2012
Journal article  Open Access

A planetary nervous system for social mining and collective awareness

Giannotti F., Pedreschi D., Pentland A., Lukowicz P., Kossmann D., Crowley J., Helbing D.

Social mining  Social sensing  Computer Science - Computers and Society  Ubiquitous Computing  Materials Science(all)  Physics and Astronomy(all)  Social and Information Networks (cs.SI)  [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]  FOS: Physical sciences  General Materials Science  Planetary Nervous System  Computers and Society (cs.CY)  General Physics and Astronomy  [SPI]Engineering Sciences [physics]  Computer Science - Social and Information Networks  FOS: Computer and information sciences  Physics - Physics and Society  Physics and Society (physics.soc-ph)  Physical and Theoretical Chemistry 

We present a research roadmap of a Planetary Nervous System (PNS), capable of sensing and mining the digital breadcrumbs of human activities and unveiling the knowledge hidden in the big data for addressing the big questions about social complexity. We envision the PNS as a globally distributed, self-organizing, techno-social system for answering analytical questions about the status of world-wide society, based on three pillars: social sensing, social mining and the idea of trust networks and privacy-aware social mining. We discuss the ingredients of a science and a technology necessary to build the PNS upon the three mentioned pillars, beyond the limitations of their respective state-of-art. Social sensing is aimed at developing better methods for harvesting the big data from the techno-social ecosystem and make them available for mining, learning and analysis at a properly high abstraction level. Social mining is the problem of discovering patterns and models of human behaviour from the sensed data across the various social dimensions by data mining, machine learning and social network analysis. Trusted networks and privacy-aware social mining is aimed at creating a new deal around the questions of privacy and data ownership empowering individual persons with full awareness and control on own personal data, so that users may allow access and use of their data for their own good and the common good. The PNS will provide a goal-oriented knowledge discovery framework, made of technology and people, able to configure itself to the aim of answering about the pulse of global society. Given an analytical request, the PNS activates a process composed by a variety of interconnected tasks exploiting the social sensing and mining methods within the transparent ecosystem provided by the trusted network. The PNS we foresee is the key tool for individual and collective awareness for the knowledge society. We need such a tool for everyone to become fully aware of how powerful is the knowledge of our society we can achieve by leveraging our wisdom as a crowd, and how important is that everybody participates both as a consumer and as a producer of the social knowledge, for it to become a trustable, accessible, safe and useful public good.

Source: The European physical journal. Special topics (Online) 214 (2012): 49–75. doi:10.1140/epjst/e2012-01688-9

Publisher: Springer Verlag (distrib.),, Heidelberg , Francia


1. Alex Pentland. Society's Nervous System: Building E ective Government, Energy, and Public Health Systems. IEEE Computer 45(1), 31-38 (2012).
2. The Economist. Data, Data Everywhere. Special Report, February 25, 2010.
3. Personal Data: The Emergence of a New Asset Class. World EconomicForum, 2011. http://www3.weforum.org/docs/WEF_ITTC_PersonalDataNewAsset_Report_2011.pdf.
4. Technology Review 2008, 10 Emerging Technologies That Will Change the World, Available at http://www.technologyreview.com/article/13060/.
5. A. Pentland, Global Information Technology Report 2008-2009, World Economic Forum, pp. 75-80.
6. D. Lazer, A. Pentland et al, Life in the network: the coming age of computational social science. Science 323, (2009) 721-723.
7. C. Parent, S. Spaccapietra, C. Renso, G. Andrienko, N. Andrienko, V. Bogorny, M. Damiani, A. Gkoulalas-Divanis, J. Macedo, N. Pelekis, Y. Theodoridis, Z. Yan, Semantic Trajectories Modeling and Analysis, ACM Computing Surveys (to appear).
8. Davy Janssens, Existing challenges in travel behavior analysis and modeling solved from the perspective of large datasets: a take-o in the DATASIM project, TRB 91st Annual Meeting, 2012.
9. Y. Min, Y. Yingxiang, W. Wei, C, Jian, D. Haoyang, Multiagent-Based Simulation of Temporal-Spatial Characteristics of Activity-Travel Patterns Using Interactive Reinforcement Learning, TRB 2012.
