@conference {13766,
	title = {Evaluating visual and statistical exploration of scientific literature networks},
	booktitle = {2011 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC)},
	year = {2011},
	month = {2011/09/18/22},
	pages = {217 - 224},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Action Science Explorer (ASE) is a tool designed to support users in rapidly generating readily consumable summaries of academic literature. It uses citation network visualization, ranking and filtering papers by network statistics, and automatic clustering and summarization techniques. We describe how early formative evaluations of ASE led to a mature system evaluation, consisting of an in-depth empirical evaluation with four domain experts. The evaluation tasks were of two types: predefined tasks to test system performance in common scenarios, and user-defined tasks to test the system{\textquoteright}s usefulness for custom exploration goals. The primary contribution of this paper is a validation of the ASE design and recommendations to provide: easy-to-understand metrics for ranking and filtering documents, user control over which document sets to explore, and overviews of the document set in coordinated views along with details-on-demand of specific papers. We contribute a taxonomy of features for literature search and exploration tools and describe exploration goals identified by our participants.},
	keywords = {abstracting, academic literature, action science explorer, automatic clustering, citation analysis, citation network visualization, Communities, Context, custom exploration goal, Data visualization, Databases, Document filtering, document handling, document ranking, easy-to-understand metrics, empirical evaluation, Google, Graphical user interfaces, Information filtering, Information Visualization, Libraries, literature exploration, network statistics, paper filtering, paper ranking, scientific literature network, statistical exploration, summarization technique, user-defined tasks, visual exploration, Visualization},
	isbn = {978-1-4577-1246-3},
	doi = {10.1109/VLHCC.2011.6070403},
	author = {Gove,R. and Dunne,C. and Shneiderman, Ben and Klavans,J. and Dorr, Bonnie J}
}
@article {17617,
	title = {Low discrepancy sets yield approximate min-wise independent permutation families},
	journal = {Information Processing Letters},
	volume = {73},
	year = {2000},
	month = {2000/01/31/},
	pages = {29 - 32},
	abstract = {Motivated by a problem of filtering near-duplicate Web documents, Broder, Charikar, Frieze and Mitzenmacher defined the following notion of ϵ-approximate min-wise independent permutation families. A multiset F of permutations of {0,1,{\textellipsis},n-1} is such a family if for all K⫅{0,1,{\textellipsis},n-1} and any x∈K, a permutation π chosen uniformly at random from F satisfies |Pr[min{π(K)}=π(x)]-1|K||<=ϵ|K|. We show connections of such families with low discrepancy sets for geometric rectangles, and give explicit constructions of such families F of size nO(logn) for ϵ=1/nΘ(1), improving upon the previously best-known bound of Indyk. We also present polynomial-size constructions when the min-wise condition is required only for |K|<=2O(log2/3n), with ϵ>=2-O(log2/3n).},
	keywords = {combinatorial problems, Document filtering, explicit constructions, Information retrieval, Min-wise independent permutations, Pseudorandom permutations},
	isbn = {0020-0190},
	doi = {10.1016/S0020-0190(99)00163-5},
	url = {http://www.sciencedirect.com/science/article/pii/S0020019099001635},
	author = {Saks,Michael and Srinivasan, Aravind and Zhou,Shiyu and Zuckerman,David}
}
