| G. Cormode, M. Datar, P. Indyk, and S. Muthukrishnan. Comparing data streams using hamming norms (how to zero in). In Proc. 28th International Conf. on Very Large Data Bases (VLDB), pages 335--345, Aug. 2002. |
....search in sequence databases. Various high di mensional index structures have been proposed in [2, 3, 11, 16, 27, 29, 33, 37] to achieve fast query response time and a good quality of answers. Theoretical methods have been developed for com paring data streams under various Lp distances [12], for clustering and computing the k median [22, 32] and for computing aggregates over data streams [14, 18] Various histogramming algorithms, provably optimal [26] and near optimal approaches [20, 21] have been proposed for maintaining the distribution of a single attribute stream. The idea ....
G. Cormode, M. Datar, P. Indyk, and S. Muthukrishnan. Comparing data streams using hamming norms (how to zero in). In VLDB, pages 335-345, 2002.
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G. Cormode, M. Datar, P. Indyk, and S. Muthukrishnan. Comparing data streams using Hamming norms. In Proc. Intl. Conf. VLDB, pages 335--345, 2002.
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G. Cormode, M. Datar, P. Indyk and S. Muthukrishnan. Comparing data streams using hamming norms (How to zero in). Proc. VLDB, 2002, 335--345.
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G. Cormode, M. Datar, P. Indyk, and S. Muthukrishnan, "Comparing data streams using Hamming norms," in VLDB, 2002, pp. 335--345
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G. Cormode, M. Datar, P. Indyk, and S. Muthukrishnan. Comparing data streams using Hamming norms. In Proceedings of 28th International Conference on Very Large Data Bases, pages 335--345, 2002.
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G. Cormode, M. Datar, P. Indyk and S. Muthukrishnan. Comparing data streams using hamming norms (How to zero in). Proc. VLDB, 2002, 335--345.
.... recent urry of results in data streams has focused on using various L p norms to compare and collate information from di erent data streams, for example, j a i;j ) p 1=p for 0 p 2 [AMS96, FKSV99, Ind00, CIKM02] and related notions such as Hamming norms i ( j a i;j ) 6= 0) CDIM02] While these norms are suitable for capturing comparative trends in multiple data streams, they are not applicable for computing the various dominance norms (max, min, count or relative) which are just as important in scenarios such as analyzing nancial, web click, and network monitoring ....
....also discuss other quantities such as min dominance, relative norms, and quantile dominance, although these are not in general norms. 2. 2 Stable Distributions Indyk pioneered the use of Stable Distributions in data streams and since then have received a great deal of attention [Ind00, CIKM02, CDIM02, GGI 02] They have been used to generalize the Johnson Lindenstrauss lemma [JL84] which approximates the Euclidean distance) to all L p distances with 0 p 2 [Ind00] A stable distribution is de ned by four parameters. These are (i) the stability index, 0 2; ii) the skewness ....
G. Cormode, M. Datar, P. Indyk, and S. Muthukrishnan. Comparing data streams using Hamming norms. In Proceedings of 28th International Conference on Very Large Data Bases, 2002.
....can be associated with different groups at different times. This makes it non trivial to ex tend the results of [8, 9] for our problem. There has been recent work on mining data streams, such as the construction of decision trees [7, 6] association rule mining [15] and similarity matching [4]. The clustering algorithms literature is extensive, but only recently have streaming al gorithms been studied [3, 13] They study the k median clustering problem, which is similar to our problem. However, they study the version with only clients, and their results do not work for the server ....
....study the version with only clients, and their results do not work for the server client instance we have under data streams. In general, few results are known in the model of dynamic maintenance, where stream data is dis carded as well. Notable exceptions include Lpnorms [16] Hamming norms [4], quantiles [11] and results on maintaining stream statistics over sliding windows [5] The study of reverse nearest neighbors (RNN) in databases was initiated by Korn and Muthukr ishnan [18] Follow up work includes the proposal of more efficient access methods for indexing re verse nearest ....
G. Cormode, M. Datar, P. Indyk, and S. Muthukrishnan. Comparing data streams using hamming norms. In Intl. Conf. on Very Large Databases (VLDB), Hong Kong, China, Aug. 2002.
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G. Cormode, M. Datar, P. Indyk, and S. Muthukrishnan. Comparing data streams using hamming norms (how to zero in). In Proc. 28th International Conf. on Very Large Data Bases (VLDB), pages 335--345, Aug. 2002.
No context found.
G. Cormode, M. Datar, P. Indyk, S. Muthukrishnan. Comparing Data Streams Using Hamming Norms (How to Zero In). In Proc. Int. Conf. on Very Large Data Bases, 2002, pp. 335--345.
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G. Cormode, M. Datar, P. Indyk, and S. Muthukrishnan. Comparing data streams using hamming norms (how to zero in). In VLDB, 2002.
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G. Cormode, M. Datar, P. Indyk, and S. Muthukrishnan. Comparing data streams using hamming norms (how to zero in). In VLDB, 2002.
No context found.
G. Cormode, M. Datar, P. Indyk, and S. Muthukrishnan. Comparing data streams using hamming norms (how to zero in). In VLDB, pages 335--345, 2002.
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