@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}
}
@article {16864,
	title = {Maintenance of K-nn and spatial join queries on continuously moving points},
	journal = {ACM Trans. Database Syst.},
	volume = {31},
	year = {2006},
	month = {2006/06//},
	pages = {485 - 536},
	abstract = {Cars, aircraft, mobile cell phones, ships, tanks, and mobile robots all have the common property that they are moving objects. A kinematic representation can be used to describe the location of these objects as a function of time. For example, a moving point can be represented by the function p(t) = \&OV0489;0 + (t - t0)\&OV0505;, where \&OV0489;0 is the start location, t0 is the start time, and \&OV0505; is its velocity vector. Instead of storing the location of the object at a given time in a database, the coefficients of the function are stored. When an object{\textquoteright}s behavior changes enough so that the function describing its location is no longer accurate, the function coefficients for the object are updated. Because the location of each object is represented as a function of time, spatial query results can change even when no transactions update the database. We present efficient algorithms to maintain k-nearest neighbor, and spatial join queries in this domain as time advances and updates occur. We assume no previous knowledge of what the updates will be before they occur. We experimentally compare these new algorithms with more straight forward adaptations of previous work to support updates. Experiments are conducted using synthetic uniformly distributed data, and real aircraft flight data. The primary metric of comparison is the number of I/O disk accesses needed to maintain the query results and the supporting data structures.},
	keywords = {<i>k</i>-nearest neighbor, continuously moving objects, materialized view maintenance, Moving object databases, Spatial join, temporal databases},
	isbn = {0362-5915},
	doi = {10.1145/1138394.1138396},
	url = {http://doi.acm.org/10.1145/1138394.1138396},
	author = {Iwerks,Glenn S. and Samet, Hanan and Smith,Kenneth P.}
}
