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六 6.1 裡不列 1. Converge Webpage Ranking Algorithms 數數兩不裡利 PageRank 來數 2. Fixed dataset 不料 6.1 3. Personal website identification 率 2 來兩數率 1. 數力 10 論力論力 1~5 論力數 2. 率 4 4 率 76

不 Recall 來量論數 property Precision 6.1 PageRank (PR) Basic Strategy TimedPageRank (TPR) HITS PageRank (PR) SA TimedPageRank (TPR) Asynchronous Ranking (Asyn) HITS PageRank (PR) AS TimedPageRank (TPR) HITS PageRank (PR) Synchronous Ranking (Syn) TimedPageRank (TPR) HITS 6.2 CPU Intel Pentium 4 2.4G 2G 100MB 路了 Data mining Association rules Database Pattern recognition 了 6.2.4 論 SIGMOD Record 裡力論 77

6.1 PageRank 6.2 6.2.1 Converge PageRank[9] 裡 6.1 322000000 100 [9] 裡料 2900 論數 0.00001 6.2 16 0.00001 20 來行 6.1 2 78

6.2.2 Fixed Dataset Data mining Association rules Database Pattern recognition 利 ranking 力 10 論 6.2 論 Data mining Association rules Database Pattern recognition score precision score precision score precision score precision PR 31 0.5 35 0.6 36 0.6 37 0.7 TP R 31 0.4 43 0.9 39 0.8 37 0.7 H ITS 17 0 32 0.5 31 0.4 37 0.7 Asyn(SA)+PR 43 0.8 35 0.4 32 0.4 39 0.9 Asyn(SA)+TPR 44 0.8 33 0.4 34 0.5 37 0.7 Asyn(SA)+HITS 14 0 32 0.5 30 0.4 37 0.7 Asyn(AS)+PR 30 0.5 27 0.2 29 0.3 38 0.8 Asyn(AS)+TPR 28 0.4 30 0.3 36 0.4 35 0.5 Asyn(AS)+HITS 17 0 32 0.5 30 0.4 34 0.7 Syn+PR 35 0.6 40 0.6 30 0.6 39 0.9 Syn+TPR 14 0.1 29 0.3 35 0.6 36 0.6 Syn+HITS 17 0 32 0.5 32 0.3 36 0.6 6.3 量 Data mining Association rules Database Pattern recognition score precision score precision score precision score precision P R 36 0.6 39 0.6 38 0.6 44 0.8 T PR 42 0.8 40 0.6 42 0.7 40 0.7 HITS 29 0.3 36 0.6 26 0.2 37 0.5 Asyn(SA)+PR 36 0.6 40 0.6 36 0.5 35 0.4 Asyn(SA)+TPR 36 0.6 40 0.6 38 0.5 34 0.3 Asyn(SA)+HITS 35 0.6 44 0.8 32 0.3 30 0.2 Asyn(AS)+PR 36 0.6 40 0.6 34 0.5 35 0.4 Asyn(AS)+TPR 36 0.6 40 0.6 34 0.5 34 0.3 Asyn(AS)+HITS 36 0.7 41 0.7 31 0.3 36 0.5 Syn+PR 38 0.6 42 0.7 36 0.4 32 0.1 Syn+TPR 37 0.5 37 0.6 35 0.4 41 0.8 Syn+HITS 28 0.3 36 0.7 33 0.4 38 0.6 79

