@conference {17162,
	title = {Finding comparable temporal categorical records: A similarity measure with an interactive visualization},
	booktitle = {IEEE Symposium on Visual Analytics Science and Technology, 2009. VAST 2009},
	year = {2009},
	month = {2009/10/12/13},
	pages = {27 - 34},
	publisher = {IEEE},
	organization = {IEEE},
	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{\textquoteright}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.},
	keywords = {data visualisation, Educational institutions, Feedback, Information retrieval, interactive search tool, interactive systems, interactive visualization tool, large databases, M\&M Measure, Match \& Mismatch measure, Medical services, numerical time series, parameters customization, Particle measurements, Similan, similarity measure, Similarity Search, temporal categorical databases, Temporal Categorical Records, temporal databases, Testing, Time measurement, time series, transportation, usability, very large databases, visual databases, Visualization},
	isbn = {978-1-4244-5283-5},
	doi = {10.1109/VAST.2009.5332595},
	author = {Wongsuphasawat,K. and Shneiderman, Ben}
}
@conference {14346,
	title = {UbiGreen: investigating a mobile tool for tracking and supporting green transportation habits},
	booktitle = {Proceedings of the 27th international conference on Human factors in computing systems},
	series = {CHI {\textquoteright}09},
	year = {2009},
	month = {2009///},
	pages = {1043 - 1052},
	publisher = {ACM},
	organization = {ACM},
	address = {New York, NY, USA},
	keywords = {ambient displays, mobile phones, sensing, Sustainability, transportation, ubicomp},
	isbn = {978-1-60558-246-7},
	doi = {10.1145/1518701.1518861},
	url = {http://doi.acm.org/10.1145/1518701.1518861},
	author = {Jon Froehlich and Dillahunt,Tawanna and Klasnja,Predrag and Mankoff,Jennifer and Consolvo,Sunny and Harrison,Beverly and Landay,James A.}
}
@conference {15207,
	title = {Efficient minimum cost matching using quadrangle inequality},
	booktitle = {Foundations of Computer Science, 1992. Proceedings., 33rd Annual Symposium on},
	year = {1992},
	month = {1992/10//},
	pages = {583 - 592},
	abstract = {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},
	keywords = {algorithm;, array;, bipartite, bitonic, blue, complexity;, computational, cost, distance;, Euclidean, function;, geometry;, graph, graphs;, inequality;, linear, MATCHING, matching;, minimisation;, minimum, Monge, perfect, points;, polynomial, problem;, quadrangle, red, theory;, TIME, transportation, transportation;, weakly},
	doi = {10.1109/SFCS.1992.267793},
	author = {Aggarwal,A. and Bar-Noy,A. and Khuller, Samir and Kravets,D. and Schieber,B.}
}
