Passages in Graphs

Directed graphs can be partitioned in so-called passages. A passage P is a set of edges such that any two edges sharing the same initial vertex or sharing the same terminal vertex are both inside $P$ or are both outside of P. Passages were first identified in the context of process mining where they are used to successfully decompose process discovery and conformance checking problems. In this article, we examine the properties of passages. We will show that passages are closed under set operators such as union, intersection and difference. Moreover, any passage is composed of so-called minimal passages. These properties can be exploited when decomposing graph-based analysis and computation problems.Comment: 8 page

Mine your own business : using process mining to turn big data into real value

Like most IT-related phenomena, also the growth of event data complies with Moore’s Law. Similar to the number of transistors on chips, the capacity of hard disks, and the computing power of computers, the digital universe is growing exponentially and roughly doubling every 2 years. Although this is not a new phenomenon, suddenly many organizations realize that increasing amounts of “Big Data” (in the broadest sense of the word) need to be used intelligently in order to compete with other organizations in terms of efficiency, speed and service. However, the goal is not to collect as much data as possible. The real challenge is to turn event data into valuable insights. Only process mining techniques directly relate event data to end-to-end business processes. Existing business process modeling approaches generating piles of process models are typically disconnected from the real processes and information systems. Data-oriented analysis techniques (e.g., data mining and machines learning) typically focus on simple classification, clustering, regression, or rule-learning problems. This keynote paper provides pointers to recent developments in process mining thereby clearly showing that process mining provides a natural link between processes and data on the one hand and performance and compliance on the other hand

Scalable discovery of hybrid process models in a cloud computing environment

Process descriptions are used to create products and deliver services. To lead better processes and services, the first step is to learn a process model. Process discovery is such a technique which can automatically extract process models from event logs. Although various discovery techniques have been proposed, they focus on either constructing formal models which are very powerful but complex, or creating informal models which are intuitive but lack semantics. In this work, we introduce a novel method that returns hybrid process models to bridge this gap. Moreover, to cope with today’s big event logs, we propose an efficient method, called f-HMD, aims at scalable hybrid model discovery in a cloud computing environment. We present the detailed implementation of our approach over the Spark framework, and our experimental results demonstrate that the proposed method is efficient and scalabl

On the User Perception of Configurable Reference Process Models - Initial Insights

Enterprise Systems potentially lead to significant efficiency gains but require a well-conducted configuration process. A configurable reference modelling language based on the widely used EPC notation, which can be used to specify Configurable EPCs (C-EPCs), has been developed to support the task of Enterprise Systems configuration. This paper presents a laboratory experiment on C-EPCs and discusses empirical data on the comparison of C-EPCs to regular EPCs. Using the Method Adoption Model we report on modeller’s perceptions as to the usefulness and ease of use of C-EPCs, concluding that C-EPCs provide sufficient yet improvable conceptual support towards reference model configuration

OC-PM: Analyzing Object-Centric Event Logs and Process Models

Object-centric process mining is a novel branch of process mining that aims to analyze event data from mainstream information systems (such as SAP) more naturally, without being forced to form mutually exclusive groups of events with the specification of a case notion. The development of object-centric process mining is related to exploiting object-centric event logs, which includes exploring and filtering the behavior contained in the logs and constructing process models which can encode the behavior of different classes of objects and their interactions (which can be discovered from object-centric event logs). This paper aims to provide a broad look at the exploration and processing of object-centric event logs to discover information related to the lifecycle of the different objects composing the event log. Also, comprehensive tool support (OC-PM) implementing the proposed techniques is described in the paper