Michael J. Pazzani

	2011
107		Pearl Pu, Michael J. Pazzani, Elisabeth André, Doug Riecken: Proceedings of the 2011 International Conference on Intelligent User Interfaces, February 13-16, 2011, Palo Alto, CA, USA ACM 2011
106		Chris Mesterharm, Michael J. Pazzani: Active learning using on-line algorithms. KDD 2011: 850-858
	2010
105		Yong Ge, Hui Xiong, Alexander Tuzhilin, Keli Xiao, Marco Gruteser, Michael J. Pazzani: An energy-efficient mobile recommender system. KDD 2010: 899-908
	2007
104		Michael J. Pazzani, Daniel Billsus: Content-Based Recommendation Systems. The Adaptive Web 2007: 325-341
103		Daniel Billsus, Michael J. Pazzani: Adaptive News Access. The Adaptive Web 2007: 550-570
	2006
102		Michael J. Pazzani: Adaptive Info - Personalizing the Wireless Web: Machine Learning 275 and Business 101. AAAI Spring Symposium: What Went Wrong and Why: Lessons from AI Research and Applications 2006: 12
101		Seth Hettich, Michael J. Pazzani: Mining for proposal reviewers: lessons learned at the national science foundation. KDD 2006: 862-871
	2005
100		Serge Abiteboul, Rakesh Agrawal, Philip A. Bernstein, Michael J. Carey, Stefano Ceri, W. Bruce Croft, David J. DeWitt, Michael J. Franklin, Hector Garcia-Molina, Dieter Gawlick, Jim Gray, Laura M. Haas, Alon Y. Halevy, Joseph M. Hellerstein, Yannis E. Ioannidis, Martin L. Kersten, Michael J. Pazzani, Michael Lesk, David Maier, Jeffrey F. Naughton, Hans-Jörg Schek, Timos K. Sellis, Avi Silberschatz, Michael Stonebraker, Richard T. Snodgrass, Jeffrey D. Ullman, Gerhard Weikum, Jennifer Widom, Stanley B. Zdonik: The Lowell database research self-assessment. Commun. ACM 48(5): 111-118 (2005)
	2004
99		Michael J. Pazzani: Machine Learning for Personalized Wireless Portals. ICTAI 2004: 3
	2003
98		Michael J. Pazzani: Adaptive Interfaces for Ubiquitous Web Access. User Modeling 2003: 1
97		Serge Abiteboul, Rakesh Agrawal, Philip A. Bernstein, Michael J. Carey, Stefano Ceri, W. Bruce Croft, David J. DeWitt, Michael J. Franklin, Hector Garcia-Molina, Dieter Gawlick, Jim Gray, Laura M. Haas, Alon Y. Halevy, Joseph M. Hellerstein, Yannis E. Ioannidis, Martin L. Kersten, Michael J. Pazzani, Michael Lesk, David Maier, Jeffrey F. Naughton, Hans-Jörg Schek, Timos K. Sellis, Avi Silberschatz, Michael Stonebraker, Richard T. Snodgrass, Jeffrey D. Ullman, Gerhard Weikum, Jennifer Widom, Stanley B. Zdonik: The Lowell Database Research Self Assessment CoRR cs.DB/0310006: (2003)
	2002
96		Michael J. Pazzani: Commercial Applications of Machine Learning for Personalized Wireless Portals. PRICAI 2002: 1-5
95		Selina Chu, Eamonn J. Keogh, David Hart, Michael J. Pazzani: Iterative Deepening Dynamic Time Warping for Time Series. SDM 2002
94		Kaushik Chakrabarti, Eamonn J. Keogh, Sharad Mehrotra, Michael J. Pazzani: Locally adaptive dimensionality reduction for indexing large time series databases. ACM Trans. Database Syst. 27(2): 188-228 (2002)
93		Michael J. Pazzani, Daniel Billsus: Adaptive Web Site Agents. Autonomous Agents and Multi-Agent Systems 5(2): 205-218 (2002)
92		Daniel Billsus, Clifford Brunk, Craig Evans, Brian Gladish, Michael J. Pazzani: Adaptive interfaces for ubiquitous web access. Commun. ACM 45(5): 34-38 (2002)
91		Eamonn J. Keogh, Michael J. Pazzani: Learning the Structure of Augmented Bayesian Classifiers. International Journal on Artificial Intelligence Tools 11(4): 587-601 (2002)
