Process Mining in Large

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Table of Contents

Application
Focus Perspectives
Types of Process Mining
Methods and Algorithms
Key Findings
Public Availability of the Dataset
Work Cited

Application

The paper implemented, tested, and evaluated the results of the advancements it proposed in the Dutch Academic Hospital. There, the author analyzed the quality and performance of fuzzy map mining, the formation of sub-longs with abstractions, etc. The logs of several departments in this clinic were used such as radiology, pathology, and general lab. Concept drift was analyzed in the case of a Dutch municipality.

Focus Perspectives

The author’s fundamental work was concentrated on the holistic description and evaluation of the process mining advancements that were proposed. Therefore, Bose analyzed them from several perspectives including control-flow, data, organizational, and performance perspectives. Control flow was described in section 11.1.2. There the author examines the application of the perspective to different departments of Dutch Academic Hospital, where the workflow of treatment and testing procedures were logged and then transformed into a new format. The organizational perspective was imbued with an extensible event stream (XES) that captures and stores data related to three groups such as resource, role, and group. The data perspective appears to be one of the central in the paper. The log modifications the author developed directly influence the way the information is processed, stored, and perceived. Bose also views her innovation from a performance perspective. She analyzes how well her algorithm restructures the existing logs into shorter and more usable versions.

Types of Process Mining

First of all, the author used process discovery. She decided to take a new approach to hierarchical process discovery. A context analysis was used in the pre-processing stage to determine and select valuable information and abstractions. The author compares this process with cartography. Conformance checking or diagnostics (a broader concept the author uses) is implemented. The diagnostic function is partially presented by trace alignment that, according to the author, allows identifying violations and deviations (Bose 19). The enhancement process consists in implementing data processing techniques and strategies including pre-processing, abstracting, concept drift, and other innovations.

Methods and Algorithms

Bose uses a variety of methods and algorithms including vector-based and syntactic approaches for trace clustering. Both approaches were tested and discussed. Above trace clustering, the author uses an innovational event log transformation algorithm such as fuzzy mapping. As for methods, the author uses case studies, experiments, and modeling to determine the viability of her hypotheses.

Key Findings

Among the key findings of the author, one may identify using preprocessing based on creating abstractions of events that assist in simplification of event logs. Bose also provided a solution for the heterogeneity of the logs that consists in implementing context-based techniques that form homogenous subsets of events. In the field of diagnostics, Bose proposed comparing event logs with process maps while using the gathered metrics. Trace alignment became a part of the diagnostic solution. The proposed event log optimization methods were packaged into a ProM framework, tested, and proved effective in several organizations.

Public Availability of the Dataset

The datasets used to belong mostly to organizations that granted the author access to them for the purposes of her research. It could be assumed that other researchers would want to experiment further on various techniques that require similar datasets. Therefore, for the sake of promoting research and innovation in the sphere of event logging and process mining, the datasets can and even should be made public. In addition, the organizations such as the hospital and Dutch Municipalities are public organizations and could presumably disclose non-personal data. A certain level of discretion, however, is advised.

Work Cited

Bose, Jagadeesh. Process Mining in the Large: Preprocessing, Discovery, and Diagnostics. Dissertation, Technische Universiteit Eindhoven, 2012. TUE, 2012.

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