(3.238.235.155) 您好!臺灣時間:2021/05/11 19:08
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果

詳目顯示:::

: 
twitterline
研究生:楊翔普
研究生(外文):Shiang-Pu Yang
論文名稱:在不確定性資料流中ε連接處理之研究
論文名稱(外文):A Study for ε Join Processing over Uncertain Data Stream
指導教授:劉傳銘劉傳銘引用關係
指導教授(外文):Chuan-Ming Liu
口試委員:王正豪俞征武
口試日期:2016-07-19
學位類別:碩士
校院名稱:國立臺北科技大學
系所名稱:資訊工程系研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
畢業學年度:104
中文關鍵詞:資料流不確定性資料ε連接運算
外文關鍵詞:Data StreamUncertain DataEpsilon join
相關次數:
  • 被引用被引用:0
  • 點閱點閱:47
  • 評分評分:系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔
  • 下載下載:9
  • 收藏至我的研究室書目清單書目收藏:0
連接運算主要是應用在資料庫中,查詢兩兩一組的物件組合中,屬性有著相同條件的物件組合,常用來作為資料清理或是異質檢測的方法,而ε連接運算則是查詢物件組合中,空間屬性在ε距離內的物件組合,而這些資料庫內的屬性資料主要都是確定性資料。但是由於硬體或環境上的一些因素,可能會造成資料在資料庫中並不是真實的數值,例如在感測網路中,感測器感應到濕度之後,會將所感測到的資料回傳給伺服器,可能會因為感測器本身硬體的不準確或是傳輸時的干擾,造成回傳到伺服器的資料是不正確的,所以發展出了不確定性資料,用來表示可能會產生誤差的資料。而在近年來即時應用的需求越來越大,連接運算的資料也從靜態的資料庫取得,變為以資料流的方式,連續性且不間斷地進入系統,而本篇論文主要探討的就是此種在不確定性資料流中的ε連接運算。
Join processing is mainly used to query and join objects which have same attribute value in database. Join processing over uncertain data streams is useful in many practical applications such as data cleaning and outlier detection. ε join processing is a kind of join processing. ε join processing is finding pairs of objects that satisfied distance less than ε. Most of data are certainty data, but errors caused by hardware and environments may led to the uncertainty of data. Take wireless sensor network for example, sensors will return sensored data to server after monitoring environment, but wrong data will be produced when inaccurate sersoring and network interference occurs. Hence, uncertain data processing is developed to deal with inaccurate data. In the other hand, requirement of real time application grows rapidly in recent years, therefore, data for join processing turns from static state to dynamic data stream. As a result, this paper discusses on ε Join Processing over Uncertain Data Stream.
摘 要 i
ABSTRACT ii
誌 謝 iv
目 錄 v
表目錄 vii
圖目錄 viii
第一章 前言 1
第二章 相關研究 3
2.1 不確定性物件 3
2.2 不確定性物件的距離 4
2.3 R-Tree 4
2.4 使用R-Tree結構索引的連接處理演算法 6
2.5 ε連接處理 9
2.6 不確定性資料的ε連接處理方法 9
2.7 準確率和遺失率 13
2.8 不確定性資料流的架構 14
2.9 在不確定性資料流下作ε連接運算 15
第三章 ε連接處理優化方法 17
3.1 傳統的ε連接處理 18
3.2 優化的ε連接處理 21
第四章 實驗 24
4.1 ε連接處理的運算時間和運算次數 26
4.2 準確率和遺失率 28
第五章 結論 29
參考文獻 30
[1]Chi-Eng Sun and Chuan-Ming Liu “An effective approach for computing epsilon join on uncertain data.” ,NTUT, 2013.
[2]Antonin Guttman “R-trees: a dynamic index structure for spatial searching.” Pro-ceedings of the 1984 ACM SIGMOD international conference on Management of data, pp.47-57, 1984.
[3]Timos Sellis, Nick Roussopoulos and Christos Faloutsos “The R+-Tree: A Dynamic Index For Multi-Dimensional Objects.” In Proceedings of the VLDB 87 Proceedings of the 13th International Conference on Very Large Data Ba-ses,pp.507-518, 1987.
[4]Thomas Brinkhoff, Hans-Peter Kriegel and Bernhard Seeger “Efficient Processing of Spatial Joins Using R-trees.” Proceedings of the 1993 ACM SIGMOD interna-tional conference on Management of data, pp. 237-246, 1993.
[5]Shawn R. Jeffery, Michael J. Franklin, and Minos Garofalakis “An Adaptive RFID Middleware for Supporting Metaphysical Data Independence,” The VLDB Jour-nal — The International Journal on Very Large Data Bases, pp. 265-289, 2008.
[6]Yunyue Zhu and Dennis Shasha “StatStream: Statistical Monitoring of Thousands of Data Streams in Real Time.” VLDB 02 Proceedings of the 28th international conference on Very Large Data Bases,pp.358-369, 2002.
[7]Edwin M. Knorr and Raymond T. Ng “Algorithms for Mining Distance-Based Outliers in Large Datasets.” VLDB 98 Proceedings of the 24rd International Con-ference on Very Large Data Bases, pp. 392-403, 1998.
[8]Yanlei Diao, Boduo Li, Anna Liu, Liping Peng, Charles Sutton, Thanh Tran and Michael Zink “Capturing Data Uncertainty in High-Volume Stream Processing.” Computing Research Repository - CORR , vol. abs/0909.1, 2009.
[9]Dmitri V. Kalashnikov and Sunil Prabhakar “Fast similarity join for mul-ti-dimensional data.” Information Systems archive Volume 32 Issue 1, pp.160-177, 2007.
[10]Xiang Lian and Lei Chen “Efficient Join Processing on Uncertain Data Streams.” CIKM 09 Proceedings of the 18th ACM conference on Information and knowledge management, pp 857-866, 2009.
[11]Tolga Urhan and Michael J. Franklin ” Xjoin: A reactively-scheduled pipelined join operator.” IEEE Data(base) Engineering Bulletin - DEBU , vol. 23, no. 2, pp. 27-33, 2000.
[12]Mohamed F. Mokbel, Ming Lu and Walid G. Aref “Hash-merge join: A non-blocking join algorithm for producing fast and early join results.” International Conference on Data Engineering - ICDE , pp. 251-263, 2004.
[13]Yufei Tao, Man Lung Yiu, Dimitris Papadias, Marios Hadjieleftheriou and Nikos Mamoulis “RPJ: Producing fast join results on streams through rate-based optimi-zation.” International Conference on Management of Data - SIGMOD , pp. 371-382, 2005.
連結至畢業學校之論文網頁點我開啟連結
註: 此連結為研究生畢業學校所提供,不一定有電子全文可供下載,若連結有誤,請點選上方之〝勘誤回報〞功能,我們會盡快修正,謝謝!
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top
系統版面圖檔 系統版面圖檔