Showing papers by "Srikanta Bedathur published in 2006"
23 May 2006
TL;DR: The BuzzRank method, a method that quantifies trends in time series of importance scores and is based on a relevant growth model of importance score, is proposed and experimentally demonstrated the usefulness of BuzzRank on a bibliographic dataset.
Abstract: Ranking methods like PageRank assess the importance of Web pages based on the current state of the rapidly evolving Web graph. The dynamics of the resulting importance scores, however, have not been considered yet, although they provide the key to an understanding of the Zeitgeist on the Web. This paper proposes the BuzzRank method that quantifies trends in time series of importance scores and is based on a relevant growth model of importance scores. We experimentally demonstrate the usefulness of BuzzRank on a bibliographic dataset.
22 citations
01 Jan 2006
TL;DR: In this article, a new layout strategy, called Stellar, is proposed to improve the search efficiency of disk-resident suffix-trees through customized layouts of tree-nodes to disk-pages.
Abstract: Suffix-trees are popular indexing structures for various sequence processing problems in biological data management. We investigate here the possibility of enhancing the search efficiency of disk-resident suffix-trees through customized layouts of tree-nodes to disk-pages. Specifically, we propose a new layout strategy, called Stellar, that provides significantly improved search performance on a representative set of real genomic sequences. Further, Stellar supports both the standard root-to-leaf lookup queries as well as sophisticated sequencesearch algorithms that exploit the suffix-links of suffix-trees. Our results are encouraging with regard to the ultimate objective of seamlessly integrating sequence processing in database engines.
21 citations
01 Jan 2006
2 citations
01 Jan 2006
TL;DR: The BuzzRank method as discussed by the authors quantifies trends in time series of importance scores and is based on a relevant growth model of importance score, which can provide the key to an understanding of the Zeitgeist on the Web.
Abstract: Ranking methods like PageRank assess the importance of Web pages based on the current state of the rapidly evolving Web graph. The dynamics of the resulting importance scores, however, have not been considered yet, although they provide the key to an understanding of the Zeitgeist on the Web. This paper proposes the BuzzRank method that quantifies trends in time series of importance scores and is based on a relevant growth model of importance scores. We experimentally demonstrate the usefulness of BuzzRank on a bibliographic dataset.
1 citations
06 Nov 2006
1 citations