Constructs Competition Miner: Process Control-Flow Discovery of BP-Domain Constructs

Redlich, David; Molka, Thomas; Gilani, Wasif; Blair, Gordon S.; Rashid, Awais

doi:10.1007/978-3-319-10172-9_9

Cited by 20 publications

(23 citation statements)

References 11 publications

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“…The extraction of a business process model from a given event log without the usage of any a-priori information is addressed by Process Discovery algorithms [19], e.g. [14,16,18,20].…”

Section: Dynamic Process Discovery For Digital Preservationmentioning

confidence: 99%

“…The process monitor in the TIMBUS solution has been implemented with a modified Constructs Competition Miner (CCM) originally proposed in [16]. The original CCM is based on two fundamental steps: (1) Footprint Creation, in which the footprint consisting of information on the global relations between any two elements of a set of activities is calculated from a given event log.…”

Section: Dynamic Constructs Competition Minermentioning

confidence: 99%

“…(2) Footprint Interpretation, in which the footprint is interpreted to a business process model construct. The CCM is technically able to identify and build a business process model based on the following constructs: Sequence, Choice, Parallelism, Loop, Loop over Sequence, Loop over Choice, Loop over Parallelism and constructs for single activities [16].…”

Section: Dynamic Constructs Competition Minermentioning

confidence: 99%

“…The final result of the algorithm is a business process model representing the behaviour recorded in the log. For more details on the functionality as well as the footprint and business process model of the original CCM please see [16].…”

Section: Dynamic Constructs Competition Minermentioning

confidence: 99%

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Evaluation of the Dynamic Construct Competition Miner for an eHealth System

Redlich

Galushka²,

Molka³

et al. 2015

Business Information Systems

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Abstract. Business processes of some domains are highly dynamic and increasingly complex due to their dependencies on a multitude of services provided by various providers. The quality of services directly impacts the business process's efficiency. A first prerequisite for any optimization initiative requires a better understanding of the deployed business processes. However, the business processes are either not documented at all or are only poorly documented. Since the actual behaviour of the business processes and underlying services can change over time it is required to detect the dynamically changing behaviour in order to carry out correct analyses. This paper presents and evaluates the integration of the Dynamic Construct Competition Miner (DCCM) as process monitor in the TIMBUS architecture. The DCCM discovers business processes and recognizes changes directly from an event stream at run-time. The evaluation is carried out in the context of an industrial use-case from the eHealth domain. We will describe the key aspects of the use-case and the DCCM as well as present the relevant evaluation results.

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“…The extraction of a business process model from a given event log without the usage of any a-priori information is addressed by Process Discovery algorithms [19], e.g. [14,16,18,20].…”

Section: Dynamic Process Discovery For Digital Preservationmentioning

confidence: 99%

Section: Dynamic Constructs Competition Minermentioning

confidence: 99%

Section: Dynamic Constructs Competition Minermentioning

confidence: 99%

Section: Dynamic Constructs Competition Minermentioning

confidence: 99%

See 2 more Smart Citations

Evaluation of the Dynamic Construct Competition Miner for an eHealth System

Redlich

Galushka²,

Molka³

et al. 2015

Business Information Systems

Self Cite

View full text Add to dashboard Cite

show abstract

“…The main advantage of using the prefix trees is the reduced processing time and usage of memory. In [48], Redlich et al design an event stream-based variant of the CCM algorithm [49]. The authors identify the need to compute dynamic footprint information based on the event stream, which can be seen as the abstract representation used by CCM.…”

Section: Related Workmentioning

confidence: 99%

Discovering Petri Nets from Event Logs

Aalst

2013

Transactions on Petri Nets and Other Models of Concurrency VII

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The aim of process discovery, originating from the area of process mining, is to discover a process model based on business process execution data. A majority of process discovery techniques relies on an event log as an input. An event log is a static source of historical data capturing the execution of a business process. In this paper, we focus on process discovery relying on online streams of business process execution events. Learning process models from event streams poses both challenges and opportunities, i.e. we need to handle unlimited amounts of data using finite memory and, preferably, constant time. We propose a generic architecture that allows for adopting several classes of existing process discovery techniques in context of event streams. Moreover, we provide several instantiations of the architecture, accompanied by implementations in the process mining toolkit ProM (http:// promtools.org). Using these instantiations, we evaluate several dimensions of stream-based process discovery. The evaluation shows that the proposed architecture allows us to lift process discovery to the streaming domain.

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Process Mining

Leemans

2022

Lecture Notes in Business Information Processing

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Constructs Competition Miner: Process Control-Flow Discovery of BP-Domain Constructs

Cited by 20 publications

References 11 publications

Evaluation of the Dynamic Construct Competition Miner for an eHealth System

Evaluation of the Dynamic Construct Competition Miner for an eHealth System

Discovering Petri Nets from Event Logs

Process Mining

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