	2001
90		Eamonn J. Keogh, Selina Chu, David Hart, Michael J. Pazzani: An Online Algorithm for Segmenting Time Series. ICDM 2001: 289-296
89		Eamonn J. Keogh, Selina Chu, Michael J. Pazzani: Ensemble-index: a new approach to indexing large databases. KDD 2001: 117-125
88		Eamonn J. Keogh, Kaushik Chakrabarti, Sharad Mehrotra, Michael J. Pazzani: Locally Adaptive Dimensionality Reduction for Indexing Large Time Series Databases. SIGMOD Conference 2001: 151-162
87		George Buchanan, Sarah Farrant, Matt Jones, Harold W. Thimbleby, Gary Marsden, Michael J. Pazzani: Improving mobile internet usability. WWW 2001: 673-680
86		Stephen D. Bay, Michael J. Pazzani: Detecting Group Differences: Mining Contrast Sets. Data Min. Knowl. Discov. 5(3): 213-246 (2001)
85		Eamonn J. Keogh, Kaushik Chakrabarti, Michael J. Pazzani, Sharad Mehrotra: Dimensionality Reduction for Fast Similarity Search in Large Time Series Databases. Knowl. Inf. Syst. 3(3): 263-286 (2001)
84		Geoffrey I. Webb, Michael J. Pazzani, Daniel Billsus: Machine Learning for User Modeling. User Model. User-Adapt. Interact. 11(1-2): 19-29 (2001)
	2000
83		Stephen D. Bay, Michael J. Pazzani: Characterizing Model Erros and Differences. ICML 2000: 49-56
82		Michael J. Pazzani: Representation of electronic mail filtering profiles: a user study. IUI 2000: 202-206
81		Daniel Billsus, Michael J. Pazzani, James Chen: A learning agent for wireless news access. IUI 2000: 33-36
80		Eamonn J. Keogh, Michael J. Pazzani: Scaling up dynamic time warping for datamining applications. KDD 2000: 285-289
79		Eamonn J. Keogh, Michael J. Pazzani: A Simple Dimensionality Reduction Technique for Fast Similarity Search in Large Time Series Databases. PAKDD 2000: 122-133
78		Koji Miyahara, Michael J. Pazzani: Collaborative Filtering with the Simple Bayesian Classifier. PRICAI 2000: 679-689
77		Michael J. Pazzani: Knowledge discovery from data? IEEE Intelligent Systems 15(2): 10-13 (2000)
76		Michael J. Pazzani: Learning with Globally Predictive Tests. New Generation Comput. 18(1): 28-38 (2000)
75		Stephen D. Bay, Dennis F. Kibler, Michael J. Pazzani, Padhraic Smyth: The UCI KDD Archive of Large Data Sets for Data Mining Research and Experimentation. SIGKDD Explorations 2(2): 81-85 (2000)
74		Daniel Billsus, Michael J. Pazzani: User Modeling for Adaptive News Access. User Model. User-Adapt. Interact. 10(2-3): 147-180 (2000)
	1999
73		Subramani Mani, Malcolm B. Dick, Michael J. Pazzani, Evelyn L. Teng, Daniel Kempler, I. Maribell Taussig: Refinement of Neuro-psychological Tests for Dementia Screening in a Cross Cultural Population Using Machine Learning. AIMDM 1999: 326-335
72		Daniel Billsus, Michael J. Pazzani: A Personal News Agent That Talks, Learns and Explains. Agents 1999: 268-275
71		Michael J. Pazzani, Daniel Billsus: Adaptive Web Site Agents. Agents 1999: 394-395
70		Stephen D. Bay, Michael J. Pazzani: Detecting Change in Categorical Data: Mining Contrast Sets. KDD 1999: 302-306
69		Eamonn J. Keogh, Michael J. Pazzani: Scaling up Dynamic Time Warping to Massive Dataset. PKDD 1999: 1-11
68		Eamonn J. Keogh, Michael J. Pazzani: Relevance Feedback Retrieval of Time Series Data. SIGIR 1999: 183-190
