Towards high-throughput gibbs sampling at scale: A study across storage managers

Proceedings of the ACM SIGMOD International Conference on Management of Data

Volumn , Issue , 2013, Pages 397-408

  Zhang, Ce ^a   Ré, Christopher ^a  

^a UNIVERSITY OF WISCONSIN MADISON   (United States)

Author keywords

Gibbs sampling; Scalability; Storage manager

Indexed keywords

ALGORITHMIC TECHNIQUES; BAYESIAN STATISTICAL METHOD; GIBBS SAMPLING; INSURANCE RISK MODEL; OPTIMAL APPROACHES; ORDERS OF MAGNITUDE; STATE-OF-THE-ART APPROACH; STORAGE MANAGER;

DATA PROCESSING; DIGITAL STORAGE; MANAGERS; RISK ASSESSMENT; SCALABILITY; THROUGHPUT;

SAMPLING;

EID: 84880563499     PISSN: 07308078     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2463676.2463702     Document Type: Conference Paper

References (46)

1. P. Agrawal, O. Benjelloun, A. D. Sarma, C. Hayworth, S. Nabar, T. Sugihara, and J. Widom. Trio: A system for data, uncertainty, and lineage. In VLDB, 2006.
2. K. Andreev and H. Räcke. Balanced graph partitioning. In SPAA, 2004.
3. C. Andrieu, N. de Freitas, A. Doucet, and M. I. Jordan. An Introduction to MCMC for Machine Learning. Machine Learning, 2003.
4. K. Antonio and J. Beirlant. Actuarial statistics with generalized linear mixed models. In Insurance: Mathematics and Economics, 2006.
5. L. Antova, C. Koch, and D. Olteanu. Query language support for incomplete information in the MayBMS system. In VLDB, 2007.
6. D. Barber. Bayesian Reasoning and Machine Learning. Cambridge University Press, 2012.
7. L. A. Belady. A study of replacement algorithms for a virtual-storage computer. IBM Syst. J., 1966.
8. D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent Dirichlet allocation. J. Mach. Learn. Res., 2003.
9. F. Chen, X. Feng, C. Re, and M. Wang. Optimizing statistical information extraction programs over evolving text. In ICDE, 2012.
10. N. Dalvi, C. Re, and D. Suciu. Queries and materialized views on probabilistic databases. JCSS, 2011.
11. S. Das, Y. Sismanis, K. S. Beyer, R. Gemulla, P. J. Haas, and J. McPherson. Ricardo: Integrating R and Hadoop. In SIGMOD, 2010.
12. P. Domingos and D. Lowd. Markov Logic: An Interface Layer for Artificial Intelligence. Morgan and Claypool Publishers, 2009.
13. N. Fenton and M. Neil. Combining evidence in risk analysis using Bayesian networks. Agena, 2004.
14. J. Finkel, T. Grenager, and C. Manning. Incorporating non-local information into information extraction systems by Gibbs sampling. In ACL, 2005.
15. J. Gonzalez, Y. Low, A. Gretton, and C. Guestrin. Parallel Gibbs sampling: From colored fields to thin junction trees. In AISTATS, 2011.
16. T. L. Griffiths and M. Steyvers. Finding scientific topics. PNAS, 2004.
17. J. M. E. A. Hellerstein. The MADlib analytics library: or MAD skills, the SQL. PVLDB, 2012.
18. T. Imieliński and W. Lipski, Jr. Incomplete information in relational databases. J. ACM, 1984.
19. R. Jampani, F. Xu, M. Wu, L. Perez, C. Jermaine, and P. J. Haas. The Monte Carlo database system: Stochastic analysis close to the data. TODS, 2011.
20. R. Jampani, F. Xu, M. Wu, L. L. Perez, C. M. Jermaine, and P. J. Haas. MCDB: A Monte Carlo approach to managing uncertain data. In SIGMOD, 2008.
21. D. Koller and N. Friedman. Probabilistic Graphical Models: Principles and Techniques. MIT Press, 2009.
22. J. D. Lafferty, A. McCallum, and F. C. N. Pereira. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In ICML, 2001.
