网络大数据：现状与展望(7)

　　【参考文献】
　　[1] Li Gu〇-Jie， Cheng Xue -Qi. Research status and scientific thinking of big data. Bulletin of Chinese Academy of Sciences，2012，27(6) ： 647-657(inChinese)
　　[2] Big data. Nature，2008，455(7209)： 1-136
　　[3] Dealing with data. Science，2011，331(6018) ： 639-806
　　[4]Holland J. Emergence： From Chaos to Order. Redwood City，California： Addison-Wesley，1997
　　[5] Anthony J G Hey. The Fourth Paradigm： Data-intensive Scientific Discovery. Microsott Research，2009
　　[6] Phan X H，Nguyen L M， Horiguchi S. Learning to classify short and sparse text & Web with hidden topics from large- scale data collections//Proceedings of the 17th International Conference on World Wide Web. Beijing， China， 2008 ： 91-100
　　[7] SahamiM，Heilman T D. A web-based kernel function for measuring the similarity of short text snippets//Proceedings of the 15th International Conference on World Wide Web. Edinburgh，Scotland，2006： 377-386
　　[8] Efron M， Organisciak P， Fenlon K. Improving retrieval of short texts through document expansion//Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval. Portland，OR，USA， 2012： 911-920
　　[9]HongL，Ahmed A，Gurumurthy S，SmolaAJ，Tsioutsiou- liklis K. Discovering geographical topics in the twitter stream//Proceedings of the 21st International Conference on World Wide Web (WWW 2012). Lyon，France，2012：769-778
　　[10] Pozdnoukhov A，Kaiser C. Space-time dynamics of topics in streaming text//Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Location-Based Social Networks. Chicago, IL，USA，2011： 1-8
　　[11] Sun Yizhou，Norick Brandon， Han Jiawei，Yan Xifeng，Yu Philip S，Yu Xiao. Integrating meta-path selection with ^^er- guided object clustering in heterogeneous information net- works//Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Beijing，China，2012： 1348-1356
　　[12]Hastie T，Tibshirani R，Friedman J. The Elements of Statistical Learning. New York： Springer，2009
　　[13] Meinshausen N，YuB. Lasso-type recovery of sparse repre?sentations for high-dimensional data. The Annals of Statistics，2009, 37(1)： 246-270
　　[14] Zhou Ao-Ying，Jin Che-Qing，Wang Guo-Ren，Ll Jian- Zhong. A survey on the management of uncertain data. Chinese Journal of Computers? 2009, 32(1) ： 1-16(in Chinese)
　　[15] Abiteboul S，Kanellakis P C，Grahne G. On the representa?tion and querying of sets of possible worlds. Theoretical Computer Science，1991，78(1)： 158-187
　　[16] Koller D, FriedmanN. Probabilistic Graphical Models： Prin?ciples and Techniques - Adaptive Computation and Machine Learning. Cambridge，MA： The MIT Press，2009
　　[17]Aggarwal C C. Managing and Mining Uncertain Data.
　　Berlin： Springer Publishing Company，Incorporated，2009计算
　　[18] Wang Quan，Xujun，Lillang，Craswell Nick. Regularized latent semantic indexing//Proceedings of the 34th Interna?tional ACM SIGIR Conference on Research and Development m Information Retrieval (SIGIR? 11). Beijing，China，2011： 685-694
　　[19] Mackey L， Talwalkar A，Jordan M I. Divide-and-conquer matrix factorization//Proceedings of the 25 th Annual Con!er- ence on Neural In.formation Processing Systems (NIPS).
　　Granada， Spain， 2011 ： 1134-1142
　　[20] Gershman S， Blei D. A tutorial on Bayesian nonparametric models. Journal of Mathematical Psychology，2012，56(1): 1-12
　　[21] Kulis B，Jordan M I. Revisiting 是-means: New algorithms via Bayesian nonparametrics//Proceedings of the 29th Inter?national Conference on Machine Learning(ICML). Edinburgh，UK，2012
　　[22] Yaneer Bar-Yam. A mathematical theory of strong emer?gence using multiscale variety. Complexity， 2004， 9(6):15-24
　　[23] Bedau Mark A. Weak emergence. Nous， 1997， 31(sl1): 375-399
　　[24] Chalmers David J. Strong and Weak Emergence. Oxford： Oxford University Press，2002
　　[25] Ilenrya Adam Douglas，Pralat Paweg Zhangvol Cun-Quan. Emergence of segregation in evolving social networks． Proceedings of the National Academy of Sciences， 2011， 108(21)： 8605-8610
　　[26] Bergman M K． White paper：The Deep Web：Surfacing hidden value. Journal of Electronic Publishing，2001，7(1). DOI： <http://dx.doi.org/10>. 3998/3336451 0007. 104
　　[27] Florescu D，Levy A，Mendelzon A. Database techniques for the World-Wide-Web: A survey. SIGMOD Record，1998, 27(3)： 59-74
　　[28] Fan Wenfei. Data quality： Theory and practice//Proceedings of the 2012 International Conference on Web-Age Information Management(WAIM? 12). Harbin，China，2012： 1-16
　　[29] Fan Wenfei， Geerts Floris. Foundations of data quality management．Synthesis Lectures on Data Management， 2012, 4(5)： 1-217
　　[30]Fan Wenfei． Dependencies revisited for improving data quality//Proceedingsof the27th ACM SIGMOD-SIGACT- SIGART Symposium on Principles of Database Systems (PODS，08). Vancuver，Canada，2008： 159-170

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网络大数据：现状与展望(7)