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題名 整合社群關係的OLAP操作推薦機制
A Recommendation Mechanism on OLAP Operations based on Social Network
作者 陳信固
Chen, Hsin Ku
貢獻者 李蔡彥
Li, Tsai Yen
陳信固
Chen, Hsin Ku
關鍵詞 社群網路分析
推薦機制
社群偵測
商業智慧
網絡中心性
Social Network Analysis
Recommendation Mechanism
Community Detection
Business Intelligence
Network Centrality
日期 2011
上傳時間 30-Oct-2012 10:44:43 (UTC+8)
摘要 近幾年在金融風暴及全球競爭等影響下，企業紛紛導入商業智慧平台，提供管理階層可簡易且快速的分析各種可量化管理的關鍵指標。但在後續的推廣上，經常會因商業智慧系統提供的資訊過於豐富，造成使用者在學習階段無法有效的取得所需資訊，導致商業智慧無法發揮預期效果。本論文以使用者在商業智慧平台上的操作相似度進行分析，建立相對於實體部門的凝聚子群，且用中心性計算各節點的關聯加權，整合至所設計的推薦機制，用以提升商業智慧平台成功導入的機率。經模擬實驗的證實，在推薦機制中考慮此因素會較原始的推薦機制擁有更高的精確度。
In recent years, enterprises are facing financial turmoil, global competition, and shortened business cycle. Under these influences, enterprises usually implement the Business Intelligence platform to help managers get the key indicators of business management quickly and easily. In the promotion stage of such Business Intelligence platforms, users usually give up using the system due to huge amount of information provided by the BI platform. They cannot intuitively obtain the required information in the early stage when they use the system. In this study, we analyze the similarity of users’ operations on the BI platform and try to establish cohesive subgroups in the corresponding organization. In addition, we also integrate the associated weighting factor calculated from the centrality measures into the recommendation mechanism to increase the probability of successful uses of BI platform. From our simulation experiments, we find that the recommendation accuracies are higher when we add the clustering result and the associated weighting factor into the recommendation mechanism.
參考文獻 參考文獻
[1] Freeman, L. C. “Centrality in Social Networks: Conceptual Clarification,” Social Networks, 1, 215-239, 1979.
[2] D.J. Brass, and M.E. Burkhardt, “Centrality and Power in Organization,” in Noria, N. & Eccles, R. G. (Eds.) Networks and Organizations: Structure, Form and Action, Boston, Massachusetts: Harvard Business School Press, pp.191-215, 1992.
[3] M. S. Granovetter , "Problems of Explanation in Economic Sociology", in N. Nohria and R. Eccles (Eds), Networks and Organization: Structure, Form, and r Action, pp.25-62, 1992.
[4] S. Wasserman, and K. Faust, Social Network Analysis: Methods and Applications, New York, Cambridge University Press, 1994.
[5] J.A. Konstan, B.N. Miller, D. Maltz, J.L. Herlocker, L.R. Gordon, J. Riedl, “Applying Collaborative Filtering to Usenet News,” Communications of the ACM 40(3), pp. 77–87, 1997.
[6] J. Han, “OLAP Mining: An Integration of OLAP with Data Mining,”in Conference Tutorial: Integration of Data Mining and Data Warehousing Technologies. (Microsoft PowerPoint slides), ICDE 1997.
[7] M. Balabanovic and Y. Shoham, “Fab Content-based, collaborative recommendation,” Communications of the ACM (40:3), pp.66–72, 1997.
[8] M. Golfarelli and S. Rizzi, “Conceptual design of data warehouses from E/R schemes,” in Proc. 31st Hawaii International Conference on System Sciences, 1998.
[9] C. Sapia, “On Modeling and Predicting Query Behavior in OLAP Systems,” in Proc. of the International Workshop on Design and Management of Data Warehouses (DMDW’99), 1999.