10. D. He and A. Goker, Detecting session boundaries from web user logs, in Proc. of BCSIRSG'00, pp 57-66.
11. C. Lucchese, S. Orlando, R. Perego, F. Silvestri, and G. Tolomei. Identifying task-based sessions in search-engines query logs. WSDM 2011, 277-286, ACM.
12. G. De Francisci Morales, A. Gionis, and C. Lucchese, From chatter to headlines: harnessing the real-time web for personalized news recommendation, in Proceedings of the fth ACM international conference on Web search and data mining WSDM 2012.
13. O. Etzioni, M. Banko, M. J. Cafarella, AAAI 2006, 1517-1519.
14. M. Banko, M. J. Cafarella, S. Soderland, M. Broadhead, and O. Etzioni, Open information extraction from the web, in IJCAI 2007.
15. M. Banko and O. Etzioni, The tradeo s between open and traditional relation extraction, In the Forty Sixth Annual Meeting of the Ass. for Computational Linguistics, 2008.
16. T.M. Mitchell, J.Betteridge, A. Carlson, E.R. Hruschka Jr. and R.C. Wang, Populating the Semantic Web by Macro-Reading Internet Text, in ISWC 2009.
17. H. Poon and P. Domingos, Machine Reading: A Killer App' for Statistical Relational AI, in AAAI-2010 Workshop on Statistical Relational Arti cial Intelligence.
18. R. Navigli, P. Velardi, and S. Faralli, A Graph-based Algorithm for Inducing Lexical Taxonomies from Scratch, In IJCAI 2011
19. M. Tsytsarau, T. Palpanas, Towards a Framework for Detecting and Managing Opinion Contradictions, PhD Forum ICDM, 2011.
20. Jerald Jariyasunant et all, The Quanti ed Traveler: Using Personal Travel Data to Promote Sustainable Transport Behavior, TRB 2012.
21. L. Wu, B. N. Waber, S. Aral, E. Brynjolfsson, and A. Pentland, Mining Face-to-Face Interaction Networks using Sociometric Badges: Predicting Productivity in an IT Con guration Task, in Proceedings of the International Conference on Information Systems, Paris, France, December 14-17, 2008.
22. A. J. Quinn and B. B. Bederson, Human computation: a survey and taxonomy of a growing eld, In Proceedings of the 2011 annual conference on Human Factors in Computing Systems, CHI'11, pages 1403-1412, 2011
23. J. Howe, 2006. The rise of crowdsourcing. Wired 14 (6).
24. L. von Ahn, Computer 39, (2006) 92-94.
25. E. Law, L. von Ahn, Input-agreement: a new mechanism for collecting data using human computation games, CHI 2009, 1197-1206.
26. Franklin M.J. et al, CrowdDB: answering queries with crowdsourcing, In Proceedings of the 2011 international conference on Management of data (SIGMOD '11). ACM, New York, NY, USA, 61-72.
27. Marcus, A. et al, Crowdsourced Databases: Query Processing with People, Conference on Innovative Data Systems Research. 2011 (Asilomar, CA, 2011), 211-214.
28. Parameswaran, A. and Polyzotis N., Answering Queries using Databases, Humans and Algorithms, Conference on Innovative Data Systems Research 2011 (Asilomar, CA, 2011), 160-166.
29. D. Helbing and W. Yu, PNAS 106 (8), (2009) 3680-3685.
30. John C. Tang, Manuel Cebrin, Nicklaus A. Giacobe, Hyun-Woo Kim, Taemie Kim, Douglas Wickert, Re ecting on the DARPA Red Balloon Challenge, Commun. ACM 54(4): 78-85 (2011).
31. S. B. Shum et al., Democratising Big Data, Complexity Modelling and Collective Intelligence, this EPJ-ST Special Issue.
32. Pang-Ning Tan, Michael Steinbach, Vipin Kumar. Introduction to Data Mining. Addison Wesley, 2006.
33. Trevor Hastie, Robert Tibshirani, Jerome Friedman. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition. Springer Series in Statistics, 2009.
34. D. J. Watts and S. H. Strogatz, Nature 393, 440 (1998).
35. A. L. Barabasi, R. Albert, Science 286, 509 (1999).
36. G. Caldarelli, Scale free networks, Oxford University Press.
37. M. E. J. Newman, Networks: An Introduction, Oxford University Press, 2010.
38. D. Easley and J. Kleinberg, Networks, Crowds, and Markets: Reasoning About a Highly Connected World, Cambridge University Press, 2010.
