Towards Robust Conformance Checking

Adriansyah, A Arya; Dongen, van Bf Boudewijn; Aalst, van der Wmp Wil

doi:10.1007/978-3-642-20511-8_11

Cited by 90 publications

(63 citation statements)

References 16 publications

(15 reference statements)

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“…Dozens (if not hundreds) of process-mining techniques are available and their value has been proven in many case studies. For example, dozens of process discovery [1,9,11,16,32,18,22,23,27,33,39,48,51,52] and conformance checking [6,13,14,15,21,28,33,41,42,47,50] approaches have been proposed in literature. However, this paper is not about new processmining techniques but about getting the event data needed for all of these techniques.…”

Section: Related Workmentioning

confidence: 99%

Extracting Event Data from Databases to Unleash Process Mining

Aalst

2015

Management for Professionals

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Abstract. Increasingly organizations are using process mining to understand the way that operational processes are executed. Process mining can be used to systematically drive innovation in a digitalized world. Next to the automated discovery of the real underlying process, there are process-mining techniques to analyze bottlenecks, to uncover hidden inefficiencies, to check compliance, to explain deviations, to predict performance, and to guide users towards "better" processes. Dozens (if not hundreds) of process-mining techniques are available and their value has been proven in many case studies. However, process mining stands or falls with the availability of event logs. Existing techniques assume that events are clearly defined and refer to precisely one case (i.e. process instance) and one activity (i.e., step in the process). Although there are systems that directly generate such event logs (e.g., BPM/WFM systems), most information systems do not record events explicitly. Cases and activities only exist implicitly. However, when creating or using process models "raw data" need to be linked to cases and activities. This paper uses a novel perspective to conceptualize a database view on event data. Starting from a class model and corresponding object models it is shown that events correspond to the creation, deletion, or modification of objects and relations. The key idea is that events leave footprints by changing the underlying database. Based on this an approach is described that scopes, binds, and classifies data to create "flat" event logs that can be analyzed using traditional process-mining techniques.

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Section: Related Workmentioning

confidence: 99%

Extracting Event Data from Databases to Unleash Process Mining

Aalst

2015

Management for Professionals

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“…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].…”

Section: Related Workmentioning

confidence: 99%

“…First of all, we note that we apply a heuristic, event-granular replayer similar to the one applied in [15]. The reasoning behind the choice to opt for a replayer playing the token game instead of an alignment-based replayer [2] are twofold. First, alignment-based replayers perform their analysis on a trace, rather than event level, meaning that a complete process instance needs to nalize in order to align the log trace with a process model transition sequence.…”

Section: Phase 3: Replaymentioning

confidence: 99%

“…To benchmark the performance of our developed real-time conformance analysis technique against related approaches, a tting event log was generated (based on We compare our proposed technique against the alignment based replay technique by Adriansyah et al [2] as well our original implementation of the tokengame based heuristic replayer [15]. Both the non-decomposed and decomposed variants of these techniques were included, applying hereto the methodology as described in [8].…”

Section: Experimental Comparisonmentioning

confidence: 99%

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Event-Based Real-Time Decomposed Conformance Analysis

Broucke

Muñoz-Gama

Carmona

et al. 2014

On the Move to Meaningful Internet Systems: OTM 2014 Conferences

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Abstract. Process mining deals with the extraction of knowledge from event logs. One important task within this research eld is denoted as conformance checking, which aims to diagnose deviations and discrepancies between modeled behavior and real-life, observed behavior. Conformance checking techniques still face some challenges, among which scalability, timeliness and traceability issues. In this paper, we propose a novel conformance analysis methodology to support the real-time monitoring of event-based data streams, which is shown to be more e cient than related approaches and able to localize deviations in a more ne-grained manner. Our developed approach can be directly applied in business process contexts where rapid reaction times are crucial; an exhaustive case example is provided to evidence the validity of the approach.

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“…Definition 6 provides the interface to existing process discovery [1,11,12,16,30,18,24,25,28,31,41,54,60,61] and conformance checking [6,13,14,15,22,29,31,42,43,51,59] techniques. M EB,PCV defines how to compute an event log (called sublog) per cell.…”

Section: Process Cube Viewmentioning

confidence: 99%

Process Cubes: Slicing, Dicing, Rolling Up and Drilling Down Event Data for Process Mining

Aalst

2013

Lecture Notes in Business Information Processing

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Abstract. Recent breakthroughs in process mining research make it possible to discover, analyze, and improve business processes based on event data. The growth of event data provides many opportunities but also imposes new challenges. Process mining is typically done for an isolated well-defined process in steady-state. However, the boundaries of a process may be fluid and there is a need to continuously view event data from different angles. This paper proposes the notion of process cubes where events and process models are organized using different dimensions. Each cell in the process cube corresponds to a set of events and can be used to discover a process model, to check conformance with respect to some process model, or to discover bottlenecks. The idea is related to the well-known OLAP (Online Analytical Processing) data cubes and associated operations such as slice, dice, roll-up, and drill-down. However, there are also significant differences because of the process-related nature of event data. For example, process discovery based on events is incomparable to computing the average or sum over a set of numerical values. Moreover, dimensions related to process instances (e.g. cases are split into gold and silver customers), subprocesses (e.g. acquisition versus delivery), organizational entities (e.g. backoffice versus frontoffice), and time (e.g., 2010, 2011, 2012, and 2013) are semantically different and it is challenging to slice, dice, roll-up, and drill-down process mining results efficiently.

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Towards Robust Conformance Checking

Cited by 90 publications

References 16 publications

Extracting Event Data from Databases to Unleash Process Mining

Extracting Event Data from Databases to Unleash Process Mining

Event-Based Real-Time Decomposed Conformance Analysis

Process Cubes: Slicing, Dicing, Rolling Up and Drilling Down Event Data for Process Mining

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