Data Flow-Oriented Process Mining to Support Security Audits

Stocker, Thomas F.

doi:10.1007/978-3-642-31875-7_18

Cited by 2 publications

(1 citation statement)

References 18 publications

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“…The lion's share of process mining research deals with the discovery of the control flow whereas the integration of the data perspective in process mining has generally not been investigated extensively in the academic community yet, apart from very few scientific publications. This observation is supported by Stocker [35] and de Leoni and van der Aalst [15]. De Leoni and van der Aalst use the data flow perspective to discover rules that explain why instances of the same process follow different execution paths.…”

Section: Related Workmentioning

confidence: 63%

Multilevel Process Mining for Financial Audits

Werner

Gehrke

2015

IEEE Trans. Serv. Comput.

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The relevance of business intelligence increases with the growing amount of recorded data. The research on business intelligence has led to a mature set of methods and tools that are used in many application areas, but they are almost absent in the auditing industry. Public accountants face the challenge to audit increasingly complex business processes that process huge amounts of transaction data. Process mining can be used as a business intelligence approach in the context of process audits to exploit this data. We introduce a process mining algorithm to improve such audits. Key requirements for this purpose are the reliability of the mining results, the integration of a data flow perspective and the ability to inspect data from the point of origin to the final output on the financial accounts. The presented algorithm integrates the control flow and data flow perspective. It operates on different abstraction levels to enable the auditor to follow the audit trail. The algorithm creates precise and fitting process models to prevent false negative and false positive audit results, accepts specific unlabeled event logs as input, and considers data relationships for inferring the control flow. It was evaluated by using extensive real world data.

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Section: Related Workmentioning

confidence: 63%