[10] I. Zukerman D. Albrecht and A. Nicholson “Predicting Users’ Requests on the WWW,” in Proc. of the 7th International Conference on User Modeling. Springer- Verlag,1999
[11] L. Page, S. Brin, R. Motwani, and T. Winograd. The PageRank Citation Ranking: Bringing Order to the Web, 1999.
[12] A. Ansari, S. Essegaier, and R. Kohli, “Internet recommendation systems,” Journal of Marketing Research, 37(3): 363, 2000.
[13] C. Sapia, “Predicting Query Behavior to Enable Predictive Caching Strategies for OLAP Systems,” PROMISE, in Y. Kambayashi, M. Mohania, A.M. Tjoa, (eds.) DaWaK 2000, LNCS, vol. 1874, pp. 224–233. Springer, Heidelberg.
[14] J. Han and M. Kamber, Data Mining: Concepts and Techniques: Measure the quality of a recommendation, Morgan Kaufmann., 2000.
[15] M. Newman, “The Structure and Function of complex Networks,” SIAM Review, 45(2), 2003.
[16] W.D. Nooy, Exploratory Network Analysis with Pajek, New York: Cambridge University Press, 2005.
[17] B. Satzger, M. Endres, and W. Kießling, “A Preference-Based Recommender System,” in Bauknecht, K., Pröll, B., Werthner, H. (eds.) EC-Web 2006, LNCS, vol. 4082, pp. 31–40, 2006.
[18] M. E. J. Newman, “Modularity and community structure in networks,” Proceedings of the National Academy of Sciences, 103(23), pp. 8577-8582, 2006.
[19] A. Giacometti, P. Marcel, and E. Negre, “A Framework for Recommending OLAP Queries,” in International Workshop on Data Warehousing and OLAP, pp. 73–80. ACM, New York, 2008.
[20] Y.-T. Chen and P.-Y. Hsu, “Supporting Tools to Query Data in Business Intelligence Systems,” in Proc. of International Conference on Business and Information, (BAI2008), 2008.
[21] H. Jerbi, F. Rava, O. Teste, and G. Zurfluh, “Applying Recommendation Technology in OLAP Systems,” in Proc. of International Conference on Enterprise Information Systems (ICEIS2009), 2009.
描述 碩士
國立政治大學
資訊科學學系
97971014
100
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0097971014
資料類型 thesis
dc.contributor.advisor 李蔡彥zh_TW
dc.contributor.advisor Li, Tsai Yenen_US
dc.contributor.author (Authors) 陳信固zh_TW
dc.contributor.author (Authors) Chen, Hsin Kuen_US
dc.creator (作者) 陳信固zh_TW
dc.creator (作者) Chen, Hsin Kuen_US
dc.date (日期) 2011en_US
dc.date.accessioned 30-Oct-2012 10:44:43 (UTC+8)-
dc.date.available 30-Oct-2012 10:44:43 (UTC+8)-
dc.date.issued (上傳時間) 30-Oct-2012 10:44:43 (UTC+8)-
dc.identifier (Other Identifiers) G0097971014en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/54334-
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) 97971014zh_TW
dc.description (描述) 100zh_TW
dc.description.abstract (摘要) 近幾年在金融風暴及全球競爭等影響下，企業紛紛導入商業智慧平台，提供管理階層可簡易且快速的分析各種可量化管理的關鍵指標。但在後續的推廣上，經常會因商業智慧系統提供的資訊過於豐富，造成使用者在學習階段無法有效的取得所需資訊，導致商業智慧無法發揮預期效果。本論文以使用者在商業智慧平台上的操作相似度進行分析，建立相對於實體部門的凝聚子群，且用中心性計算各節點的關聯加權，整合至所設計的推薦機制，用以提升商業智慧平台成功導入的機率。經模擬實驗的證實，在推薦機制中考慮此因素會較原始的推薦機制擁有更高的精確度。zh_TW
