Temporal summaries: supporting temporal categorical searching, aggregation and comparison.
When analyzing thousands of event histories, analysts often want to see the events as an aggregate to detect insights and generate new hypotheses about the data. An analysis tool must emphasize both the prevalence and the temporal ordering of these events. Additionally, the analysis tool must also support flexible comparisons to allow analysts to gather visual evidence. In a previous work, we introduced align, rank, and filter (ARF) to accentuate temporal ordering. In this paper, we present temporal summaries, an interactive visualization technique that highlights the prevalence of event occurrences. Temporal summaries dynamically aggregate events in multiple granularities (year, month, week, day, hour, etc.) for the purpose of spotting trends over time and comparing several groups of records. They provide affordances for analysts to perform temporal range filters. We demonstrate the applicability of this approach in two extensive case studies with analysts who applied temporal summaries to search, filter, and look for patterns in electronic health records and academic records.
Wang
Taowei David
TD
Human-Computer Interaction Lab and Department of Computer Science, University of Maryland at College Park, USA. tw7@cs.umd.edu
Plaisant
Catherine
C
Shneiderman
Ben
B
Spring
Neil
N
Roseman
David
D
Marchand
Greg
G
Mukherjee
Vikramjit
V
Smith
Mark
M
D019295:Computational Biology / Q000379:methods*; D003196:Computer Graphics; D016208:Databases, Factual; D006493:Heparin / Q000009:adverse effects; D006801:Humans; D016347:Medical Records Systems, Computerized; D010363:Pattern Recognition, Automated / Q000379:methods*; D013921:Thrombocytopenia / Q000139:chemically induced; D013997:Time Factors
D006493:Heparin