@techreport{TD:100484,
	att_abstract={{Many data sets contain temporal records over a long period of time;
each record is associated with a time stamp and describes some aspects
of a real-world entity at that particular time (e.g., author information
in DBLP). In such cases, we often wish to identify records
that describe the same entity over time and so be able to enable interesting
longitudinal data analysis. However, existing record linkage
techniques ignore the temporal information and can fall short
for temporal data.

This paper studies linking temporal records. First, we apply time
decay to capture the effect of elapsed time on entity value evolution.
Second, instead of comparing each pair of records locally, we
propose clustering methods that consider time order of the records
and make global decisions. Experimental results show that our algorithms
significantly outperform traditional linkage methods on
various temporal data sets.}},
	att_authors={xd0649, ds8961},
	att_categories={C_NSS.2},
	att_copyright={{VLDB Foundation}},
	att_copyright_notice={{The definitive version was published in Very Large Databases, 2011. {{, 2011-08-29}}
}},
	att_donotupload={true},
	att_private={false},
	att_projects={},
	att_tags={},
	att_techdoc={true},
	att_techdoc_key={TD:100484},
	att_url={},
	author={Xin Dong and Divesh Srivastava and Pei Li and Andrea Maurio},
	institution={{VLDB Conference}},
	month={August},
	title={{Linking Temporal Records}},
	year=2011,
}