來論 6.2 6.3 6.4 6.5 6.4 量 Data mining Association rules Database Pattern recognition score precision score precision score precision score precision P R 42 0.8 41 0.7 34 0.4 42 0.8 TP R 38 0.7 41 0.7 31 0.3 37 0.5 H ITS 34 0.6 34 0.5 31 0.4 38 0.7 Asyn(SA)+PR 37 0.6 41 0.7 34 0.4 42 0.8 Asyn(SA)+TPR 37 0.7 41 0.7 35 0.3 38 0.6 Asyn(SA)+HITS 34 0.6 34 0.5 32 0.4 40 0.7 Asyn(AS)+PR 41 0.8 40 0.6 39 0.5 31 0.1 Asyn(AS)+TPR 42 0.8 40 0.6 39 0.5 35 0.5 Asyn(AS)+HITS 39 0.7 38 0.6 39 0.6 34 0.4 Syn+PR 43 0.9 46 0.9 37 0.5 35 0.4 Syn+TPR 31 0.5 43 0.8 36 0.6 40 0.8 Syn+HITS 33 0.6 33 0.5 37 0.5 37 0.6 6.5 量 Data mining Association rules Database Pattern recognition score precision score precision score precision score precision P R 39 0.7 41 0.6 35 0.4 40 0.7 TP R 39 0.7 41 0.6 36 0.4 40 0.7 H ITS 35 0.5 37 0.5 34 0.4 39 0.7 Asyn(SA)+PR 38 0.6 41 0.6 35 0.4 40 0.7 Asyn(SA)+TPR 39 0.7 41 0.6 36 0.4 40 0.7 Asyn(SA)+HITS 35 0.5 37 0.5 35 0.4 39 0.7 Asyn(AS)+PR 39 0.7 44 0.8 36 0.4 38 0.7 Asyn(AS)+TPR 40 0.8 44 0.8 34 0.3 36 0.5 Asyn(AS)+HITS 39 0.8 42 0.7 34 0.4 34 0.5 Syn+PR 37 0.6 44 0.8 35 0.4 38 0.6 Syn+TPR 35 0.5 39 0.6 36 0.4 37 0.5 Syn+HITS 37 0.7 37 0.5 34 0.3 38 0.6 80

Database 不理 Database 了來度異量不來 1. Data mining Synchronous Ranking + PageRank 2. Association rules Synchronous Ranking + PageRank 6.6 1 Bing Liu http://www.cs.uic.edu/~liub/ 2 Christos Faloutsos http://www-2.cs.cmu.edu/~christos/ 3 Divesh Srivastava http://www.research.att.com/~divesh/ 4 Hector Garcia-Molina http://www-db.stanford.edu/people/hector.html 5 Heikki Mannila http://www.cs.helsinki.fi/~mannila/ 6 Jeffrey F.Naughton http://www.cs.wisc.edu/~naughton/naughton.html 7 Jian Pei http://www.cse.buffalo.edu/faculty/jianpei/ 8 Jiawei Han http://www.cs.sfu.ca/~han/ 9 Johannes Gehrke http://www.cs.cornell.edu/johannes/ 10 Laks V.S.Lakshmanan http://www.cs.ubc.ca/~laks/ 11 Michael J.Carey http://www.informatik.uni-trier.de/~ley/db/indices/ a-tree/c/ Carey:Michael J=.html 12 Michael Stonebraker http://epoch.cs.berkeley.edu:8000/nasa_e2e/mike.html 13 Mohammed Javeed Zaki http://www.cs.rpi.edu/~zaki/ 14 Padhraic Smyth http://www.ics.uci.edu/~smyth/ 15 Philip S.Yu http://www.research.ibm.com/people/p/psyu/ 16 Rakesh Agrawal http://www.almaden.ibm.com/cs/people/ragrawal/ 17 Ramakrishnan Srikant http://www.almaden.ibm.com/cs/people/srikant/ 18 Surajit Chaudhuri http://research.microsoft.com/~surajitc/ 19 Usama M.Fayyad http://www-aig.jpl.nasa.gov/mls/home/fayyad/ 20 Wynne Hsu http://www.comp.nus.edu.sg/~whsu/ 81