39. S. Fortunato, Physics Report 486 (3-5), 75-174 (2010).
40. M. Coscia, F. Giannotti, D. Pedreschi, Statistical Analysis and Data Mining 4(5), 512- 546 (2011).
41. J. Kleinberg, Nature 406 (2000), 845.
42. D. Kempe, J. Kleinberg, and E. Tardos, Maximizing the spread of in uence through a social network, in Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining (KDD '03), ACM, New York, NY, USA, 137-146.
43. R. Pastor-Satorras, A. Vespignani, Phys. Rev. Lett. 86, (2001) 32003203.
44. M. J. Keeling, K. T. D. Eames, J. Royal Soc. Interface, 2005.
45. D. Liben-Nowell, J. Kleinberg, The link prediction problem for social networks, In CIKM, 2003.
46. H. Kashima, T. Kato, Yoshihiro Yamanishi, M. Sugiyama, and K. Tsuda, Link propagation: A fast semi-supervised learning algorithm for link prediction, In SIAM, 2009.
47. J. Leskovec, D. Huttenlocher, and J. Kleinberg, Predicting positive and negative links in online social networks, In WWW, 2010.
48. J. Leskovec, J. Kleinberg, and C. Faloutsos, Graphs over time: densi cation laws, shrinking diameters and possible explanations, in Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining (KDD '05), ACM, New York, NY, USA, 177-187, 2005.
49. P. Holme, J. Saramaki, Temporal Networks. eprint arXiv:1108.1780.
50. P. J. Mucha, T. Richardson, K. Macon, M. A. Porter, and J.-P. Onnela, Science 328, 876, 2010.
51. M. Berlingerio, M. Coscia, F. Giannotti, A. Monreale, D. Pedreschi, As Time Goes by: Discovering Eras in Evolving Social Networks, PAKDD 2010.
52. B.Bringmann, M. Berlingerio, F. Bonchi, A. Gionis, Learning and Predicting the Evolution of Social Networks, IEEE Intelligent Systems (EXPERT), 2010.
53. Gao Jianxi, Buldyrev Sergey V., Stanley H. Eugene, Havlin S, Nature Physics 8, 40-48 (2012).
54. M. Berlingerio, M. Coscia, F. Giannotti, A. Monreale, D. Pedreschi, Multidimensional Networks: Foundations of Structural Analysis, WWW Journal. 2012 (to appear). DOI: 10.1007/s11280-012-0190-4.
55. L. Tang and H. Liu, Relational learning via latent social dimensions, In KDD 2009.
56. B. Pang and L. Lee, Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2(1/2), (2008) 1-135.
57. A. Esuli and F. Sebastiani, Machines that learn how to code open-ended survey data. International Journal of Market Research, 52(6), (2010) 775-800.
58. D. Brockmann, L. Hufnagel, T. Geisel, The scaling laws of human travel, Nature 439, 2006.
59. M. C. Gonzalez, C. A. Hidalgo, A. L. Barabasi, Understanding human mobility patterns, Nature 454, (2008) 779-782.
60. C. Song, T. Koren, P. Wang, A. L. Barabasi, Modelling the scaling properties of human mobility, Nature Physics, 2010
61. M. Moussad, D. Helbing, and G. Theraulaz, How simple rules determine pedestrian behavior and crowd disasters. Proceedings of the National Academy of Sciences of the USA (PNAS) 108(17), (2011) 6884-6888.
62. F. Giannotti and D. Pedreschi, Mobility, Data Mining and Privacy, Springer, 2008.
63. R. Trasarti, F. Pinelli, M. Nanni, F. Giannotti, Mining mobility user pro les for car pooling,Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2011, 1190-1198.
64. F. Giannotti, M. Nanni, F. Pinelli, D. Pedreschi, Trajectory pattern mining,Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2007, 330-339.
65. F. Giannotti, M. Nanni, D. Pedreschi, F. Pinelli, C. Renso, S. Rinzivillo, R. Trasarti, Unveiling the complexity of human mobility by querying and mining massive trajectory data. The VLDB Journal 20(5), (2011) 695-719.
66. A. Monreale, F. Pinelli, R. Trasarti, F. Giannotti, WhereNext: a location predictor on trajectory pattern mining,Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2009, 637-646.
67. D. Wang, D. Pedreschi, C. Song, F. Giannotti, A. L. Barabasi, Human Mobility, Social Ties, and Link Prediction,Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2011, 1100-1108.
