Paper Info
Title
Process-Oriented Iterative Multiple Alignment for Medical Process Mining
Abstract
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(N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> L <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) 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(NL <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) 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.
Year
DOI
Venue
2017
10.1109/ICDMW.2017.63
2017 IEEE International Conference on Data Mining Workshops (ICDMW)
Keywords
DocType
Volume
Trace Alignment,Process Mining,Workflow Analysis,Knowledge Discovery,Medical Healthcare Informatics
Conference
abs/1709.05440
ISSN
ISBN
Citations 
2375-9232
978-1-5386-3801-9
0
PageRank 
References 
Authors
0.34
9
5
Name
Order
Citations
PageRank
Shuhong Chen14910.21
Sen Yang2103.98
Moliang Zhou3163.55
Randall S. Burd412221.53
Ivan Marsic571691.96