政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/100632

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/100632

English | 正體中文 | 简体中文 | Post-Print筆數 : 27 | 全文筆數/總筆數 : 113648/144635 (79%)
造訪人次 : 51674659 線上人數 : 718

RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.

搜尋範圍

查詢小技巧：

您可在西文檢索詞彙前後加上"雙引號"，以獲取較精準的檢索結果

若欲以作者姓名搜尋，建議至進階搜尋限定作者欄位，可獲得較完整資料

進階搜尋

主頁 ‧ 登入 ‧ 上傳 ‧ 說明 ‧ 關於政大典藏 ‧ 管理

到手機版

政大機構典藏 > 資訊學院 > 資訊科學系 > 學位論文 > Item 140.119/100632

請使用永久網址來引用或連結此文件: https://nccur.lib.nccu.edu.tw/handle/140.119/100632

題名:	在Spark大數據平台上分析DBpedia開放式資料：以電影票房預測為例 Analyzing DBpedia Linked Open Data (LOD) on Spark：Movie Box Office Prediction as an Example
作者:	劉文友 Liu, Wen Yu
貢獻者:	胡毓忠 Hu, Yuh Jong 劉文友 Liu, Wen Yu
關鍵詞:	開放式鏈結資料資源描述框架巨量資料 Spark 簡單貝氏分類貝氏網路 LOD RDF Big Data Spark Naive Bayes Bayesian Network
日期:	2016
上傳時間:	2016-08-22 17:23:39 (UTC+8)
摘要:	近年來鏈結開放式資料 (Linked Open Data，簡稱LOD) 被認定含有大量潛在價值。如何蒐集與整合多元化的LOD並提供給資料分析人員進行資料的萃取與分析，已成為當前研究的重要挑戰。LOD資料是RDF (Resource Description Framework) 的資料格式。我們可以利用SPARQL來查詢RDF資料，但是目前對於大量RDF的資料除了缺少一個高性能且易擴展的儲存和查詢分析整合性系統之外，對於RDF大數據資料分析流程的研究也不夠完備。本研究以預測電影票房為例，使用DBpedia LOD資料集並連結外部電影資料庫 (例如：IMDb)，並在Spark大數據平台上進行巨量圖形的分析。首先利用簡單貝氏分類與貝氏網路兩種演算法進行電影票房預測模型實例的建構，並使用貝氏訊息準則 (Bayesian Information Criterion，簡稱BIC) 找到最佳的貝氏網路結構。接著計算多元分類的ROC曲線與AUC值來評估本案例預測模型的準確率。 Recent years, Linked Open Data (LOD) has been identified as containing large amount of potential value. How to collect and integrate multiple LOD contents for effective analytics has become a research challenge. LOD is represented as a Resource Description Framework (RDF) format, which can be queried through SPARQL language. But large amount of RDF data is lack of a high performance and scalable storage analysis system. Moreover, big RDF data analytics pipeline is far from perfect. The purpose of this study is to exploit the above research issue. A movie box office sale prediction scenario is demonstrated by using DBpedia with external IMDb movie database. We perform the DBpedia big graph analytics on the Apache Spark platform. The movie box office prediction for optimal model selection is first evaluated by BIC. Then, Naïve Bayes and Bayesian Network optimal model’s ROC and AUC values are obtained to justify our approach.
參考文獻:	[1] H. Paulheim, "Knowledge graph refinement: A survey of approaches and evaluation methods," Semantic Web, vol. 7, 2016. [2] M. Nickel, et al., "A review of relational machine learning for knowledge graphs," Proceedings of the IEEE, vol. 104, pp. 11-33, 2016. [3] P. Bloem, et al., "Simplifying RDF Data for Graph-Based Machine Learning," in KNOW@ LOD, 2014. [4] S. Auer, et al., Dbpedia: A nucleus for a web of open data: Springer, 2007. [5] J. Lehmann, et al., "DBpedia–a large-scale, multilingual knowledge base extracted from Wikipedia," Semantic Web, vol. 6, pp. 167-195, 2015. [6] J. Han, et al., Data Mining: Concepts and Techniques: Morgan Kaufmann Publishers Inc., 2011. [7] M. Zaharia, et al., "Resilient distributed datasets: A fault-tolerant abstraction for in-memory cluster computing," in Proceedings of the 9th USENIX conference on Networked Systems Design and Implementation, 2012, pp. 2-2. [8] R. S. Xin, et al., "GraphX: a resilient distributed graph system on Spark," presented at the First International Workshop on Graph Data Management Experiences and Systems, New York, New York, 2013. [9] J. E. Gonzalez, et al., "Graphx: Graph processing in a distributed dataflow framework," in 11th USENIX Symposium on Operating Systems Design and