TY - CONF T1 - Finding comparable temporal categorical records: A similarity measure with an interactive visualization T2 - IEEE Symposium on Visual Analytics Science and Technology, 2009. VAST 2009 Y1 - 2009 A1 - Wongsuphasawat,K. A1 - Shneiderman, Ben KW - data visualisation KW - Educational institutions KW - Feedback KW - Information retrieval KW - interactive search tool KW - interactive systems KW - interactive visualization tool KW - large databases KW - M&M Measure KW - Match & Mismatch measure KW - Medical services KW - numerical time series KW - parameters customization KW - Particle measurements KW - Similan KW - similarity measure KW - Similarity Search KW - temporal categorical databases KW - Temporal Categorical Records KW - temporal databases KW - Testing KW - Time measurement KW - time series KW - transportation KW - usability KW - very large databases KW - visual databases KW - Visualization AB - 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. JA - IEEE Symposium on Visual Analytics Science and Technology, 2009. VAST 2009 PB - IEEE SN - 978-1-4244-5283-5 M3 - 10.1109/VAST.2009.5332595 ER - TY - CONF T1 - UbiGreen: investigating a mobile tool for tracking and supporting green transportation habits T2 - Proceedings of the 27th international conference on Human factors in computing systems Y1 - 2009 A1 - Jon Froehlich A1 - Dillahunt,Tawanna A1 - Klasnja,Predrag A1 - Mankoff,Jennifer A1 - Consolvo,Sunny A1 - Harrison,Beverly A1 - Landay,James A. KW - ambient displays KW - mobile phones KW - sensing KW - Sustainability KW - transportation KW - ubicomp JA - Proceedings of the 27th international conference on Human factors in computing systems T3 - CHI '09 PB - ACM CY - New York, NY, USA SN - 978-1-60558-246-7 UR - http://doi.acm.org/10.1145/1518701.1518861 M3 - 10.1145/1518701.1518861 ER - TY - CONF T1 - Efficient minimum cost matching using quadrangle inequality T2 - Foundations of Computer Science, 1992. Proceedings., 33rd Annual Symposium on Y1 - 1992 A1 - Aggarwal,A. A1 - Bar-Noy,A. A1 - Khuller, Samir A1 - Kravets,D. A1 - Schieber,B. KW - algorithm; KW - array; KW - bipartite KW - bitonic KW - blue KW - complexity; KW - computational KW - cost KW - distance; KW - Euclidean KW - function; KW - geometry; KW - graph KW - graphs; KW - inequality; KW - linear KW - MATCHING KW - matching; KW - minimisation; KW - minimum KW - Monge KW - perfect KW - points; KW - polynomial KW - problem; KW - quadrangle KW - red KW - theory; KW - TIME KW - transportation KW - transportation; KW - weakly AB - The authors present efficient algorithms for finding a minimum cost perfect matching, and for solving the transportation problem in bipartite graphs, G = (Red cup; Blue, Red times; Blue), where |Red| = n, |Blue| = m, n les; m, and the cost function obeys the quadrangle inequality. The first results assume that all the red points and all the blue points lie on a curve that is homeomorphic to either a line or a circle and the cost function is given by the Euclidean distance along the curve. They present a linear time algorithm for the matching problem. They generalize the method to solve the corresponding transportation problem in O((m+n)log(m+n)) time. The next result is an O(n log m) algorithm for minimum cost matching when the cost array is a bitonic Monge array. An example of this is when the red points lie on one straight line and the blue points lie on another straight line (that is not necessarily parallel to the first one). Finally, they provide a weakly polynomial algorithm for the transportation problem in which the associated cost array is a bitonic Monge array JA - Foundations of Computer Science, 1992. Proceedings., 33rd Annual Symposium on M3 - 10.1109/SFCS.1992.267793 ER -