@inproceedings {conference-BBN10,
	title = {Partial Kernelization for Rank Aggregation: Theory and Experiments},
	booktitle = {Proceedings of the 5th International Symposium on Parameterized and Exact Computation (IPEC {\textquoteright}10)},
	series = {LNCS},
	volume = {6478},
	year = {2010},
	pages = {26-37},
	publisher = {Springer},
	organization = {Springer},
	type = {conference2010},
	abstract = {R<small>ANK </small>A<small>GGREGATION</small> is important  in many  areas ranging from web search over databases to bioinformatics. The underlying decision problem K<small>EMENY </small>S<small>CORE</small> is NP-complete even in case of four input rankings to be aggregated into a  "median ranking". We study efficient polynomial-time data reduction rules that  allow us to find <em>optimal</em> median rankings. On the theoretical side, we improve a result for a "partial problem kernel" from quadratic to linear size. On the practical side, we provide encouraging experimental results with data based on web search and sport competitions, e.g., computing optimal median rankings for real-world instances with more than 100 candidates within milliseconds.},
	keywords = {2010 - Conference articles},
	doi = {10.1007/978-3-642-17493-3_5},
	url = {http://fpt.akt.tu-berlin.de/publications/Partial_Kernelization_for_Rank_Aggregation__Theory_and_Experiments-IPEC.pdf},
	author = {Nadja Betzler and Robert Bredereck and Rolf Niedermeier}
}