68. S. Jiang, J. Ferreira and M. C. Gonzalez, Clustering daily patterns of human activities in the city. Data Mining and Knowledge Discovery 25, 2012.
69. L. Ferrari, M. Mamei, Classi cation and prediction of whereabouts patterns from reality mining dataset, Pervasive and Mobile Computing, Available online 25 April 2012.
70. A. Zimmermann, S. Schonfelder, G Rindsfuser, T. Haupt, Observing the rhythms of daily life: a six-week travel diary. Transportation 29(2):95-124.
71. M. M. Gaber, A. Zaslavsky, and S. Krishnaswamy, Mining data streams: a review, SIGMOD Rec. 34, 2 (June 2005).
72. P. Samarati, L. Sweeney, Generalizing Data to Provide Anonymity when Disclosing Information, PODS 1998, 188.
73. L. Sweeney, Achieving k-Anonymity Privacy Protection Using Generalization and Suppression. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 10(5), (2002) 571-588.
74. The New York Times. A Face Is Exposed for AOL Searcher No. 4417749. August 9, 2006. http://www.nytimes.com/2006/08/09/technology/09aol.html
75. C. C. Aggarwal and P. S. Yu, Privacy-Preserving Data Mining Models and Algorithms, The Kluwer International series on advances in database systems, Volume 34, 2008.
76. F. Bonchi, E. Ferrari, Privacy-Aware Knowledge Discovery: Novel Applications and New Techniques, Chapman & Hall/CRC Data Mining and Knowledge Discovery Series, Taylor & Francis LLC 2010.
77. P. Samarati, Protecting respondents' identities in microdata release, in IEEE Transactions on Knowledge and Data Engineering (TKDE), volume 13, pages 10101027, 2001.
78. A. Machanavajjhala, D. Kifer, J. Gehrke, and M. Venkitasubramaniam, l-diversity: Privacy beyond k-anonymity, in Proceedings of the International Conference on Data Engineering (ICDE), 2006.
79. B. C. M. Fung, K. Wang, and P. S. Yu, Anonymizing classi cation data for privacy preservation. IEEE Transactions on Knowledge and Data Engineering 19, pages 711725, 2007.
80. X. Xiao and Y. Tao, Anatomy: simple and e ective privacy preservation, in Proceedings of the International Conference on Very Large Data Bases (VLDB), 139-150, 2006.
81. M. Atzori, F. Bonchi, F. Giannotti, and D. Pedreschi, Anonymity preserving pattern discovery. The International Journal on Very Large Data Bases (VLDB) 17(4), (2008) 703727.
82. V. S. Verykios, A. K. Elmagarmid, E. Bertino, Y. Saygin, and E. Dasseni, Association rule hiding. in IEEE Transactions on Knowledge and Data Engineering (TKDE) 16, 434447, 2004.
83. M. Kantarcioglu and C. Clifton, Privacy-preserving distributed mining of association rules on horizontally par- titioned data. IEEE Transactions on Knowledge and Data Engineering (TKDE), 16(9):1026-1037, 2004.
84. B. Gilburd, A. Schuste, and R. Wol , k-ttp: A new privacy model for large scale distributed environments, in Proceedings of the International Conference on Very Large Data Bases (VLDB), 563-568, 2005.
85. W. K. Wong, D. W. Cheung, E. Hung, B.Kao, and N. Mamoulis, Security in outsourcing of association rule mining, in VLDB, pages 111122, 2007.
86. F. Giannotti, L. V.S. Lakshmanan, A. Monreale, D. Pedreschi, and H. Wang. Privacypreserving data mining from outsourced databases. Computers, Privacy and Data Protection: an Element of Choice, Part 4, 411-426, Springer, 2011.
87. C. Dwork, F. McSherry, K. Nissim, and A. Smith. Calibrating noise to sensitivity in private data analysis. In Shai Halevi and Tal Rabin, editors, Theory of Cryptography, Third Theory of Cryptography Conference, TCC 2006, volume 3876 of Lecture Notes in Computer Science, pages 265284. Springer, 2006.
88. C. Dwork. Di erential privacy. In Michele Bugliesi, Bart Preneel, Vladimiro Sassone, and Ingo Wegener, editors, Automata, Languages and Programming, 33rd International Colloquium, ICALP 2006, Part II, volume 4052 of Lecture Notes in Computer Science, pages 112. Springer, 2006.
