Replaying history on process models for conformance checking and performance analysis

Abstract

Process mining techniques use event data to discover process models, to check the conformance of predefined process models, and to extend such models with information about bottlenecks, decisions, and resource usage. These techniques are driven by observed events rather than hand‐made models. Event logs are used to learn and enrich process models. By replaying history using the model, it is possible to establish a precise relationship between events and model elements. This relationship can be used to check conformance and to analyze performance. For example, it is possible to diagnose deviations from the modeled behavior. The severity of each deviation can be quantified. Moreover, the relationship established during replay and the timestamps in the event log can be combined to show bottlenecks. These examples illustrate the importance of maintaining a proper alignment between event log and process model. Therefore, we elaborate on the realization of such alignments and their application to conformance checking and performance analysis. © 2012 Wiley Periodicals, Inc. This article is categorized under: Algorithmic Development > Association Rules Algorithmic Development > Spatial and Temporal Data Mining Application Areas > Business and Industry Fundamental Concepts of Data and Knowledge > Data Concepts

Toward an Automated Labeling of Event Log Attributes

“…Measuring Fitness can be performed using several approaches such as the "Alignment-based Replay Fitness" [13] and the Petri-net replay technique that allows to detect possible mismatches [9]. The Precision dimension is used to quantify the extra behaviour allowed by a process model that is not recorded in the log [11].…”

Event-Based Real-Time Decomposed Conformance Analysis

“…One of the drawbacks of this approach is that for undeterministic models, the heuristics used in the replay may lead to overestimating the metrics, due to the arti cial creation of super uos tokens in the model. To overcome this fundamental problem, Adriansyah et al propose an alternative approach where the concept of alignments are introduced in order to match an event trace with a path through the model as closely as possible [2,3,4].…”

Improving Business Process Models Using Observed Behavior

“…al. [2,4]. Given a process model and an event log, deviations are expressed in the form of skipped activities (activities that should be performed according to the model, but do not occur in the log) and inserted activities (activities that are not supposed to happen according to the model, but that occur in the log).…”

“…In their notion, a process model needs repair if the observed behavior cannot be replayed by the process model. This is detected using the alignment between the process model and the observed behavior of [2,4]. The detected deviations are then repaired by extending the process model with sub-processes nested in a loop block.…”

“…To explain our approach, it is useful to reflect on the four basic quality dimensions of the process model with respect to the observed behavior [1,2] (cf. Figure 1a).…”