67		Eamonn J. Keogh, Michael J. Pazzani: An Indexing Scheme for Fast Similarity Search in Large Time Series Databases. SSDBM 1999: 56-67
66		Richard H. Lathrop, Nicholas R. Steffen, Miriam P. Raphael, Sophia Deeds-Rubin, Michael J. Pazzani, Paul J. Cimoch, Darryl M. See, Jeremiah G. Tilles: Knowledge-Based Avoidance of Drug-Resistant HIV Mutants. AI Magazine 20(1): 13-25 (1999)
65		Michael J. Pazzani: A Framework for Collaborative, Content-Based and Demographic Filtering. Artif. Intell. Rev. 13(5-6): 393-408 (1999)
64		Subramani Mani, William Rodman Shankle, Malcolm B. Dick, Michael J. Pazzani: Two-Stage Machine Learning model for guideline development. Artificial Intelligence in Medicine 16(1): 51-71 (1999)
63		Richard H. Lathrop, Michael J. Pazzani: Combinatorial Optimization in Rapidly Mutating Drug-Resistant Viruses. J. Comb. Optim. 3(2-3): 301-320 (1999)
62		Christopher J. Merz, Michael J. Pazzani: A Principal Components Approach to Combining Regression Estimates. Machine Learning 36(1-2): 9-32 (1999)
61		Ian Soboroff, Charles K. Nicholas, Michael J. Pazzani: Workshop on Recommender Systems: Algorithms and Evaluation. SIGIR Forum 33(1): 36-43 (1999)
	1998
60		Richard H. Lathrop, Nicholas R. Steffen, Miriam P. Raphael, Sophia Deeds-Rubin, Michael J. Pazzani, Paul J. Cimoch, Darryl M. See, Jeremiah G. Tilles: Knowledge-Based Avoidance of Drug-Resistant HIV Mutants. AAAI/IAAI 1998: 1071-1078
59		Geoffrey I. Webb, Michael J. Pazzani: Adjusted Probability Naive Bayesian Induction. Australian Joint Conference on Artificial Intelligence 1998: 285-295
58		Michael J. Pazzani: Learning with Globally Predictive Tests. Discovery Science 1998: 220-231
57		Daniel Billsus, Michael J. Pazzani: Learning Collaborative Information Filters. ICML 1998: 46-54
56		Eamonn J. Keogh, Michael J. Pazzani: An Enhanced Representation of Time Series Which Allows Fast and Accurate Classification, Clustering and Relevance Feedback. KDD 1998: 239-243
	1997
55		Subramani Mani, Michael J. Pazzani, John West: Knowledge Discovery from a Breast Cancer Database. AIME 1997: 130-133
54		William Rodman Shankle, Subramani Mani, Michael J. Pazzani, Padhraic Smyth: Detecting Very Early Stages of Dementia from Normal Aging with Machine Learning Methods. AIME 1997: 73-85
53		Michael J. Pazzani, Subramani Mani, William Rodman Shankle: Beyond Concise and Colorful: Learning Intelligible Rules. KDD 1997: 235-238
52		Mark S. Ackerman, Brian Starr, Michael J. Pazzani: The Do-I-Care Agent: Effective Social Discovery and Filtering on the Web. RIAO 1997: 17-32
51		Mark S. Ackerman, Daniel Billsus, Scott Gaffney, Seth Hettich, Gordon Khoo, Dong Joon Kim, Raymond Klefstad, Charles Lowe, Alexius Ludeman, Jack Muramatsu, Kazuo Omori, Michael J. Pazzani, Douglas Semler, Brian Starr, Paul Yap: Learning Probabilistic User Profiles: Applications for Finding Interesting Web Sites, Notifying Users of Relevant Changes to Web Pages, and Locating Grant Opportunities. AI Magazine 18(2): 47-56 (1997)
50		Michael J. Pazzani, Daniel Billsus: Learning and Revising User Profiles: The Identification of Interesting Web Sites. Machine Learning 27(3): 313-331 (1997)
49		Pedro Domingos, Michael J. Pazzani: On the Optimality of the Simple Bayesian Classifier under Zero-One Loss. Machine Learning 29(2-3): 103-130 (1997)
	1996