Implementation (OSDI 14), 2014, pp. 599-613. [10] S. Das, et al., "A Tale of Two Graphs: Property Graphs as RDF in Oracle," in EDBT, 2014, pp. 762-773. [11] J. Huang, et al., "Scalable SPARQL querying of large RDF graphs," Proceedings of the VLDB Endowment, vol. 4, pp. 1123-1134, 2011. [12] K. Rohloff and R. E. Schantz, "High-performance, massively scalable distributed systems using the MapReduce software framework: the SHARD triple-store," presented at the Programming Support Innovations for Emerging Distributed Applications, Reno, Nevada, 2010. [13] P. Cudré-Mauroux, et al., "NoSQL Databases for RDF: An Empirical Evaluation," in The Semantic Web – ISWC 2013: 12th International Semantic Web Conference, Sydney, NSW, Australia, October 21-25, 2013, Proceedings, Part II, H. Alani, L. Kagal, A. Fokoue, P. Groth, C. Biemann, J. X. Parreira, et al., Eds., ed Berlin, Heidelberg: Springer Berlin Heidelberg, 2013, pp. 310-325. [14] A. S. Ismail, et al., "Bridging the gap for retrieving DBpedia data," in e-Technologies and Networks for Development (ICeND), 2015 Forth International Conference on, 2015, pp. 1-5. [15] M. Nickel, et al., "Factorizing YAGO: scalable machine learning for linked data," presented at the Proceedings of the 21st international conference on World Wide Web, Lyon, France, 2012. [16] V. Tresp, et al., "Towards Machine Learning on the Semantic Web," in Uncertainty Reasoning for the Semantic Web I, C. Paulo Cesar, D. A. Claudia, F. Nicola, B. L. Kathryn, J. L. Kenneth, L. Thomas, et al., Eds., ed: Springer-Verlag, 2008, pp. 282-314. [17] P. Bloem and G. K. De Vries, "Machine learning on linked data, a position paper," Linked Data for Knowledge Discovery, p. 69, 2014. [18] D. Krompaß and V. Tresp, "Ensemble Solutions for Link-Prediction in Knowledge Graphs," 2015. [19] N. Jayaram, et al., "Querying knowledge graphs by example entity tuples," IEEE Transactions on Knowledge and Data Engineering, vol. 27, pp. 2797-2811, 2015. [20] M. D. Scutari, Jean-Baptiste, Bayesian Networks: With Examples in R: Chapman and Hall/CRC 2014. [21] M. Scutari, "Learning Bayesian Networks with the bnlearn R Package," 2010, vol. 35, p. 22, 2010-07-16 2010. [22] G. Shmueli, "To explain or to predict?," Statistical science, pp. 289-310, 2010. [23] G. Schwarz, "Estimating the dimension of a model," The annals of statistics, vol. 6, pp. 461-464, 1978. [24] D. Pyle, Data preparation for data mining vol. 1: Morgan Kaufmann, 1999. [25] H. Liu, et al., "Discretization: An Enabling Technique," Data Mining and Knowledge Discovery, vol. 6, pp. 393-423. [26] M. C. Monard and G. E. Batista, "Learmng with Skewed Class Distributions," Advances in Logic, Artificial Intelligence, and Robotics: LAPTEC 2002, vol. 85, p. 173, 2002. [27] T. Fawcett, "An introduction to ROC analysis," Pattern recognition letters, vol. 27, pp. 861-874, 2006. [28] F. Provost and P. Domingos, "Well-trained PETs: Improving probability estimation trees," 2000. [29] D. J. Hand and R. J. Till, "A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems," Machine Learning, vol. 45, pp. 171-186, 2001.
描述:	碩士國立政治大學資訊科學學系 103753023
資料來源:	http://thesis.lib.nccu.edu.tw/record/#G0103753023
資料類型:	thesis
顯示於類別:	[資訊科學系] 學位論文

文件中的檔案:

檔案	大小	格式	瀏覽次數
302301.pdf	6337Kb	Adobe PDF2	292	檢視/開啟

在政大典藏中所有的資料項目都受到原著作權保護.

社群 sharing

著作權政策宣告 Copyright Announcement

1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.

DSpace Software Copyright © 2002-2004 MIT & Hewlett-Packard / Enhanced by NTU Library IR team Copyright © - 回饋