Mining process models from workflow logs

Agrawal, Rakesh; Gunopulos, Dimitrios; Leymann, Frank

doi:10.1007/bfb0101003

Cited by 524 publications

(505 citation statements)

References 7 publications

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“…In this context, process mining [20,21], as a general method in business process analysis, is gaining increasing attention in analyzing CPs and other kinds of health-care processes [7,9,10]. The underlying idea of process mining is to discover CP models from careflow logs that record their executions.…”

Section: Related Workmentioning

confidence: 99%

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Latent Treatment Pattern Discovery for Clinical Processes

2013

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A clinical process is typically a mixture of various latent treatment patterns, implicitly indicating the likelihood of what clinical activities are essential/critical to the process. Discovering these hidden patterns is one of the most important components of clinical process analysis. What makes the pattern discovery problem complex is that these patterns are hidden in clinical processes, are composed of variable clinical activities, and often vary significantly between patient individuals. This paper employs Latent Dirichlet Allocation (LDA) to discover treatment patterns as a probabilistic combination of clinical activities. The probability distribution derived from LDA surmises the essential features of treatment patterns, and clinical processes can be accurately described by combining different classes of distributions. The presented approach has been implemented and evaluated via real-world data sets.

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Section: Related Workmentioning

confidence: 99%

“…These techniques are also called process mining [20][21][22][23]. Process mining techniques have been widely studied in the domain of business process management, which attempt to extract non-trivial and useful information from workflow logs [7,23].…”

mentioning

confidence: 99%

Latent Treatment Pattern Discovery for Clinical Processes

2013

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“…Both Agrawal et al [26] as well as Huang and Yang's [27] define noise as instances when executed activities were not collocated, timestamps of the activities are mistakenly recorded or exceptions that deviate from the normal processing order [27]. They furthermore use similar probabilistic reasoning and estimate how much error their process discovery algorithm would give due to the noise [27].…”

Section: Noise In Process Miningmentioning

confidence: 99%

Process Prediction in Noisy Data Sets: A Case Study in a Dutch Hospital

Spoel

Keulen

Amrit

2013

Lecture Notes in Business Information Processing

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Abstract. Predicting the amount of money that can be claimed is critical to the effective running of an Hospital. In this paper we describe a case study of a Dutch Hospital where we use process mining to predict the cash flow of the Hospital. In order to predict the cost of a treatment, we use different data mining techniques to predict the sequence of treatments administered, the duration and the final "care product" or diagnosis of the patient. While performing the data analysis we encountered three specific kinds of noise that we call sequence noise, human noise and duration noise. Studies in the past have discussed ways to reduce the noise in process data. However, it is not very clear what effect the noise has to different kinds of process analysis. In this paper we describe the combined effect of sequence noise, human noise and duration noise on the analysis of process data, by comparing the performance of several mining techniques on the data.

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“…Process mining techniques [19,21,1,3,4] extract knowledge about business processes by analyzing event logs. So far, the main focus has been on the analysis of processes inside single organizations.…”

Section: Related Workmentioning

confidence: 99%

Mining Inter-organizational Business Process Models from EDI Messages: A Case Study from the Automotive Sector

Engel

Aalst

Zapletal

et al. 2012

Notes on Numerical Fluid Mechanics and Multidisciplinary Design

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Abstract. Traditional standards for Electronic Data Interchange (EDI), such as EDIFACT and ANSI X12, have been employed in Business-toBusiness (B2B) e-commerce for decades. Due to their wide industry coverage and long-standing establishment, they will presumably continue to play an important role for some time. EDI systems are typically not "process-aware", i.e., messages are standardized but processes simply "emerge". However, to improve performance and to enhance the control, it is important to understand and analyze the "real" processes supported by these systems. In the case study presented in this paper we uncover the inter-organizational business processes of an automotive supplier company by analyzing the EDIFACT messages that it receives from its business partners. We start by transforming a set of observed messages to an event log, which requires that the individual messages are correlated to process instances. Thereby, we make use of the specific structure of EDIFACT messages. Then we apply process mining techniques to uncover the inter-organizational business processes. Our results show that inter-organizational business process models can be derived by analyzing EDI messages that are exchanged in a network of organizations.

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Mining process models from workflow logs

Cited by 524 publications

References 7 publications

Latent Treatment Pattern Discovery for Clinical Processes

Latent Treatment Pattern Discovery for Clinical Processes

Process Prediction in Noisy Data Sets: A Case Study in a Dutch Hospital

Mining Inter-organizational Business Process Models from EDI Messages: A Case Study from the Automotive Sector

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