dc.description.abstract (摘要) In recent years, enterprises are facing financial turmoil, global competition, and shortened business cycle. Under these influences, enterprises usually implement the Business Intelligence platform to help managers get the key indicators of business management quickly and easily. In the promotion stage of such Business Intelligence platforms, users usually give up using the system due to huge amount of information provided by the BI platform. They cannot intuitively obtain the required information in the early stage when they use the system. In this study, we analyze the similarity of users’ operations on the BI platform and try to establish cohesive subgroups in the corresponding organization. In addition, we also integrate the associated weighting factor calculated from the centrality measures into the recommendation mechanism to increase the probability of successful uses of BI platform. From our simulation experiments, we find that the recommendation accuracies are higher when we add the clustering result and the associated weighting factor into the recommendation mechanism.en_US
dc.description.tableofcontents 目錄
第一章 緒論 1
1.1 前言 1
1.2 研究動機 2
1.3 研究目的 3
1.4 研究方式 4
1.5 論文架構 6
第二章 相關研究 7
2.1 推薦機制 7
2.1.1 OLAP推薦機制 8
2.1.2 網頁推薦機制 10
2.1.3 Page Rank演算法 10
2.2 社會網絡分析 11
第三章 研究方法 13
3.1 研究假設 13
3.2 系統架構 14
3.3使用者操作紀錄收集 15
3.4多維度操作紀錄正規化 16
第四章 社群網絡分析 19
4.1 凝聚子群 19
4.1.1 Modularity Q的計算 20
4.1.2 社群切割(Subgroups) 22
4.2老手程度的判斷 23
第五章 推薦機制 27
5.1 相似度判斷 27
5.2 候選項目篩選 29
5.3 推薦機制：最大信心度選擇 31
5.4 推薦機制：最大使用人次選擇 32
5.5 推薦機制與參考關聯加權 32
第六章 系統實作與實驗 34
6.1 程式語言、資料來源 34
6.2多維度分析推薦輔助系統功能模組說明 34
6.2.1 操作紀錄正規化 35
6.2.2 操作紀錄之相似度判斷 36
6.2.3 產生推薦候選項目集合 37
6.2.4 使用者分群 38
6.2.5 老手程度加權計算 41
6.2.6 推薦項目產出 42
6.2.7 使用者回饋機制 42
6.3 多維度分析推薦輔助系統操作介面介紹 43
6.3模擬實驗 46
6.3.1 推薦機制：最大信心度選擇的模擬結果分析 47
6.3.2 推薦機制：最大使用人次選擇的模擬結果分析 48
6.3.3 推薦機制：最大信心度選擇加入參考關聯加權的模擬結果分析 49
6.3.4 推薦機制：最大使用人次選擇加入參考關聯加權的模擬結果分析 51
6.4 使用者實際操作回饋 52
6.4.1 問卷的回饋與分析 53
6.4.2 推薦機制的正確率分析 54
第七章 結論與未來發展 58
7.1 研究結論 58
7.2 未來發展 59
參考文獻 61
附錄一：多維度分析平台推薦輔助系統實驗問卷 64		zh_TW
dc.language.iso en_US-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0097971014en_US
dc.subject (關鍵詞) 社群網路分析zh_TW
dc.subject (關鍵詞) 推薦機制zh_TW
dc.subject (關鍵詞) 社群偵測zh_TW
dc.subject (關鍵詞) 商業智慧zh_TW
dc.subject (關鍵詞) 網絡中心性zh_TW
dc.subject (關鍵詞) Social Network Analysisen_US
dc.subject (關鍵詞) Recommendation Mechanismen_US
dc.subject (關鍵詞) Community Detectionen_US
dc.subject (關鍵詞) Business Intelligenceen_US
dc.subject (關鍵詞) Network Centralityen_US
dc.title (題名) 整合社群關係的OLAP操作推薦機制zh_TW
dc.title (題名) A Recommendation Mechanism on OLAP Operations based on Social Networken_US
dc.type (資料類型) thesisen
dc.relation.reference (參考文獻) 參考文獻
[1] Freeman, L. C. “Centrality in Social Networks: Conceptual Clarification,” Social Networks, 1, 215-239, 1979.