3. Database Basic Strategy + TimedPageRank 4. Pattern recognition Basic Strategy + PageRank 6.2.3 Personal Website Identification 20 利 6.6 欄來 19 率 0.95 11 DBLP 列了料不料 6.7 Data mining 力論 1 Fast Algorithms for Mining Association Rules 2 Fast Algorithms for Mining Association Rules in Large Databases 3 High-Dimensional Similarity Joins 4 Information Sharing Across Private Databases 5 Mining Association Rules with Item Constraints 6 Mining Generalized Association Rules 7 Mining Quantitative Association Rules in Large Relational Tables 8 Mining Sequential Patterns 9 Mining Sequential Patterns:Generalizations and Performance Improvements 10 Privacy-Preserving Data Mining 6.8 Data mining 力 1 Carlo Zaniolo 2 Hector Garcia-Molina 3 Heikki Mannila 4 Jiawei Han 5 Martin C.Rinard 6 Mohammed Javeed Zaki 7 Raghu Ramakrishnan 8 Rakesh Agrawal 9 Ramakrishnan Srikant 10 Serge Abiteboul 82

6.2.4 Summary of Experiments 來 Data mining 例力來論利 Basic Strategy PageRank 力論 6.7 力 6.8 力 6.9 力 6.10 力論 1 Very Large Data Bases 2 SIGMOD Conference 6.9 Data mining 力 3 Knowledge Discovery and Data Mining 4 International Conference on Data Engineering 5 Extending Database Technology 6 Conference on Parallel and Distributed Information Systems 7 International Conference on Information and Knowledge Management 8 IEEE International Conference on Data Mining 9 Research Issues on Data Mining and Knowledge Discovery 10 Symposium on Principles of Database Systems 6.10 Data mining 力 1 IEEE Transactions on Knowledge and Data Engineering 2 Data Mining and Knowledge Discovery 3 Information Systems 4 Machine Learning 5 Journal of Parallel and Distributed Computing 6 Communications of the ACM 7 VLDB Journal 8 ACM Transactions on Database Systems 9 Data and Knowledge Engineering 10 SIGMOD Record 83

(Topic Shift) 不 Data mining 來例 4 6 論不理論 1. 不例利力論來論不 2. Citation 利來論論不 [46] 裡不類了論參 3. 來 Data mining 力料 Database 不不 Data mining Database 例兩度連論論來 SIGMOD Record 1998 年都來說力論了 42 了 39 力論 1990 年論 6.11 6.11 欄論論欄年 SIGMOD Record Database 利 Database 來 SIGMOD Record 來 10 6.11 4 11 兩 84

兩 1. SIGMOD Record 力論年都 1990 年 15 數不 2. SIGMOD Record 力論 Database Database 6.11 SIGMOD Record 力論 1 Regular Tree and Regular Hedge Languages over Unranked Alphabets (F. 01 Neven) 2 String B-tree: A New Data Structure for String Search in External Memory and 99 its Applications (N. Koudas) 3 First-order Queries on Finite Structures over the Reals (L. Libkin) 98 4 Automatic Subspace Clustering of High Dimensional Data for Data Mining 98 Applications (W. Wang) 5 Online Aggregation (F. Korn) 97 6 Transactional Client-Server Cache Consistency: Alternatives and Performance 97 (K. Vorugan) 7 Outerjoin Simplification and Reordering for Query Optimization (J. Rao) 97 8 Combining Fuzzy Information from Multiple Systems (L. Gravano) 96 9 The space complexity of approximating the frequency moments (M. 96 Garofalakis) 10 On the power of languages for the manipulation of complex values (T. Milo) 95 11 Fast algorithms for mining association rules (J. Han) 94 12 Functional Database Query Languages as Typed Lambda Calculi of Fixed 94 Order (D. Suciu) 13 Byte-aligned Bitmap Compression (T. Johnson) 94 14 ARIES: A Transactino Recovery Method Supporting Fine-Granularity Locking 92 and Partial Rollbacks Using Write-Ahead Logging (B. Salzberg) 15 Reliable Transaction Management in a Multidatabase System (P. Scheuermann) 90 85