89. A. Monreale. Privacy by Design in Data Mining. PhD Thesis, University of Pisa, 2011.
90. A. Monreale et al., Movement Data Anonymity through Generalization. Transactions on Data Privacy 3(2), 91- 121, 2010.
91. Website of the Commission on the Measurement of Economic Performance and Social Progress, http://www.stiglitz-sen- toussi.fr/
92. Stiglitz and Sens Manifesto on Measuring Economic Performance and Social Progress, http://www.worldchanging.com/archives/010627.html
93. D. Helbing and S. Balietti, How to create an innovation accelerator, Eur. Phys. J. Special Topics 195, 101-136 (2011).
94. J.V. Henderson, A. Storeygard, D. N. Weil, NBER Working Paper No. w15199 (2009).
95. Google Flu Trends. http://www.google.org/flutrends/.
96. P. S. Dodds, C. M. Danforth, Journal of Happiness Studies 11, 444-456 (2010).
97. S. Golder and M.W. Macy, Science 333, 1878-1881 (2011).
98. Planetary Skin Institute (http://www.planetaryskin.org/).
99. Digital Earth project http://www.digitalearth-isde.org/.
100. D. Helbing et al., FuturICT - New science and technology to manage our complex, strongly connected world.Eur. Phys. J. Special Topics 214, nal pagination (2012)
101. Rosaria Conte et al., Manifesto of Computational Social Science. Eur. Phys. J. Special Topics 214, nal pagination (2012)
102. L.E. Cederman et al., Exploratory of Society. Eur. Phys. J. Special Topics 214, nal pagination (2012)
103. Silvano Cincotti et al., A European Economic and Financial Exploratory. Eur. Phys. J. Special Topics 214, nal pagination (2012)
104. Michael Batty et al., Smart Cities of the Future. Eur. Phys. J. Special Topics 214, nal pagination (2012)
105. Simon Buckingham Shum et al., Democratising Big Data, Complexity Modelling and Collective Intelligence. Eur. Phys. J. Special Topics 214, nal pagination (2012)
106. Donald Kossman et al., The Living Earth Simulator and the Exploratories. Eur. Phys. J. Special Topics 214, nal pagination (2012)
107. Maxi San Miguel et al., Challenges in Complex Systems Science. Eur. Phys. J. Special Topics 214, nal pagination (2012)
108. Shlomo Havlin, et al., Challenges of network science: Applications to infrastructures, climate, social systems and economics. Eur. Phys. J. Special Topics 214, nal pagination (2012)
109. Jeroen van den Hoven et al., FuturICT { The Road towards Ethical ICT. Eur. Phys. J. Special Topics 214, nal pagination (2012)
1. A. Pentland, IEEE Computer 45, 31 (2012)
2. The Economist, Data, Data Everywhere. Special Report, February 25, 2010
3. Personal Data: The Emergence of a New Asset Class. World EconomicForum, 2011. http://www3.weforum.org/docs/WEF ITTC PersonalDataNewAsset Report 2011.pdf
4. Technology Review 2008, 10 Emerging Technologies That Will Change the World, Available at http://www.technologyreview.com/article/13060/
5. A. Pentland, Global Information Technology Report 2008-2009, World Economic Forum, p. 75
6. D. Lazer, A. Pentland, et al., Science 323, 721 (2009)
7. C. Parent, S. Spaccapietra, C. Renso, G. Andrienko, N. Andrienko, V. Bogorny, M. Damiani, A. Gkoulalas-Divanis, J. Macedo, N. Pelekis, Y. Theodoridis, Z. Yan, Semantic Trajectories Modeling and Analysis, ACM Computing Surveys (to appear)
8. D. Janssens, Existing challenges in travel behavior analysis and modeling solved from the perspective of large datasets: a take-off in the DATASIM project, TRB 91st Annual Meeting, 2012
9. Y. Min, Y. Yingxiang, W. Wei, C, Jian, D. Haoyang, Multiagent-Based Simulation of Temporal-Spatial Characteristics of Activity-Travel Patterns Using Interactive Reinforcement Learning, TRB 2012
