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| Title: | 在高度分散式環境下進行Top-k相似文件檢索
Similar Top-k documents retrieval in highly distributed environments |
| Authors: | 王俊閎
Wang, Chun Hung |
| Contributors: | 陳良弼
Chen, Arbee L.P.
王俊閎
Wang, Chun Hung |
| Keywords: | 分散式環境
Tok-k 相似文件檢索
端對端網路
distributed environments
similar top-k documents retrieval
peer-to-peer network |
| Date: | 2012 |
| Issue Date: | 2012-12-03 11:27:23 (UTC+8) |
| Abstract: | 在文件資料庫的查詢處理上，Top-k相似文件查詢主要是協助使用者可以從龐大的文件集合中，檢索出和查詢文件具有高度相關性的文件集合。將資料庫內的文件依據和查詢文件之相似度程度，選擇出相似度最高的前k篇文件回傳給使用者。然而過去集中式資料庫，因其覆蓋性和可擴充性的不足，使得這種排名傾向的文件查詢處理，需耗費大量時間及運算成本。近年來，使用端對端(Peer-to-peer, P2P)架構解決相關的文件檢索問題已成為一種趨勢，但在高度分散式環境下，支援排名傾向的相似文件查詢是困難的，因為缺乏全域資訊和適當的系統協調者。
在本研究中，我們先針對各節點資料庫作分群前處理，並提出一個利用區域切割的作法[1]，將P2P環境劃分成數個子區塊後，建立特徵索引表。因此在查詢處理時，可透過索引表加快挑選出Top-k相似群集的速度，並且確保有適當數量的回傳結果。最後在實驗中，我們提出的方法會與傳統集中式搜尋引擎以及SON-based [1] 做比較，在高度分散式環境下，我們的方法在執行Top-k相似文件查詢時，會比上述兩種作法有較為優異的表現。
On query processing in a large database, similar top-k documents query is an important mechanism to retrieve the highly correlated document collection with query for users. It ranks documents with a similarity ranking function and reports the k documents with highest similarity. However, the former approach in web searching, i.e., centralized search engines, rises some issues such as lack of coverage and scalability, impact provides rank-based query become a costly operation. Recently, using Peer-to-peer (P2P) architectures to tackle above issues has emerged as a trend of solution, but due to the shortage of global knowledge and some appropriate central coordinators, support rank-based query in highly distributed environment has been difficulty.
In this paper, we proposed a framework to solve these problems. First, we performed the local cluster pre-processing on each peer, followed by the zone creation process, forming sub-zones over P2P network, and then constructing the feature index table to improve the performance of selecting similar top-k cluster results. The experiments show that our approach performs similar top-k documents query outperforms than SON-based approach in highly distributed environment. |
| Reference: | [1] Christos Doulkeridis, Kjetil Nørvåg, Michalis Vazirgiannis. 2008. Peer-to-peer similarity search over widely distributed document collections. LSDS-IR 35-42.
[2] Stoica, I., Morris, R., Karger, D., Kaashoek, M.F., Balakrishnan, H. 2001. Chord : A scalable peer-to-peer lookup service for internet applications. In Proceedings of the ACM SIGCOMM 149-160.
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[16] Wolf-Tilo Balke, Wolfgang Nejdl, Wolf Siberski, Uwe Thaden. 2005. Progressive Distributed Top k Retrieval in Peer-to-Peer Networks. ICDE 174-185.
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[18] C. Gkantsidis, M. Mihail, and A. Saberi. 2005. Hybrid search schemes for unstructured peer-to-peer networks. In Proceedings of INFOCOM.
[19] Inderjit S. Dhillon, Dharmendra S. Modha. 2001. Concept Decompositions for Large Sparse Text Data Using Clustering. Machine Learning 42(1/2): 143-175.
[20] Akrivi Vlachou, Christos Doulkeridis, Kjetil Nørvåg, Michalis Vazirgiannis. 2008. On efficient top-k query processing in highly distributed environments. SIGMOD 753-764.
[21] Shiwei Zhu, Junjie Wu, Hui Xiong, Guoping Xia. 2011. Scaling up top-K cosine similarity search. Data Knowl. Eng. (DKE) 70(1) 60-83.
[22] Aoying Zhou, Rong Zhang, Weining Qian, Quang Hieu Vu, Tianming Hu. 2008. Adaptive indexing for content-based search in P2P systems. Data Knowl. Eng. (DKE) 67(3) 381-398. |
| Description: | 碩士
國立政治大學
資訊科學學系
99753034
101 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0099753034 |
| Data Type: | thesis |
| Appears in Collections: | [資訊科學系] 學位論文
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