Auditing 2.0: Using Process Mining to Support Tomorrow's Auditor

Aalst, Wil M. P. van der; Hee, K.M. van; Werf, Jan Martijn E. M. van der; Verdonk, Marc

doi:10.1109/mc.2010.61

Cited by 130 publications

(63 citation statements)

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“…In [17], a case study shows the applicability of process discovery in this context. -Auditing: The role of an auditor may change in the presence of process mining techniques, since many checkings can be done automatically and without the restriction to be applied to a small set of records [18]. We informally describe with a toy example the problem of process discovery and the main differences between additive and restrictive models.…”

Section: Process Discovery: Applications and A Motivating Examplementioning

confidence: 99%

Encoding process discovery problems in SMT

Solé¹,

Carmona

2016

Softw Syst Model

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Information systems, which are responsible for driving many processes in our lives (health care, the web, municipalities, commerce and business, among others), store information in the form of logs which is often left unused. Process mining, a discipline in between data mining and software engineering, proposes tailored algorithms to exploit the information stored in a log, in order to reason about the processes underlying an information system. A key challenge in process mining is discovery: Given a log, derive a formal process model that can be used afterward for a formal analysis. In this paper, we provide a general approach based on satisfiability modulo theories (SMT) as a solution for this challenging problem. By encoding the problem into the logical/arithmetic domains and using modern SMT engines, it is shown how two separate families of process models can be discovered. The theory of this paper is accompanied with a tool, and experimental results witness the significance of this novel view of the process discovery problem.

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Section: Process Discovery: Applications and A Motivating Examplementioning

confidence: 99%

Encoding process discovery problems in SMT

Solé¹,

Carmona

2016

Softw Syst Model

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“…The set of process discovery and visualization techniques enables the acquisition of a full and precise insight in the real business process, summarized in one visual. Therefore, process visualization can be considered as the ideal technique for process exploration and thus a first step in a compliance analysis [44]. In a compliance checking setting the Alpha Miner [2] the Alpha Miner ??…”

Section: Process-oriented Auditing Compliance and Conformance Checkimentioning

confidence: 99%

On the gap between reality and registration: a business event analysis classification framework

et al. 2016

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This paper presents a business event analysis classification framework, based on five business criteria. As a result, we are able to distinguish thirteen event types distributed over four categories, i.e. truthful, invisible, false and unobserved events. Currently, several of these event types are not commonly dealt with in business process management (BPM) and analytics (BPA) research. Based on the proposed framework we situate the different BPM and BPA research areas and indicate the potential issues for each field. A business case is elaborated to demonstrate the relevance of the event classification framework.

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“…These works use the concepts of (i) fitness [31,29], which assess the portion size of the log that can be correctly played by the model, that is, the degree of completeness; (ii) behavioral appropriateness [29,35], which measures the degree of predictability to support the execution of unseen trace in the log; (iii) structural appropriateness [29,35], which assess the complexity of the process model; and (iv) appropriateness [8], which was suggested to be a balance between structural and behavioral appropriateness.…”

Section: Process Miningmentioning

confidence: 99%

Algorithms for anomaly detection of traces in logs of process aware information systems

Bezerra

Wainer

2013

Information Systems

102

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a b s t r a c tThis paper discusses four algorithms for detecting anomalies in logs of process aware systems. One of the algorithms only marks as potential anomalies traces that are infrequent in the log. The other three algorithms: threshold, iterative and sampling are based on mining a process model from the log, or a subset of it. The algorithms were evaluated on a set of 1500 artificial logs, with different profiles on the number of anomalous traces and the number of times each anomalous traces was present in the log. The sampling algorithm proved to be the most effective solution. We also applied the algorithm to a real log, and compared the resulting detected anomalous traces with the ones detected by a different procedure that relies on manual choices.

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Auditing 2.0: Using Process Mining to Support Tomorrow's Auditor

Cited by 130 publications

References 0 publications

Encoding process discovery problems in SMT

Encoding process discovery problems in SMT

On the gap between reality and registration: a business event analysis classification framework

Algorithms for anomaly detection of traces in logs of process aware information systems

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