| dc.contributor.advisor | 陳良弼 | zh_TW |
| dc.contributor.advisor | Chen, Arbee L. P. | en_US |
| dc.contributor.author (Authors) | 鄧雅文 | zh_TW |
| dc.contributor.author (Authors) | Teng, Ya Wen | en_US |
| dc.creator (作者) | 鄧雅文 | zh_TW |
| dc.creator (作者) | Teng, Ya Wen | en_US |
| dc.date (日期) | 2009 | en_US |
| dc.date.accessioned | 11-Oct-2011 16:57:34 (UTC+8) | - |
| dc.date.available | 11-Oct-2011 16:57:34 (UTC+8) | - |
| dc.date.issued (上傳時間) | 11-Oct-2011 16:57:34 (UTC+8) | - |
| dc.identifier (Other Identifiers) | G0097753034 | en_US |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/51590 | - |
| dc.description (描述) | 碩士 | zh_TW |
| dc.description (描述) | 國立政治大學 | zh_TW |
| dc.description (描述) | 資訊科學學系 | zh_TW |
| dc.description (描述) | 97753034 | zh_TW |
| dc.description (描述) | 98 | zh_TW |
| dc.description.abstract (摘要) | 在資料庫的處理中，top-k查詢幫助使用者從龐大的資料中萃取出具有價值的物件，它將資料庫中的物件依照給分公式給分後，選擇出分數最高的前k個回傳給使用者。然而在多數的情況下，一個物件也許不只有一個分數，要如何在多個分數中仍然選擇出整體最高分的前k個物件，便成為一個新的問題。在本研究中，我們將這樣的物件用不確定資料來表示，而每個物件的不確定性則是其帶有機率的分數以表示此分數出現的可能性，並提出一個新的問題：Best-kGROUP查詢。在此我們將情況模擬為一個複合式競賽，其中有多個子項目，每個項目的參賽人數各異，且最多需要k個人參賽；我們希望能針對此複合式競賽挑選出最佳的k個球員組合。當我們定義一個較佳的組合為其在較多項目居首位的機率比另一組合高，而最佳的組合則是沒有比它更佳的組合。為了加快挑選的速度，我們利用動態規劃的方式與篩選的演算法，將不可能的組合先剔除；所剩的組合則是具有天際線特質的組合，在這些天際線組合中，我們可以輕易的找出最佳的組合。此外，在實驗中，對於在所有球員中挑選最佳的組合，Best-kGROUP查詢也有非常優異的表現。 | zh_TW |
| dc.description.abstract (摘要) | In a large database, top-k query is an important mechanism to retrieve the most valuable information for the users. It ranks data objects with a ranking function and reports the k objects with the highest scores. However, when an object has multiple scores, how to rank objects without information loss becomes challenging. In this paper, we model the object with multiple scores as an uncertain data object and the uncertainty of the object as a distribution of the scores, and consider a novel problem named Best-kGROUP query. Imagine the following scenario. Assume there is a composite competition consisting of several games each of which requires a distinct number of players. Suppose the largest number is k, and we want to select the best group of k players from all the players for the competition. A group x is considered better than another group y if x has higher aggregated probability to be the top ones in more games than y. In order to speed up the selection process, the groups worse than another group definitely should first be discarded. We identify these groups using a dynamic programming based approach and a filtering algorithm. The remaining groups with the property that none of them have higher aggregated probability to be the top ones for all games against the other groups are called skyline groups. From these skyline groups, we can easily compare them to select the best group for the composite competition. The experiments show that our approach outperforms the other approaches in selecting the best group to defeat the other groups in the composite competitions. | en_US |
