2","Part 3"), ...)

  36. DataCamp Business Process Analytics in R Impact on Activity Types and Instances Is-a Part-of Decreased number of activity types Decreased number of activity types Equal number of activity instances Decreased number of activity instances

  37. DataCamp Business Process Analytics in R BUSINESS PROCESS ANALYTICS IN R Let's practice!

  38. DataCamp Business Process Analytics in R BUSINESS PROCESS ANALYTICS IN R Enriching events Gert Janssenswillen Creator of bupaR

  39. DataCamp Business Process Analytics in R Mutate new variables

  40. DataCamp Business Process Analytics in R Mutate new variables log %>% group_by_case() %>% mutate(total_cost = sum(cost, na.rm = T)

  41. DataCamp Business Process Analytics in R Mutate new variables log %>% group_by_case %>% mutate(total_cost = sum(cost, na.rm = T) %>% mutate(impact = case_when(cost <= 1000 ~ "Low", cost <= 5000 ~ "Medium", T ~ "High"))

  42. DataCamp Business Process Analytics in R Mutate new variables log %>% group_by_case %>% mutate(refund_made = any(str_detect(activity, "Pay Claim")))

  43. DataCamp Business Process Analytics in R Adding process metrics Adding information about a case to the original data Its througput time Its length Its amount of rework ... Adding information about activities Its frequency Its specialization by resources ...

  44. DataCamp Business Process Analytics in R Adding process metrics log %>% throughput_time(level = "case", units = "days", append = TRUE) log %>% throughput_time(level = "case", units = "days", append = TRUE) %>% mutate(on_time = processing_time_case <= 7)

  45. DataCamp Business Process Analytics in R BUSINESS PROCESS ANALYTICS IN R Let's practice!

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