[2] D.J. Brass, and M.E. Burkhardt, “Centrality and Power in Organization,” in Noria, N. & Eccles, R. G. (Eds.) Networks and Organizations: Structure, Form and Action, Boston, Massachusetts: Harvard Business School Press, pp.191-215, 1992.
[3] M. S. Granovetter , "Problems of Explanation in Economic Sociology", in N. Nohria and R. Eccles (Eds), Networks and Organization: Structure, Form, and r Action, pp.25-62, 1992.
[4] S. Wasserman, and K. Faust, Social Network Analysis: Methods and Applications, New York, Cambridge University Press, 1994.
[5] J.A. Konstan, B.N. Miller, D. Maltz, J.L. Herlocker, L.R. Gordon, J. Riedl, “Applying Collaborative Filtering to Usenet News,” Communications of the ACM 40(3), pp. 77–87, 1997.
[6] J. Han, “OLAP Mining: An Integration of OLAP with Data Mining,”in Conference Tutorial: Integration of Data Mining and Data Warehousing Technologies. (Microsoft PowerPoint slides), ICDE 1997.
[7] M. Balabanovic and Y. Shoham, “Fab Content-based, collaborative recommendation,” Communications of the ACM (40:3), pp.66–72, 1997.
[8] M. Golfarelli and S. Rizzi, “Conceptual design of data warehouses from E/R schemes,” in Proc. 31st Hawaii International Conference on System Sciences, 1998.
[9] C. Sapia, “On Modeling and Predicting Query Behavior in OLAP Systems,” in Proc. of the International Workshop on Design and Management of Data Warehouses (DMDW’99), 1999.
[10] I. Zukerman D. Albrecht and A. Nicholson “Predicting Users’ Requests on the WWW,” in Proc. of the 7th International Conference on User Modeling. Springer- Verlag,1999
[11] L. Page, S. Brin, R. Motwani, and T. Winograd. The PageRank Citation Ranking: Bringing Order to the Web, 1999.
[12] A. Ansari, S. Essegaier, and R. Kohli, “Internet recommendation systems,” Journal of Marketing Research, 37(3): 363, 2000.
[13] C. Sapia, “Predicting Query Behavior to Enable Predictive Caching Strategies for OLAP Systems,” PROMISE, in Y. Kambayashi, M. Mohania, A.M. Tjoa, (eds.) DaWaK 2000, LNCS, vol. 1874, pp. 224–233. Springer, Heidelberg.
[14] J. Han and M. Kamber, Data Mining: Concepts and Techniques: Measure the quality of a recommendation, Morgan Kaufmann., 2000.
[15] M. Newman, “The Structure and Function of complex Networks,” SIAM Review, 45(2), 2003.
[16] W.D. Nooy, Exploratory Network Analysis with Pajek, New York: Cambridge University Press, 2005.
[17] B. Satzger, M. Endres, and W. Kießling, “A Preference-Based Recommender System,” in Bauknecht, K., Pröll, B., Werthner, H. (eds.) EC-Web 2006, LNCS, vol. 4082, pp. 31–40, 2006.
[18] M. E. J. Newman, “Modularity and community structure in networks,” Proceedings of the National Academy of Sciences, 103(23), pp. 8577-8582, 2006.
[19] A. Giacometti, P. Marcel, and E. Negre, “A Framework for Recommending OLAP Queries,” in International Workshop on Data Warehousing and OLAP, pp. 73–80. ACM, New York, 2008.
[20] Y.-T. Chen and P.-Y. Hsu, “Supporting Tools to Query Data in Business Intelligence Systems,” in Proc. of International Conference on Business and Information, (BAI2008), 2008.
[21] H. Jerbi, F. Rava, O. Teste, and G. Zurfluh, “Applying Recommendation Technology in OLAP Systems,” in Proc. of International Conference on Enterprise Information Systems (ICEIS2009), 2009.		zh_TW