48		Michael J. Pazzani, Jack Muramatsu, Daniel Billsus: Syskill & Webert: Identifying Interesting Web Sites. AAAI/IAAI, Vol. 1 1996: 54-61
47		Pedro Domingos, Michael J. Pazzani: Simple Bayesian Classifiers Do Not Assume Independence. AAAI/IAAI, Vol. 2 1996: 1386
46		Pedro Domingos, Michael J. Pazzani: Beyond Independence: Conditions for the Optimality of the Simple Bayesian Classifier. ICML 1996: 105-112
45		Christopher J. Merz, Michael J. Pazzani: Combining Neural Network Regression Estimates with Regularized Linear Weights. NIPS 1996: 564-570
44		Christopher J. Merz, Michael J. Pazzani, Andrea Pohoreckyj Danyluk: Tuning Numeric Parameters to Troubleshoot a Telephone-Network Loop. IEEE Expert 11(1): 44-49 (1996)
43		Michael J. Pazzani: Review of "Inductive Logic Programming: Techniques and Applications" by Nada Lavrac, Saso Dzeroski. Machine Learning 23(1): 103-108 (1996)
42		Kamal M. Ali, Michael J. Pazzani: Error Reduction through Learning Multiple Descriptions. Machine Learning 24(3): 173-202 (1996)
	1995
41		Takefumi Yamazaki, Michael J. Pazzani, Christopher J. Merz: Learning Hierarchies from Ambiguous Natural Language Data. ICML 1995: 575-583
40		Clifford Brunk, Michael J. Pazzani: A Lexical Based Semantic Bias for Theory Revision. ICML 1995: 81-89
39		Michael J. Pazzani: An Iterative Improvement Approach for the Discretization of Numeric Attributes in Bayesian Classifiers. KDD 1995: 228-233
38		Takefumi Yamazaki, Michael J. Pazzani, Christopher J. Merz: Acquiring and updating hierarchical knowledge for machine translation based on a clustering technique. Learning for Natural Language Processing 1995: 329-342
	1994
37		Patrick M. Murphy, Michael J. Pazzani: Revision of Production System Rule-Bases. ICML 1994: 199-207
36		Michael J. Pazzani, Christopher J. Merz, Patrick M. Murphy, Kamal Ali, Timothy Hume, Clifford Brunk: Reducing Misclassification Costs. ICML 1994: 217-225
35		Kamal Ali, Clifford Brunk, Michael J. Pazzani: On Learning Multiple Descriptions of a Concept. ICTAI 1994: 476-483
34		Christopher J. Merz, Michael J. Pazzani: Parameter Tuning for the MAX Expert System. ICTAI 1994: 632-639
33		Giovanni Semeraro, Floriana Esposito, Donato Malerba, Clifford Brunk, Michael J. Pazzani: Avoiding Non-Termination when Learning Logical Programs: A Case Study with FOIL and FOCL. LOPSTR 1994: 183-198
32		Patrick M. Murphy, Michael J. Pazzani: Exploring the Decision Forest: An Empirical Investigation of Occam's Razor in Decision Tree Induction. J. Artif. Intell. Res. (JAIR) 1: 257-275 (1994)
31		Michael J. Pazzani: Guest Editor's Introduction. Machine Learning 16(1-2): 7-9 (1994)
	1993
30		Michael J. Pazzani, Clifford Brunk: Finding Accurate Frontiers: A Knowledge-Intensive Approach to Relational Learning. AAAI 1993: 328-334
29		Kamal M. Ali, Michael J. Pazzani: HYDRA: A Noise-tolerant Relational Concept Learning Algorithm. IJCAI 1993: 1064-1071
28		James Wogulis, Michael J. Pazzani: A Methodology for Evaluating Theory Revision Systems: Results with Audrey II. IJCAI 1993: 1128-1134
27		Michael J. Pazzani: A Reply to Cohen's Book Review of Creating a Memory of Causal Relationships. Machine Learning 10: 185-190 (1993)
26		Michael J. Pazzani: Learning Causal Patterns: Making a Transition from Data-Driven to Theory-Driven Learning. Machine Learning 11: 173-194 (1993)
