Title

Process-Oriented Iterative Multiple Alignment for Medical Process Mining

Document Type

Conference Proceeding

Publication Date

12-15-2017

Journal

IEEE International Conference on Data Mining Workshops, ICDMW

Volume

2017-November

DOI

10.1109/ICDMW.2017.63

Keywords

Knowledge Discovery; Medical Healthcare Informatics; Process Mining; Trace Alignment; Workflow Analysis

Abstract

© 2017 IEEE. Adapted from biological sequence alignment, trace alignment is a process mining technique used to visualize and analyze workflow data. Any analysis done with this method, however, is affected by the alignment quality. The best existing trace alignment techniques use progressive guide-trees to heuristically approximate the optimal alignment in O(N2L2) time. These algorithms are heavily dependent on the selected guide-tree metric, often return sum-of-pairs-score-reducing errors that interfere with interpretation, and are computationally intensive for large datasets. To alleviate these issues, we propose process-oriented iterative multiple alignment (PIMA), which contains specialized optimizations to better handle workflow data. We demonstrate that PIMA is a flexible framework capable of achieving better sum-of-pairs score than existing trace alignment algorithms in only O(NL2) time. We applied PIMA to analyzing medical workflow data, showing how iterative alignment can better represent the data and facilitate the extraction of insights from data visualization.

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