| dc.description.tableofcontents | 1 INTRODUCTION 12 RELATED WORK 72.1 Top-k Queries 72.2 Uncertain Data 82.2.1 Uncertain Top-k Queries 82.2.2 Uncertain Top-k Queries on Data Streams 142.2.3 Uncertain Nearest Neighbor Queries 142.3 Skyline 153 METHODOLOGY 173.1 Problem Definition 173.2 The Basic Algorithms 203.3 The Heuristic Approaches 284 EXPERIMENTS 294.1 Experiment Setup 294.2 On Execution Time 324.3 On Accuracy 344.4 On Performance of the Composite Competition 355 CONCLUSIONS AND FUTURE WORK 376 REFERENCES 40 | zh_TW |
| dc.language.iso | en_US | - |
| dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#G0097753034 | en_US |
| dc.subject (關鍵詞) | Top-k查詢 | zh_TW |
| dc.subject (關鍵詞) | 不確定資料 | zh_TW |
| dc.subject (關鍵詞) | Best-kGROUP查詢 | zh_TW |
| dc.subject (關鍵詞) | Top-k query | en_US |
| dc.subject (關鍵詞) | uncertain data | en_US |
| dc.subject (關鍵詞) | Best-kGROUP query | en_US |
| dc.subject (關鍵詞) | skyline kGROUP | en_US |
| dc.title (題名) | 針對複合式競賽挑選最佳球員組合的方法 | zh_TW |
| dc.title (題名) | Selecting the best group of players for a composite competition | en_US |
| dc.type (資料類型) | thesis | en |
| dc.relation.reference (參考文獻) | [1] I. F. Ilyas, G. Beskales, and M. A. Soliman. 2008. A Survey of Top-k Query Processing Techniques in Relational Database System. ACM Computing Surveys, Vol. 40, No. 4, pp. 11:1-11:58. | zh_TW |
| dc.relation.reference (參考文獻) | [2] C. C. Aggarwal and P. S. Yu. 2009. A Survey of Uncertain Data Algorithms and Applications. IEEE Transactions on Knowledge and Data Engineering, Vol. 21, No. 5, pp. 609-623. | zh_TW |
| dc.relation.reference (參考文獻) | [3] M. Soliman, I. Ilyas, and K. Chang. 2007. Top-k Query Processing in Uncertain Databases. In Proceedings of the 23rd International Conference on Data Engineering (ICDE), pp. 896-905. | zh_TW |
| dc.relation.reference (參考文獻) | [4] M. Hua, J. Pei, W. Zhang, and X. Lin. 2008. Efficiently Answering Probabilistic Threshold Top-k Queries on Uncertain Data. In Proceedings of the 24th International Conference on Data Engineering (ICDE), pp. 1403-1405. | zh_TW |
| dc.relation.reference (參考文獻) | [5] T. Ge, S. Zdonik, and S. Madden. 2009. Top-k Queries on Uncertain Data: On Score Distribution and Typical Answers. In Proceedings of the 35th International Conference on Management of Data (SIGMOD), pp. 375-388. | zh_TW |
| dc.relation.reference (參考文獻) | [6] C. Jin, K. Yi, L. Chen, J. X. Yu, and X. Lin. 2008. Sliding-Window Top-k Queries on Uncertain Streams. In Proceedings of the VLDB Endowment, Vol. 1, No. 1, pp. 301-312. | zh_TW |
| dc.relation.reference (參考文獻) | [7] G. Beskales, M. A. Soliman, and I. F. Ilyas. 2008. Efficient Search for the Top-k Probable Nearest Neighbors in Uncertain Databases. In Proceedings of the VLDB Endowment, Vol. 1, No. 1, pp. 326-339. | zh_TW |
| dc.relation.reference (參考文獻) | [8] M. L. Yiu and N. Mamoulis. 2007. Efficient Processing of Top-k Dominating Queries on Multi-Dimensional Data. In Proceedings of the 33rd International Conference on Very Large Data Bases, pp. 483-494. | zh_TW |
| dc.relation.reference (參考文獻) | [9] C.-Y. Chan, H. V. Jagadish, K.-L. Tan, A. K. H. Tung, and Z. Zhang. 2006. Finding k-Dominant Skylines in High Dimensional Space. In Proceedings of the 32nd International Conference on Management of Data (SIGMOD), pp. 503-514. | zh_TW |