	1992
25		Daniel S. Hirschberg, Michael J. Pazzani: Average Case Analysis of Learning kappa-CNF Concepts. ML 1992: 206-211
24		Michael J. Pazzani, Wendy Sarrett: A Framework for Average Case Analysis of Conjunctive Learning Algorithms. Machine Learning 9: 349-372 (1992)
23		Michael J. Pazzani, Dennis F. Kibler: The Utility of Knowledge in Inductive Learning. Machine Learning 9: 57-94 (1992)
	1991
22		Patrick M. Murphy, Michael J. Pazzani: Constructive Induction of M-of-N Terms. ML 1991: 183-187
21		Glenn Silverstein, Michael J. Pazzani: Relational Clichés: Constraining Induction During Relational Learning. ML 1991: 203-207
20		Clifford Brunk, Michael J. Pazzani: An Investigation of Noise-Tolerant Relational Concept Learning Algorithms. ML 1991: 389-393
19		Michael J. Pazzani, Clifford Brunk, Glenn Silverstein: A Knowledge-intensive Approach to Learning Relational Concepts. ML 1991: 432-436
18		Michael J. Pazzani: A Computational Theory of Learning Causal Relationships. Cognitive Science 15(3): 401-424 (1991)
	1990
17		Michael J. Pazzani: Creating a memory of causal relationships - an integration of empirical and explanation-based learning methods. Lawrence Erlbaum 1990: 1-350
16		Michael J. Pazzani, Wendy Sarrett: Average Case Analysis of Conjunctive Learning Algorithms. ML 1990: 339-347
	1989
15		Michael J. Pazzani: Detecting and Correcting Errors of Omission After Explanation-Based Learning. IJCAI 1989: 713-718
14		Wendy Sarrett, Michael J. Pazzani: One-Sided Algorithms for Integrating Empirical and Explanation-Based Learning. ML 1989: 26-28
13		Michael J. Pazzani: Explanation-Based Learning with Week Domain Theories. ML 1989: 72-74
	1988
12		Michael J. Pazzani: Integrating Explanation-Based and Empirical Learning Methods in OCCAM. EWSL 1988: 147-165
11		Michael J. Pazzani: Integrated Learning with Incorrect and Incomplete Theories. ML 1988: 291-297
	1987
10		Michael J. Pazzani, Michael G. Dyer: A Comparison of Concept Identification in Human Learning and Network Learning with the Generalized Delta Rule. IJCAI 1987: 147-150
9		Michael J. Pazzani, Michael G. Dyer, Margot Flowers: Using Prior Learning to Facilitate the Learning of New Causal Theories. IJCAI 1987: 277-279
8		Michael J. Pazzani: Creating High Level Knowledge Structures from Simple Elements. Knowledge Representation and Organization in Machine Learning 1987: 258-288
7		Michael J. Pazzani: Explanation-Based Learning for Knowledge-Based Systems. International Journal of Man-Machine Studies 26(4): 413-433 (1987)
	1986
6		Michael J. Pazzani: Refining the Knowledge Base of a Diagnostic Expert System: An Application of Failure-Driven Learning. AAAI 1986: 1029-1035
5		Michael J. Pazzani, Michael G. Dyer, Margot Flowers: The Role of Prior Causal Theories in Generalization. AAAI 1986: 545-550
	1984
4		Michael J. Pazzani: Conceptual Analysis of Garden-Path Sentences. COLING 1984: 486-490
3		Richard E. Cullingford, Michael J. Pazzani: Word-Meaning Selection in Multiprocess Language Understanding Programs. IEEE Trans. Pattern Anal. Mach. Intell. 6(4): 493-509 (1984)
	1983
2		Michael J. Pazzani: Interactive Script Instantiation. AAAI 1983: 320-326
1		Michael J. Pazzani, Carl Engelman: Knowledge Based Question Answering. ANLP 1983: 73-80

Coauthor Index

1	Serge Abiteboul	[97] [100]