23. Z. Liu, Y. Zhang, E. Y. Chang, and M. Sun. PLDA+: Parallel latent Dirichlet allocation with data placement and pipeline processing. ACM TIST., 2011.
24. Y. Low, J. Gonzalez, A. Kyrola, D. Bickson, C. Guestrin, and J. M. Hellerstein. Graphlab: A new parallel framework for machine learning. In UAI, 2010.
25. D. Lunn, D. Spiegelhalter, A. Thomas, and N. Best. The BUGS project: Evolution, critique and future directions. Statistics in Medicine, 2009.
26. A. McCallum, K. Schultz, and S. Singh. Factorie: Probabilistic programming via imperatively defined factor graphs. In NIPS, 2009.
27. D. Newman, P. Smyth, and M. Steyvers. Scalable parallel topic models. Journal of Intelligence Community Research and Development, 2006.
28. F. Niu, C. Ré, A. Doan, and J. W. Shavlik. Tuffy: Scaling up statistical inference in Markov logic networks using an RDBMS. PVLDB, 2011.
29. D. Olteanu, J. Huang, and C. Koch. Sprout: Lazy vs. eager query plans for tuple-independent probabilistic databases. In ICDE, 2009.
30. J. Pearl. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, 1st edition, Sept. 1988.
31. L. Peng, Y. Diao, and A. Liu. Optimizing probabilistic query processing on continuous uncertain data. PVLDB, 2011.
32. I. Porteous, D. Newman, A. Ihler, A. Asuncion, P. Smyth, and M. Welling. Fast collapsed Gibbs sampling for latent Dirichlet allocation. In KDD, 2008.
33. R. Ramakrishnan and J. Gehrke. Database Management Systems. Osborne/McGraw-Hill, 2000.
34. C. P. Robert and G. Casella. Monte Carlo Statistical Methods (Springer Texts in Statistics). Springer-Verlag New York, Inc., 2005.
35. A. D. Sarma, O. Benjelloun, A. Halevy, S. Nabar, and J. Widom. Representing uncertain data: Models, properties, and algorithms. The VLDB Journal, 2009.
36. S. Satpal, S. Bhadra, S. Sellamanickam, R. Rastogi, and P. Sen. Web information extraction using Markov logic networks. In WWW, 2011.
37. P. Sen, A. Deshpande, and L. Getoor. PrDB: Managing and exploiting rich correlations in probabilistic databases. The VLDB Journal, 2009.
38. A. Smola and S. Narayanamurthy. An architecture for parallel topic models. PVLDB, 2010.
39. J. R. Sobehart, R. Stein, V. Mikityanskaya, and L. Li. Moody's public firm risk model: A hybrid approach to modeling short term default risk. Moody's Investors Service, Global Credit Research, Rating Methodology, 2000.
40. C. Sutton and A. Mccallum. Collective segmentation and labeling of distant entities in information extraction. Technical Report #04-49, U. Mass, 2004.
41. M. J. Wainwright and M. I. Jordan. Graphical models, exponential families, and variational inference. Found. Trends Mach. Learn., 2008.
42. D. Z. Wang, M. J. Franklin, M. Garofalakis, J. M. Hellerstein, and M. L. Wick. Hybrid in-database inference for declarative information extraction. In SIGMOD, 2011.
43. D. Z. Wang, E. Michelakis, M. Garofalakis, and J. M. Hellerstein. BayesStore: Managing large, uncertain data repositories with probabilistic graphical models. PVLDB, 2008.
44. M. Wick, A. McCallum, and G. Miklau. Scalable probabilistic databases with factor graphs and mcmc. PVLDB, 2010.
45. T. Xu and A. T. Ihler. Multicore Gibbs sampling in dense, unstructured graphs. AISTATS, 2011.
46. J. Zhu, Z. Nie, X. Liu, B. Zhang, and J.-R. Wen. StatSnowball: A statistical approach to extracting entity relationships. In WWW, 2009.