İzleri İncelemek

Süreci tanımak için önce ne kadar yapılandırılmış olduğuna bakalım. Başlamak için (1) iz kapsamının bir grafiğini çiz ve (2) aşağıdaki ifadelerden hangisinin doğru olduğunu belirt. Ardından (3) trace_explorer() kullanarak en sık görülen süreç varyantlarını görselleştir.

İz kapsamı, level = "trace" ile trace_coverage() metriği kullanılarak elde edilebilir. Sonuç, iz sayısı ile olay günlüğünün kümülatif kapsaması arasındaki ilişkiyi gösterir.

Bu egzersiz, kursun bir parçasıdır

R ile İş Süreci Analitiği

Kursa Göz Atın

Uygulamalı etkileşimli egzersiz

Bu egzersizi bu örnek kodu tamamlayarak deneyin.

# Create trace coverage graph
otc %>%
  ___ %>%
  ___

Kodu Düzenle ve Çalıştır

Bu egzersiz, kursun bir parçasıdır

R ile İş Süreci Analitiği

IntermediárioNível de habilidade

4.8+

42 reviews

Kursa Ücretsiz Başla

The amount of event data has grown enormously during the last decades. A considerable amount of this data is recorded within the context of various business process. In this chapter, you will discover a methodology for analyzing process data, consisting of three stages: extraction, processing and analysis. You will have our first encounter with the specific elements of process data which are required for analysis, and have a first deep dive into the world of activities and traces, which will allow you to reveal of first glimpse of the process.

Exercise 1: Introduction to process analytics Exercise 2: Identify process elements Exercise 3: Process Analysis Workflow Exercise 4: Activities as cornerstones of processes Exercise 5: Retrieve basic process information Exercise 6: Explore activities Exercise 7: The happy path Exercise 8: Create process maps Exercise 9: Components of process data Exercise 10: Event data identifiers Exercise 11: Constructing event data objects Exercise 12: Inspecting event data objects

A process can be looked at from different angles: the control-flow, the performance and the organizational background. In this chapter, you will make a deep dive into each of these perspectives. The control-flow refers to the different ways in which the process can be executed, and thus, how it is structured. Considering performance, we are both interested in discovering how long things take, as well as when they take place. Finally, the organizational perspective looks at the actors in the process.

Exercise 1: Organizational analysis Exercise 2: Specialization of resources Exercise 3: Detecting roles and specializations Exercise 4: Involvement of resources Exercise 5: Structuredness Exercise 6: Process variants Exercise 7: Rework Exercise 8: Precedence matrix Exercise 9: Performance analysis Exercise 10: Eating habits Exercise 11: Eating habits - Dotted chart Exercise 12: Eat, sleep, repeat Exercise 13: Linking perspectives Exercise 14: The right person on the job I Exercise 15: The right person on the job II Exercise 16: The right person on the job faster

Event data rarely comes in a form which is ready to analyze. Therefore, you often require a set of tools to get the data in the right shape, before we can answer our research question. At the end of this chapter, you will be familiar with three common preprocessing tasks: filtering data, aggregating events and enriching data.

Exercise 1: Filtering cases Exercise 2: The path well traveled Exercise 3: The downside of rejections Exercise 4: The downside of refusals Exercise 5: Filtering events Exercise 6: The vicious disapproval circle Exercise 7: Working with high-paid R&D candidates Exercise 8: Aggreggating events Exercise 9: Disapprovals are disapprovals Exercise 10: Zooming out Exercise 11: Admiring the view Exercise 12: Enriching events Exercise 13: Everything has a cost Exercise 14: Cost versus urgency

In this final chapter we will use everything we have learned so far to do and end-to-end analysis of an order-to-cash process. Firstly, we will transform data from various sources to an event log. Secondly, we will have a helicopter view of the process, exploring the dimensions of the data and the different activities, stages and flows in the process. Finally, we will combine preprocessing and analysis tools to formulate an answer to several research questions.

Exercise 1: Olay verilerini hazırlama Exercise 2: Satış Verilerini Dönüştürme (1/3)Exercise 3: Satış Verilerini Dönüştürme (2/3)Exercise 4: Satış Verilerini Dönüştürme (3/3)Exercise 5: Süreci bir araya getirme Exercise 6: Olay günlüğünü oluşturma Exercise 7: Süreci tanımak Exercise 8: İzleri İncelemek

Geçerli egzersiz

Exercise 9: Uzaklaştırarak Bakmak Exercise 10: Roller ve Kurallar Exercise 11: Kaynak Rolleri Exercise 12: 4 Göz Prensibi Exercise 13: Kontrol akışı sapmaları Exercise 14: Hızlı Üretim, Hızlı Teslimat Exercise 15: Çevrim süresi analizi Exercise 16: Kurs Özeti