10. D. He, A. Goker, Detecting session boundaries from web user logs, in Proc. of BCSIRSG'00, p. 57
11. C. Lucchese, S. Orlando, R. Perego, F. Silvestri, G. Tolomei, Identifying task-based sessions in search-engines query logs. WSDM 2011, 277-286, ACM
12. G. De Francisci Morales, A. Gionis, and C. Lucchese, From chatter to headlines: harnessing the real-time web for personalized news recommendation, in Proceedings of the fifth ACM international conference on Web search and data mining WSDM 2012
13. O. Etzioni, M. Banko, M.J. Cafarella, AAAI 2006, 1517
14. M. Banko, M.J. Cafarella, S. Soderland, M. Broadhead, O. Etzioni, Open information extraction from the web, in IJCAI 2007
15. M. Banko, O. Etzioni, The tradeoffs between open and traditional relation extraction, In the Forty Sixth Annual Meeting of the Ass. for Computational Linguistics, 2008
16. T.M. Mitchell, J.Betteridge, A. Carlson, E.R. Hruschka Jr., R.C. Wang, Populating the Semantic Web by Macro-Reading Internet Text, in ISWC 2009
17. H. Poon, P. Domingos, Machine Reading: A Killer App' for Statistical Relational AI, in AAAI-2010 Workshop on Statistical Relational Artificial Intelligence
18. R. Navigli, P. Velardi, S. Faralli, A Graph-based Algorithm for Inducing Lexical Taxonomies from Scratch, In IJCAI 2011
19. M. Tsytsarau, T. Palpanas, PhD Forum ICDM, 2011
20. Jerald Jariyasunant, et al., The Quantified Traveler: Using Personal Travel Data to Promote Sustainable Transport Behavior, TRB 2012
21. L. Wu, B.N. Waber, S. Aral, E. Brynjolfsson, A. Pentland, Mining Face-to-Face Interaction Networks using Sociometric Badges: Predicting Productivity in an IT Configuration Task, in Proceedings of the International Conference on Information Systems, Paris, France, December 14-17, 2008
22. A.J. Quinn, B.B. Bederson, Proceedings of the 2011 annual conference on Human Factors in Computing Systems, CHI'11 (2011), p. 1403
23. J. Howe, Wired 14 (6) (2006)
24. L. von Ahn, Computer 39, 92 (2006)
25. E. Law, L. von Ahn, Input-agreement: a new mechanism for collecting data using human computation games, CHI 2009, 1197
26. M.J. Franklin, et al., Proceedings of the 2011 international conference on Management of data (SIGMOD '11), ACM, New York, NY, USA, 61
27. A. Marcus, et al., Crowdsourced Databases: Query Processing with People, Conference on Innovative Data Systems Research. 2011 (Asilomar, CA, 2011), 211
28. A. Parameswaran, N. Polyzotis, Answering Queries using Databases, Humans and Algorithms, Conference on Innovative Data Systems Research 2011 (Asilomar, CA, 2011), p. 160
29. D. Helbing, W. Yu, PNAS 106, 3680 (2009)
30. J.C. Tang, M. Cebrin, N.A. Giacobe, H.-W. Kim, T. Kim, D. Wickert, Commun. ACM 54, 78 (2011)
31. S.B. Shum, et al., Eur. Phys. J. Special Topics 214, 109 (2012)
32. P.-N. Tan, Michael Steinbach, Vipin Kumar. Introduction to Data Mining (Addison Wesley, 2006)
33. T. Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics, 2009)
34. D.J. Watts, S.H. Strogatz, Nature 393, 440 (1998)
35. A.L. Barabasi, R. Albert, Science 286, 509 (1999)
36. G. Caldarelli, Scale free networks (Oxford University Press)
37. M.E.J. Newman, Networks: An Introduction (Oxford University Press, 2010)
38. D. Easley, J. Kleinberg, Networks, Crowds, and Markets: Reasoning About a Highly Connected World (Cambridge University Press, 2010)
39. S. Fortunato, Physics Report 486, 75 (2010)
40. M. Coscia, F. Giannotti, D. Pedreschi, Stat. Anal. Data Mining 4, 512 (2011)
41. J. Kleinberg, Nature 406, 845 (2000)