2	Mark S. Ackerman	[51] [52]
3	Rakesh Agrawal	[97] [100]
4	Kamal Ali	[35] [36]
5	Kamal M. Ali	[29] [42]
6	Elisabeth André	[107]
7	Stephen D. Bay	[70] [75] [83] [86]
8	Philip A. Bernstein	[97] [100]
9	Daniel Billsus	[48] [50] [51] [57] [71] [72] [74] [81] [84] [92] [93] [103] [104]
10	Clifford Brunk	[19] [20] [30] [33] [35] [36] [40] [92]
11	George Buchanan	[87]
12	Michael J. Carey	[97] [100]
13	Stefano Ceri	[97] [100]
14	Kaushik Chakrabarti	[85] [88] [94]
15	James Chen	[81]
16	Selina Chu	[89] [90] [95]
17	Paul J. Cimoch	[60] [66]
18	W. Bruce Croft	[97] [100]
19	Richard E. Cullingford	[3]
20	Andrea Pohoreckyj Danyluk	[44]
21	David J. DeWitt	[97] [100]
22	Sophia Deeds-Rubin	[60] [66]
23	Malcolm B. Dick	[64] [73]
24	Pedro Domingos	[46] [47] [49]
25	Michael G. Dyer	[5] [9] [10]
26	Carl Engelman	[1]
27	Floriana Esposito	[33]
28	Craig Evans	[92]
29	Sarah Farrant	[87]
30	Margot Flowers	[5] [9]
31	Michael J. Franklin (Mike Franklin)	[97] [100]
32	Scott Gaffney	[51]
33	Hector Garcia-Molina	[97] [100]
34	Dieter Gawlick	[97] [100]
35	Yong Ge	[105]
36	Brian Gladish	[92]
37	Jim Gray	[97] [100]
38	Marco Gruteser	[105]
39	Laura M. Haas	[97] [100]
40	Alon Y. Halevy (Alon Y. Levy)	[97] [100]
41	David Hart	[90] [95]
42	Joseph M. Hellerstein	[97] [100]
43	Seth Hettich	[51] [101]
44	Daniel S. Hirschberg	[25]
45	Timothy Hume	[36]
46	Yannis E. Ioannidis	[97] [100]
47	Matt Jones	[87]
48	Daniel Kempler	[73]
49	Eamonn J. Keogh	[56] [67] [68] [69] [79] [80] [85] [88] [89] [90] [91] [94] [95]
50	Martin L. Kersten	[97] [100]
51	Gordon Khoo	[51]
52	Dennis F. Kibler	[23] [75]
53	Dong Joon Kim	[51]
54	Raymond Klefstad	[51]
55	Richard H. Lathrop	[60] [63] [66]
56	Michael E. Lesk (Michael Lesk)	[97] [100]
57	Charles Lowe	[51]
58	Alexius Ludeman	[51]
59	David Maier	[97] [100]
60	Donato Malerba	[33]
61	Subramani Mani	[53] [54] [55] [64] [73]
62	Gary Marsden	[87]
63	Sharad Mehrotra	[85] [88] [94]
64	Christopher J. Merz	[34] [36] [38] [41] [44] [45] [62]
65	Chris Mesterharm	[106]
66	Koji Miyahara	[78]
67	Jack Muramatsu	[48] [51]
68	Patrick M. Murphy	[22] [32] [36] [37]
69	Jeffrey F. Naughton	[97] [100]
70	Charles K. Nicholas	[61]
71	Kazuo Omori	[51]
72	Pearl Pu	[107]
73	Miriam P. Raphael	[60] [66]
74	Doug Riecken	[107]
75	Wendy Sarrett	[14] [16] [24]
76	Hans-Jörg Schek	[97] [100]
77	Darryl M. See	[60] [66]
78	Timos K. Sellis	[97] [100]
79	Giovanni Semeraro	[33]
80	Douglas Semler	[51]
81	William Rodman Shankle	[53] [54] [64]
82	Abraham Silberschatz (Avi Silberschatz)	[97] [100]
83	Glenn Silverstein	[19] [21]
84	Padhraic Smyth	[54] [75]
85	Richard T. Snodgrass	[97] [100]
86	Ian Soboroff	[61]
87	Brian Starr	[51] [52]
88	Nicholas R. Steffen	[60] [66]
89	Michael Stonebraker	[97] [100]
90	I. Maribell Taussig	[73]
91	Evelyn L. Teng	[73]
92	Harold W. Thimbleby	[87]
93	Jeremiah G. Tilles	[60] [66]
94	Alexander Tuzhilin	[105]
95	Jeffrey D. Ullman	[97] [100]
96	Geoffrey I. Webb	[59] [84]
97	Gerhard Weikum	[97] [100]
98	John West	[55]
99	Jennifer Widom	[97] [100]
100	James Wogulis	[28]
101	Keli Xiao	[105]
102	Hui Xiong	[105]
103	Takefumi Yamazaki	[38] [41]
104	Paul Yap	[51]
105	Stanley B. Zdonik	[97] [100]

Colors in the list of coauthors

Last update Sun Jun 3 16:06:10 2012 CET by the DBLP Team — Data released under the ODC-BY 1.0 license — See also our legal information page