Process Mining: Fuzzy Clustering and Performance Visualization

Dongen, van Bf Boudewijn; Adriansyah, A Arya

doi:10.1007/978-3-642-12186-9_15

Cited by 37 publications

(29 citation statements)

References 14 publications

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“…Other studies overlay the performance measures on top of a process model by replaying the log on the process model [3,32] and calculating aggregate performance measures for each element in the process model during the replay. Techniques for enhancing the quality of performance analysis based on log replay have been proposed [9]. A related technique supported by contemporary performance analysis tools is log animation.…”

Section: Approaches and Tools (Rq3)mentioning

confidence: 99%

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A Comparative Evaluation of Log-Based Process Performance Analysis Techniques

Milani

Maggi

2018

Business Information Systems

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Process mining has gained traction over the past decade and an impressive body of research has resulted in the introduction of a variety of process mining approaches measuring process performance. Having this set of techniques available, organizations might find it difficult to identify which approach is best suited considering context, performance indicator, and data availability. In light of this challenge, this paper aims at introducing a framework for categorizing and selecting performance analysis approaches based on existing research. We start from a systematic literature review for identifying the existing works discussing how to measure process performance based on information retrieved from event logs. Then, the proposed framework is built starting from the information retrieved from these studies taking into consideration different aspects of performance analysis. accessibility to open source tools has never been easier, in particular for data driven analysis of business processes. Therefore, in a digital era, businesses cannot hope to survive with manually driven methods. The process analysis must also be digitally transformed by tapping into data-driven analysis methods.One group of techniques for data driven performance analysis uses event logs of processes to assess performance. Indeed, nowadays, business processes are often supported by IT systems that log their execution. For instance, an order-to-invoice process might include activities such as register, validate, approve, fill order and send invoice. Each order has a unique id and every activity is recorded in the event log with information about the time when it was executed (timestamp) and other additional data such as the resource (person or device) that executed the activity. As such, the process is inherently captured in the log. With process mining techniques [2], the performance of such processes can be assessed and analyzed in great detail based on event logs.The body of research and tools within the field of process mining has grown significantly during this decade. However, the availability of tools and approaches developed for specific aspects of process performance does not make it easier for businesses to employ them. In fact, it poses a challenge. There is no way for businesses to easily get an overview of what performance indicators can be measured, what input data is required for such analysis, or what industry specific implementations are available. In light of this context, we propose a framework for the selection of log-based performance analysis techniques. We do so by conducting a systematic literature review to identify the body of existing work. We analyze the results and focus on identifying existing process performance indicators, required input data, and approaches available. Based on the results, we build a framework for the selection of suitable performance analysis approaches.The structure of this paper is as follows. Section 2 summarizes the research protocol for the systematic survey. In Section 3, the research que...

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Section: Approaches and Tools (Rq3)mentioning

confidence: 99%

“…A related technique supported by contemporary performance analysis tools is log animation. Log animation displays in a movielike fashion how cases circulate through the process model over time [9,13,8].…”

Section: Approaches and Tools (Rq3)mentioning

confidence: 99%

A Comparative Evaluation of Log-Based Process Performance Analysis Techniques

Milani

Maggi

2018

Business Information Systems

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“…Early process mining work [25], [27] used simple directed graphs which did not specify the types of splits and joins. More recently, Simple Precedence Diagrams [3] are similar, loosely capturing process structure to semi-formally describe processes. Nodes describe activities or groups of activities.…”

Section: A Business Processes and Their Representationsmentioning

confidence: 99%

“…genetic [32], [33] and region mining [18], [19]. Recent approaches aim to mine complex or noisy processes using clustering and abstraction at the level of traces [9]- [11], or activities [3], [12], [13]. Other work includes artificially generating negative examples to improve learning [34], and mining from logs lacking case IDs [20] to identify process instances.…”

Section: Process Miningmentioning

confidence: 99%

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A Framework for the Analysis of Process Mining Algorithms

Weber

Bordbar

Tiňo

2013

IEEE Trans. Syst. Man Cybern, Syst.

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Abstract-There are many process mining algorithms and representations, making it difficult to choose which algorithm to use or compare results. Process mining is essentially a machine learning task, but little work as been done on systematically analysing algorithms to understand their fundamental properties, such as how much data is needed for confidence in mining.We propose a framework for analysing process mining algorithms. Processes are viewed as distributions over traces of activities, and mining algorithms as learning these distributions. We use probabilistic automata as a unifying representation to which other representation languages can be converted.We present an analysis of the Alpha algorithm under this framework, and experimental results which show that from the sub-structures in a model and behaviour of the algorithm, the amount of data needed for mining can be predicted. This allows efficient use of data and quantification of the confidence which can be placed in the results.

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