42. D. Kempe, J. Kleinberg, E. Tardo¨s, Maximizing the spread of influence through a social network, in Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining (KDD '03), ACM, New York, NY, USA, 137
43. R. Pastor-Satorras, A. Vespignani, Phys. Rev. Lett. 86, 3200 (2001)
44. M.J. Keeling, K.T.D. Eames, J. Royal Soc. Interface, 2005
45. D. Liben-Nowell, J. Kleinberg, In CIKM, 2003
46. H. Kashima, T. Kato, Yoshihiro Yamanishi, M. Sugiyama, K. Tsuda, In SIAM, 2009
47. J. Leskovec, D. Huttenlocher, J. Kleinberg, Predicting positive and negative links in online social networks, In WWW, 2010
48. J. Leskovec, J. Kleinberg, C. Faloutsos, Graphs over time: densification laws, shrinking diameters and possible explanations, in Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining (KDD '05), ACM, New York, NY, USA, 177, 2005
49. P. Holme, J. Saramaki, Temporal Networks [eprint arXiv:1108.1780]
50. P.J. Mucha, T. Richardson, K. Macon, M.A. Porter, J.-P. Onnela, Science 328, 876 (2010)
51. M. Berlingerio, M. Coscia, F. Giannotti, A. Monreale, D. Pedreschi, As Time Goes by: Discovering Eras in Evolving Social Networks, PAKDD 2010
52. B. Bringmann, M. Berlingerio, F. Bonchi, A. Gionis, Learning and Predicting the Evolution of Social Networks, IEEE Intelligent Systems (EXPERT), 2010
53. G. Jianxi, B. Sergey V., S.H. Eugene, S. Havlin, Nat. Phys. 8, 40 (2012)
54. M. Berlingerio, M. Coscia, F. Giannotti, A. Monreale, D. Pedreschi, Multidimensional Networks: Foundations of Structural Analysis, WWW Journal (2012) (to appear) doi: 10.1007/s11280-012-0190-4
55. L. Tang, H. Liu, Relational learning via latent social dimensions, In KDD 2009
56. B. Pang, L. Lee, Found. Trends Inf. Retrieval 2, 1 (2008)
57. A. Esuli, F. Sebastiani, Int. J. Market Res. 52, 775 (2010)
58. D. Brockmann, L. Hufnagel, T. Geisel, Nature 439 (2006)
59. M.C. Gonzalez, C.A. Hidalgo, A.L. Baraba´si, Nature 454, 779 (2008)
60. C. Song, T. Koren, P. Wang, A.L. Barabasi, Modelling the scaling properties of human mobility, Nature Physics (2010)
61. M. Moussad, D. Helbing, G. Theraulaz, Proc. Nat. Acad. Sci. USA (PNAS) 108, 6884 (2011)
62. F. Giannotti D. Pedreschi, Mobility, Data Mining and Privacy (Springer, 2008)
63. R. Trasarti, F. Pinelli, M. Nanni, F. Giannotti, Mining mobility user profiles for car pooling, Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2011, 1190
64. F. Giannotti, M. Nanni, F. Pinelli, D. Pedreschi, Trajectory pattern mining, Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2007, 330
65. F. Giannotti, M. Nanni, D. Pedreschi, F. Pinelli, C. Renso, S. Rinzivillo, R. Trasarti, VLDB J. 20, 695 (2011)
66. A. Monreale, F. Pinelli, R. Trasarti, F. Giannotti, WhereNext: a location predictor on trajectory pattern mining, Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2009, 637
67. D. Wang, D. Pedreschi, C. Song, F. Giannotti, A.L. Baraba´si, Human Mobility, Social Ties, and Link Prediction, Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2011, 1100
68. S. Jiang, J. Ferreira, M.C. Gonza´lez, Data Mining Knowledge Discovery 25, 2012
69. L. Ferrari, M. Mamei, Classification and prediction of whereabouts patterns from reality mining dataset, Pervasive and Mobile Computing, Available online 25 April 2012
70. A. Zimmermann, S. Schonfelder, G. Rindsfuser, T. Haupt, Transportation 29, 95
71. M.M. Gaber, A. Zaslavsky, S. Krishnaswamy, Mining data streams: a review, SIGMOD Rec. 34, 2 (June 2005)
72. P. Samarati, L. Sweeney, Generalizing Data to Provide Anonymity when Disclosing Information, PODS 1998, 188
73. L. Sweeney, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 10, 571 (2002)
