Analysis of Business Process Batching Using Causal Event Models

Waibel, Philipp; Novak, Christian; Bala, Saimir; Revoredo, Kate; Mendling, Jan

doi:10.1007/978-3-030-72693-5_2

Cited by 5 publications

(4 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Alternative formalizations of Definition 8 define just a partial order over events [4,30,55,56] describing the local directly-follows relation wrt. various entities 6.…”

Section: Formal Definition Of An Event Knowledge Graphmentioning

confidence: 99%

See 1 more Smart Citation

Process Mining over Multiple Behavioral Dimensions with Event Knowledge Graphs

Fahland

2022

Lecture Notes in Business Information Processing

View full text Add to dashboard Cite

Classical process mining relies on the notion of a unique case identifier, which is used to partition event data into independent sequences of events. In this chapter, we study the shortcomings of this approach for event data over multiple entities. We introduce event knowledge graphs as data structure that allows to naturally model behavior over multiple entities as a network of events. We explore how to construct, query, and aggregate event knowledge graphs to get insights into complex behaviors. We will ultimately show that event knowledge graphs are a very versatile tool that opens the door to process mining analyses in multiple behavioral dimensions at once.

show abstract

“…Alternative formalizations of Definition 8 define just a partial order over events [4,30,55,56] describing the local directly-follows relation wrt. various entities 6.…”

Section: Formal Definition Of An Event Knowledge Graphmentioning

confidence: 99%

“…. , n k [36,42,55]. For example, e 27 batches X1, X2, Y 1, e 30 batches I1, I2, and e 31 batches X3, Y 2.…”

Section: How To Read Synchronization In a Graphmentioning

confidence: 99%

Process Mining over Multiple Behavioral Dimensions with Event Knowledge Graphs

Fahland

2022

Lecture Notes in Business Information Processing

View full text Add to dashboard Cite

show abstract

“…We also observe the requirement to explicitly capture the cardinality between events and event types. The analysis of the cardinality is a key requirement for those processes that exhibit bundling and unbundling scenarios [8,20], processes with divergence and convergence [10], or batching operations [21]. The lack of support by classic PM techniques leads to the construction of loops and spurious relationships for event types that are in a 1:N relationship with the event type that triggers a process.…”

Section: Requirementsmentioning

confidence: 99%

“…If for example an observed event is shared e.g., between several orders that have the same shipment, we obtain a connected graph structure that refers to several orders. In the following, we will call these shared events batching events [21]. The type of the event e.g., Receive Purchase Order or Pick Order Item, is added as a label λ.…”

Section: Definition 4 (Causal Event Graphmentioning

confidence: 99%

Causal Process Mining from Relational Databases with Domain Knowledge

Waibel¹,

Pfahlsberger²,

Revoredo³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

The plethora of algorithms in the research field of process mining builds on directly-follows relations. Even though various improvements have been made in the last decade, there are serious weaknesses of these relationships. Once events associated with different objects that relate with a cardinality of 1:N and N:M to each other, techniques based on directly-follows relations produce spurious relations, self-loops, and back-jumps. This is due to the fact that event sequence as described in classical event logs differs from event causation. In this paper, we address the research problem of representing the causal structure of process-related event data. To this end, we develop a new approach called Causal Process Mining. This approach renounces the use of flat event logs and considers relational databases of event data as an input. More specifically, we transform the relational data structures based on the Causal Process Template into what we call Causal Event Graph. We evaluate our approach and compare its outputs with techniques based on directlyfollows relations in a case study with an European food production company. Our results demonstrate that directly-follows miners produce a large number of spurious relationships, which our approach captures correctly.

show abstract

Data-Driven Analysis of Batch Processing Inefficiencies in Business Processes

Lashkevich

Milani

Chapela-Campa

et al. 2022

Research Challenges in Information Science

View full text Add to dashboard Cite

Batch processing reduces processing time in a business process at the expense of increasing waiting time. If this trade-off between processing and waiting time is not analyzed, batch processing can, over time, evolve into a source of waste in a business process. Therefore, it is valuable to analyze batch processing activities to identify waiting time wastes. Identifying and analyzing such wastes present the analyst with improvement opportunities that, if addressed, can improve the cycle time efficiency (CTE) of a business process. In this paper, we propose an approach that, given a process execution event log, (1) identifies batch processing activities, (2) analyzes their inefficiencies caused by different types of waiting times to provide analysts with information on how to improve batch processing activities. More specifically, we conceptualize different waiting times caused by batch processing patterns and identify improvement opportunities based on the impact of each waiting time type on the CTE. Finally, we demonstrate the applicability of our approach to a real-life event log.

show abstract

Analysis of Business Process Batching Using Causal Event Models

Cited by 5 publications

References 21 publications

Process Mining over Multiple Behavioral Dimensions with Event Knowledge Graphs

Process Mining over Multiple Behavioral Dimensions with Event Knowledge Graphs

Causal Process Mining from Relational Databases with Domain Knowledge

Data-Driven Analysis of Batch Processing Inefficiencies in Business Processes

Contact Info

Product

Resources

About