| dc.contributor | 資科系 | |
| dc.creator (作者) | Lin, Y.-W.;Wang, E.T.;Chiang, Chieh-Feng;Chen, Arbee L. P. | |
| dc.creator (作者) | 陳良弼 | zh_TW |
| dc.date (日期) | 2014-04 | |
| dc.date.accessioned | 16-Jun-2015 15:57:19 (UTC+8) | - |
| dc.date.available | 16-Jun-2015 15:57:19 (UTC+8) | - |
| dc.date.issued (上傳時間) | 16-Jun-2015 15:57:19 (UTC+8) | - |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/75850 | - |
| dc.description.abstract (摘要) | Finding the nearest neighbors and finding the farthest neighbors are fundamental problems in spatial databases. Consider two sets of data points in a two-dimensional data space, which represent a set of favor locations F, such as libraries and schools, and a set of disfavor locations D, such as dumps and gambling houses. Given another set of data points C in this space as houses for rent, one who needs to rent a house may need a recommendation which takes into account the favor and disfavor locations. To solve this problem, a new two-dimensional data space is employed, in which dimension X describes the distance from a data point c in C to its nearest neighbor in D and dimension Y describes the distance from c to its farthest neighbor in F. Notice that the larger value is preferred in dimension X while the smaller value is preferred in dimension Y. Following the above dominance rule, the recommendation for the house renting can be achieved by a skyline query. A naïve method to processing this query is 1) to find the nearest neighbor from D and the farthest neighbor from F for each data point in C and then 2) to construct a new two-dimensional data space based on the results from 1) and to apply any of the existing skyline algorithms to get the answer. In this paper, based on the quad-tree index, we propose an efficient algorithm to answer this query by combining the above two steps. A series of experiments with synthetic data and real data are performed to evaluate this approach and the experiment results demonstrate the efficiency of the approach. Copyright 2014 ACM. | |
| dc.format.extent | 176 bytes | - |
| dc.format.mimetype | text/html | - |
| dc.relation (關聯) | Proceedings of the ACM Symposium on Applied Computing, 2014, Pages 821-826, 29th Annual ACM Symposium on Applied Computing, SAC 2014; Gyeongju; South Korea; 24 March 2014 到 28 March 2014; 代碼 106778 | |
| dc.subject (關鍵詞) | Algorithms; Experiments; Houses; Data points; Dominance rules; Farthest neighbors; Nearest neighbors; Skyline query; Skyline query processing; Spatial database; Synthetic data; Query processing | |
| dc.title (題名) | Finding targets with the nearest favor neighbor and farthest disfavor neighbor by a skyline query | |
| dc.type (資料類型) | conference | en |
| dc.identifier.doi (DOI) | 10.1145/2554850.2554863 | |
| dc.doi.uri (DOI) | http://dx.doi.org/10.1145/2554850.2554863 | |
