Finding comparable temporal categorical records: A similarity measure with an interactive visualization

An increasing number of temporal categorical databases are being collected: Electronic Health Records in healthcare organizations, traffic incident logs in transportation systems, or student records in universities. Finding similar records within these large databases requires effective similarity m...

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Bibliographic Details
Published in:2009 IEEE Symposium on Visual Analytics Science and Technology pp. 27 - 34
Main Authors: Wongsuphasawat, K., Shneiderman, B.
Format: Conference Proceeding
Language:English
Published: IEEE 01.10.2009
Subjects:
Educational institutions
Feedback
M&M Measure
Medical services
Particle measurements
Similan
Similarity Search
Temporal Categorical Records
Testing
Time measurement
Transportation
Usability
Visual databases
Visualization
ISBN:9781424452835, 142445283X
Online Access:Get full text
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Abstract An increasing number of temporal categorical databases are being collected: Electronic Health Records in healthcare organizations, traffic incident logs in transportation systems, or student records in universities. Finding similar records within these large databases requires effective similarity measures that capture the searcher's intent. Many similarity measures exist for numerical time series, but temporal categorical records are different. We propose a temporal categorical similarity measure, the M&M (Match & Mismatch) measure, which is based on the concept of aligning records by sentinel events, then matching events between the target and the compared records. The M&M measure combines the time differences between pairs of events and the number of mismatches. To accom-modate customization of parameters in the M&M measure and results interpretation, we implemented Similan, an interactive search and visualization tool for temporal categorical records. A usability study with 8 participants demonstrated that Similan was easy to learn and enabled them to find similar records, but users had difficulty understanding the M&M measure. The usability study feedback, led to an improved version with a continuous timeline, which was tested in a pilot study with 5 participants.
AbstractList An increasing number of temporal categorical databases are being collected: Electronic Health Records in healthcare organizations, traffic incident logs in transportation systems, or student records in universities. Finding similar records within these large databases requires effective similarity measures that capture the searcher's intent. Many similarity measures exist for numerical time series, but temporal categorical records are different. We propose a temporal categorical similarity measure, the M&M (Match & Mismatch) measure, which is based on the concept of aligning records by sentinel events, then matching events between the target and the compared records. The M&M measure combines the time differences between pairs of events and the number of mismatches. To accom-modate customization of parameters in the M&M measure and results interpretation, we implemented Similan, an interactive search and visualization tool for temporal categorical records. A usability study with 8 participants demonstrated that Similan was easy to learn and enabled them to find similar records, but users had difficulty understanding the M&M measure. The usability study feedback, led to an improved version with a continuous timeline, which was tested in a pilot study with 5 participants.
Author Shneiderman, B.
Wongsuphasawat, K.
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  organization: Dept. of Comput. Sci. & Human-Comput. Interaction Lab., Univ. of Maryland, College Park, MD, USA
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Snippet An increasing number of temporal categorical databases are being collected: Electronic Health Records in healthcare organizations, traffic incident logs in...
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StartPage 27
SubjectTerms Educational institutions
Feedback
M&M Measure
Medical services
Particle measurements
Similan
Similarity Search
Temporal Categorical Records
Testing
Time measurement
Transportation
Usability
Visual databases
Visualization
Title Finding comparable temporal categorical records: A similarity measure with an interactive visualization
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