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Sakti Pramanik


Publications by Sakti Pramanik (bibliography)

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Kolbe, Dashiell, Zhu, Qiang and Pramanik, Sakti (2010): Efficient k-nearest neighbor searching in nonordered discrete data spaces. In ACM Transactions on Information Systems, 28 (2) p. 7. http://dx.doi.org/10.1145/1740592.1740595

Numerous techniques have been proposed in the past for supporting efficient k-nearest neighbor (k-NN) queries in continuous data spaces. Limited work has been reported in the literature for k-NN queries in a nonordered discrete data space (NDDS). Performing k-NN queries in an NDDS raises new challenges. The Hamming distance is usually used to measure the distance between two vectors (objects) in an NDDS. Due to the coarse granularity of the Hamming distance, a k-NN query in an NDDS may lead to a high degree of nondeterminism for the query result. We propose a new distance measure, called Granularity-Enhanced Hamming (GEH) distance, which effectively reduces the number of candidate solutions for a query. We have also implemented k-NN queries using multidimensional database indexing in NDDSs. Further, we use the properties of our multidimensional NDDS index to derive the probability of encountering valid neighbors within specific regions of the index. This probability is used to develop a new search ordering heuristic. Our experiments on synthetic and genomic data sets demonstrate that our index-based k-NN algorithm is efficient in finding k-NNs in both uniform and nonuniform data sets in NDDSs and that our heuristics are effective in improving the performance of such queries.

© All rights reserved Kolbe et al. and/or ACM Press

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Qian, Gang, Zhu, Qiang, Xue, Qiang and Pramanik, Sakti (2006): A space-partitioning-based indexing method for multidimensional non-ordered discrete data spaces. In ACM Transactions on Information Systems, 24 (1) pp. 79-110. http://doi.acm.org/10.1145/1125857.1125860

There is an increasing demand for similarity searches in a multidimensional non-ordered discrete data space (NDDS) from application areas such as bioinformatics and data mining. The non-ordered and discrete nature of an NDDS raises new challenges for developing efficient indexing methods for similarity searches. In this article, we propose a new indexing technique, called the NSP-tree, to support efficient similarity searches in an NDDS. As we know, overlap causes a performance degradation for indexing methods (e.g., the R-tree) for a continuous data space. In an NDDS, this problem is even worse due to the limited number of elements available on each dimension of an NDDS. The key idea of the NSP-tree is to use a novel discrete space-partitioning (SP) scheme to ensure no overlap at each level in the tree. A number of heuristics and strategies are incorporated into the tree construction algorithms to deal with the challenges for developing an SP-based index tree for an NDDS. Our experiments demonstrate that the NSP-tree is quite promising in supporting efficient similarity searches in NDDSs. We have compared the NSP-tree with the ND-tree, a data-partitioning-based indexing technique for NDDSs that was proposed recently, and the linear scan using different NDDSs. It was found that the search performance of the NSP-tree was better than those of both methods.

© All rights reserved Qian et al. and/or ACM Press

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Xue, Qiang, Pramanik, Sakti, Qian, Gang and Zhu, Qiang (2005): The Hybrid Digital Tree: A New Indexing Technique for Large String Databases. In: Chen, Chin-Sheng, Filipe, Joaquim, Seruca, Isabel and Cordeiro, Jos (eds.) ICEIS 2005 - Proceedings of the Seventh International Conference on Enterprise Information Systems May 25-28, 2005, Miami, USA. pp. 115-121.

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