Citations: Database selection for processing k nearest neighbors queries in distributed environments

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C. Yu, P. Sharma, W. Meng and Y. Qin. Databases Selection for Processing k Nearest Neighbors Queries in Distributed Environments. 1st ACM/IEEE-CS joint conf. on DL, 2001. 16

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Efficient k Nearest Neighbor Queries on Remote Spatial.. - Liu, Lim, Ng (Correct)

....among all centroids. The selected centroids are shown in Figure 6. The latitude and longitude of the original centriods were shifted for the computational convenience. To measure the performances of our methods, we adopted three measures; namely, iteration, average accuracy, and average e#ciency [30]. Iteration refers to the number of window queries needed to obtain all k nearest neighbors. Average accuracy refers to the average ratio of the number of actual nearest neighbors retrieved to k in each window query. Mathematically, accuracy avg = # iteration i=1 accuracy i iteration , where ....

C. Yu, P. Sharma, W. Y. Meng, and Y. Qin. Database selection for processing k nearest neighbors queries in distributed environments. In ACM/IEEE Joint Conference on Digital Libraries, pages 215--222, Roanoke, VA, USA, 2001.

Efficient k Nearest Neighbor Queries on Remote Spatial.. - Liu, Lim, Ng (Correct)

....are shown in Figure 12. The latitude and longitude of the original centriods are shifted for the computational convenience. All asterisks in Figure 12 form the boundary. To measure the performances of our methods, we adopted and extended the two metrics (i.e. accuracy and e#ciency) mentioned in [33]. Performances are then evaluated by six measures: iteration, accuracy of the first window query, average accuracy, e#ciency of the first window query, e#ciency of the last window query, and average e#ciency. The notations used have been summarized in Table 1. Iteration refers to the number of ....

C. Yu, P. Sharma, W. Y. Meng, and Y. Qin. Database selection for processing k nearest neighbors queries in distributed environments. In ACM/IEEE Joint Conference on Digital Libraries, pages 215--222, 2001. 31

Distributed Top-N Query Processing with Possibly.. - Yu, Philip, Meng (2003) Self-citation (Yu Meng) (Correct)

....potential to be ranked among the top N results and uses histogram based techniques to narrow the search space. Preference query evaluation typically ranks all tuples and uses views or materialized views to support preference capabilities [10, 12] 5. The most closely related work to this paper is [23] but there are several signi cant di erences between it and this paper. First, a new merge algorithm is proposed, which is is compared with that in [23] in this paper. Second, 23] used just one type of histograms to estimate the distance of the best matched tuple in a database and no comparison ....

....all tuples and uses views or materialized views to support preference capabilities [10, 12] 5. The most closely related work to this paper is [23] but there are several signi cant di erences between it and this paper. First, a new merge algorithm is proposed, which is is compared with that in [23] in this paper. Second, 23] used just one type of histograms to estimate the distance of the best matched tuple in a database and no comparison with other methods was made. In this paper, three di erent types of histograms are utilized and compared with the one used in [23] Third, the estimation ....

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C. Yu, P. Sharma, W. Meng and Y. Qin. Databases Selection for Processing k Nearest Neighbors Queries in Distributed Environments. 1st ACM/IEEE-CS joint conf. on DL, 2001. 16

Distributed Top-N Query Processing with Possibly.. - Yu, Philip, Meng Self-citation (Yu Meng) (Correct)