C. Bohm, B. Braunmuller, F. Krebs, and H.-P. Kriegel. Epsilon grid order: an algorithm for the similarity join on massive high-dimensional data. In Proceedings of the 2001. Home/Search Document Details and Download
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Accelerating High-dimensional Nearest Neighbor Queries - Lang, Singh (2002) (Correct)
....do not take seek and transfer costs into account. Instead, they try to minimize the number of page misses during a join. Since these algorithms perform the join by navigating through the index tree rather than through the disk pages, they cause many random page accesses. Other papers (e.g. 21] [9]) investigate new indexing and query processing techniques in order to reduce the number of page accesses during spatial joins. In contrast to our approach, these techniques do not make use of existing index structures and are restricted to join conditions of the type oe(x; y) the distance ....
Christian Bohm, Bernhard Braunmuller, Florian Krebs, and Hans-Peter Kriegel. Epsilon grid order: An algorithm for the similarity join on massive high-dimensional data. In Proc. ACM SIGMOD Int. Conf. on Management of Data, 2001.
Joining Massive High-Dimensional Datasets - Kahveci, Lang, Singh (2003) (Correct)
....I O cost. Section 8 discusses cluster ordering. Section 9 presents the experimental results. We end with a brief discussion in Section 10. 2 Related work Joining two datasets is a costly operation. Current techniques reduce this cost by pruning pairs of data Without Index With Index point data [6, 7, 19, 44] [8, 11, 24] spatial data [3, 12, 29, 30, 36, 38, 45] 5, 20, 23, 31, 32] Table 1. A classification of join techniques. points that do not appear in the final join. They can be classified into two groups based on the data structures they use: 1) no index is built on the datasets, or 2) index is ....
....a modest data replication. The authors replicate an object under two conditions: 1) if less than t split lines intersect with it, where t is a predefined threshold, or 2) if the actual level of the object is less than some predefined maximum split level. Epsilon grid ordering technique [7] considers the distance join problem for point data. This technique splits the data space into grid cells, and assigns each data point to the cell that contains it. Later, these cells are ordered by assigning priorities to dimensions and constructing a lexicographic order. A pair of data points ....
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C. Bohm, B. Braunmuller, and F. Krebs H.-P. Kriegel. Epsilon grid order: An algorithm for the similarity join on massive high-dimensional data. In SIGMOD, Santa Barbara, CA, 2001.
Adaptable Similarity Search using Non-Relevant Information - Ashwin, Gupta, Ghosal (2002) (1 citation) (Correct)
.... retrieval of 3D objects from a CAD database [2] finding similar objects based on content from multimedia databases containing audio, image or video [11] Also, several approximation schemes have been developed to e#ciently process similarity queries using multidimensional indexing structures [19, 1, 3]. In this paper, we focus on accuracy improvement of similarity based retrieval of database objects using relevance feedback. To support similarity based modern database applications, multidimensional attribute or feature vectors are extracted from the original object, and stored in the database. ....
C. Bohm, B. Braunmuller, F. Krebs, and H.- P. Kriegel. Epsilon grid order: An algorithm for the similarity join on massive high-dimensional data. In Proc. of SIGMOD, pages 379--388, Santa Barbara, CA, 2001.
Similarity Join for Low- and High-Dimensional Data - Kalashnikov, Prabhakar (2002) (Correct)
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C. Bohm, B. Braunmuller, F. Krebs, and H.-P. Kriegel. Epsilon grid order: an algorithm for the similarity join on massive high-dimensional data. In Proceedings of the 2001.
Efficient Querying of Constantly Evolving Data - Kalashnikov (2003) (Correct)
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C. Bohm, B. Braunmuller, F. Krebs, and H.-P. Kriegel. Epsilon grid order: An algorithm for the similarity join on massive high-dimensional data. In Proceedings of the ACM SIGMOD International Conference on Management of Data, pages 379--388, 2001.
Similarity Join for Low- and High-Dimensional Data - Kalashnikov, Prabhakar (2002) (Correct)
No context found.
C. Bohm, B. Braunmuller, F. Krebs, and H.-P. Kriegel. Epsilon grid order: an algorithm for the similarity join on massive high-dimensional data. In SIGMOD, 2001.
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