8 research outputs found
Process-oriented Iterative Multiple Alignment for Medical Process Mining
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.Comment: accepted at ICDMW 201
Evaluation of Trace Alignment Quality and its Application in Medical Process Mining
Trace alignment algorithms have been used in process mining for discovering
the consensus treatment procedures and process deviations. Different alignment
algorithms, however, may produce very different results. No widely-adopted
method exists for evaluating the results of trace alignment. Existing
reference-free evaluation methods cannot adequately and comprehensively assess
the alignment quality. We analyzed and compared the existing evaluation
methods, identifying their limitations, and introduced improvements in two
reference-free evaluation methods. Our approach assesses the alignment result
globally instead of locally, and therefore helps the algorithm to optimize
overall alignment quality. We also introduced a novel metric to measure the
alignment complexity, which can be used as a constraint on alignment algorithm
optimization. We tested our evaluation methods on a trauma resuscitation
dataset and provided the medical explanation of the activities and patterns
identified as deviations using our proposed evaluation methods.Comment: 10 pages, 6 figures and 5 table
Sub-Sync: automatic synchronization of subtitles in the broadcasting of true live programs in spanish
Individuals With Sensory Impairment (Hearing Or Visual) Encounter Serious Communication Barriers Within Society And The World Around Them. These Barriers Hinder The Communication Process And Make Access To Information An Obstacle They Must Overcome On A Daily Basis. In This Context, One Of The Most Common Complaints Made By The Television (Tv) Users With Sensory Impairment Is The Lack Of Synchronism Between Audio And Subtitles In Some Types Of Programs. In Addition, Synchronization Remains One Of The Most Significant Factors In Audience Perception Of Quality In Live-Originated Tv Subtitles For The Deaf And Hard Of Hearing. This Paper Introduces The Sub-Sync Framework Intended For Use In Automatic Synchronization Of Audio-Visual Contents And Subtitles, Taking Advantage Of Current Well-Known Techniques Used In Symbol Sequences Alignment. In This Particular Case, These Symbol Sequences Are The Subtitles Produced By The Broadcaster Subtitling System And The Word Flow Generated By An Automatic Speech Recognizing The Procedure. The Goal Of Sub-Sync Is To Address The Lack Of Synchronism That Occurs In The Subtitles When Produced During The Broadcast Of Live Tv Programs Or Other Programs That Have Some Improvised Parts. Furthermore, It Also Aims To Resolve The Problematic Interphase Of Synchronized And Unsynchronized Parts Of Mixed Type Programs. In Addition, The Framework Is Able To Synchronize The Subtitles Even When They Do Not Correspond Literally To The Original Audio And/Or The Audio Cannot Be Completely Transcribed By An Automatic Process. Sub-Sync Has Been Successfully Tested In Different Live Broadcasts, Including Mixed Programs, In Which The Synchronized Parts (Recorded, Scripted) Are Interspersed With Desynchronized (Improvised) Ones
Duration-aware alignment of process traces
© Springer International Publishing Switzerland 2016. Objective: To develop an algorithm for aligning process traces that considers activity duration during alignment and helps derive data-driven insights from workflow data. Methods: We developed a duration-aware trace alignment algorithm as part of a Java application that provides visualization of the alignment. The relative weight of the activity type vs. activity duration during the alignment is an adjustable parameter. We evaluated proportional and logarithmic weights for activity duration. Results: We used duration-aware trace alignment on two real-world medical datasets. Compared with existing context-based alignment algorithm, our results show that duration-aware alignment algorithm achieves higher alignment accuracy and provides more intuitive insights for deviation detection and data visualization. Conclusion: Duration-aware trace alignment improves upon an existing trace alignment approach and offers better alignment accuracy and visualization
Duration-aware alignment of process traces
© Springer International Publishing Switzerland 2016. Objective: To develop an algorithm for aligning process traces that considers activity duration during alignment and helps derive data-driven insights from workflow data. Methods: We developed a duration-aware trace alignment algorithm as part of a Java application that provides visualization of the alignment. The relative weight of the activity type vs. activity duration during the alignment is an adjustable parameter. We evaluated proportional and logarithmic weights for activity duration. Results: We used duration-aware trace alignment on two real-world medical datasets. Compared with existing context-based alignment algorithm, our results show that duration-aware alignment algorithm achieves higher alignment accuracy and provides more intuitive insights for deviation detection and data visualization. Conclusion: Duration-aware trace alignment improves upon an existing trace alignment approach and offers better alignment accuracy and visualization