74. The New York Times, A Face Is Exposed for AOL Searcher No. 4417749. August 9, 2006. http://www.nytimes.com/2006/08/09/technology/09aol.html
75. C.C. Aggarwal, P.S. Yu, Privacy-Preserving Data Mining Models and Algorithms, The Kluwer International series on advances in database systems, vol. 34 (2008)
76. F. Bonchi, E. Ferrari, Privacy-Aware Knowledge Discovery: Novel Applications and New Techniques, Chapman & Hall/CRC Data Mining and Knowledge Discovery Series, Taylor & Francis LLC 2010
77. P. Samarati, IEEE Trans. Knowledge Data Eng. (TKDE) 13, 1010 (2001)
78. A. Machanavajjhala, D. Kifer, J. Gehrke, M. Venkitasubramaniam, l-diversity: Privacy beyond k-anonymity, in Proceedings of the International Conference on Data Engineering (ICDE) (2006)
79. B.C.M. Fung, K. Wang, P.S. Yu, IEEE Trans. Knowledge Data Eng. 19, 711 (2007)
80. X. Xiao, Y. Tao, Anatomy: simple and effective privacy preservation, in Proceedings of the International Conference on Very Large Data Bases (VLDB), 139 (2006)
81. M. Atzori, F. Bonchi, F. Giannotti, D. Pedreschi, Int. J. Very Large Data Bases (VLDB) 17, 703 (2008)
82. V.S. Verykios, A.K. Elmagarmid, E. Bertino, Y. Saygin, E. Dasseni, IEEE Trans. Knowledge Data Eng. (TKDE) 16, 434 (2004)
83. M. Kantarcioglu C. Clifton, IEEE Trans. Knowledge Data Eng. (TKDE), 16, 1026 (2004)
84. B. Gilburd, A. Schuste, R. Wolff, k-ttp: A new privacy model for large scale distributed environments, in Proceedings of the International Conference on Very Large Data Bases (VLDB), 563 (2005)
85. W.K. Wong, D.W. Cheung, E. Hung, B. Kao, N. Mamoulis, Security in outsourcing of association rule mining, in VLDB (2007), p. 111122
86. F. Giannotti, L.V.S. Lakshmanan, A. Monreale, D. Pedreschi, and H. Wang. Privacypreserving data mining from outsourced databases. Computers, Privacy and Data Protection: an Element of Choice, Part 4 (Springer, 2011), p. 411
87. C. Dwork, F. McSherry, K. Nissim, A. Smith. Calibrating noise to sensitivity in private data analysis. In Shai Halevi and Tal Rabin, editors, Theory of Cryptography, Third Theory of Cryptography Conference, TCC 2006, vol. 3876 of Lecture Notes in Computer Science (Springer, 2006), p. 265284
88. C. Dwork, Differential privacy, In Michele Bugliesi, Bart Preneel, Vladimiro Sassone, and Ingo Wegener, editors, Automata, Languages and Programming, 33rd International Colloquium, ICALP 2006, Part II, vol. 4052 of Lecture Notes in Computer Science (Springer, 2006), p. 112
89. A. Monreale, Privacy by Design in Data Mining, Ph.D. thesis, University of Pisa, 2011
90. A. Monreale, et al., Trans. Data Privacy 3, 91 (2010)
91. Website of the Commission on the Measurement of Economic Performance and Social Progress, http://www.stiglitz-sen-fitoussi.fr/
93. D. Helbing, S. Balietti, Eur. Phys. J. Special Topics 195, 101 (2011)
94. J.V. Henderson, A. Storeygard, D. N. Weil, NBER Working Paper No. w15199 (2009)
95. Google Flu Trends. http://www.google.org/flutrends/
96. P.S. Dodds, C.M. Danforth, J. Happiness Studies 11, 444 (2010)
97. S. Golder, M.W. Macy, Science 333, 1878 (2011)
98. Planetary Skin Institute, http://www.planetaryskin.org/
99. Digital Earth project, http://www.digitalearth-isde.org/
100. D. Helbing, et al., Eur. Phys. J. Special Topics 214, 41 (2012)
101. R. Conte, et al., Eur. Phys. J. Special Topics 214, 325 (2012)
102. L.E. Cederman, et al., Eur. Phys. J. Special Topics 214, 347 (2012)
103. S. Cincotti, et al., Eur. Phys. J. Special Topics 214, 361 (2012)
104. M. Batty, et al., Eur. Phys. J. Special Topics 214, 481 (2012)
105. S. Buckingham Shum, et al., Eur. Phys. J. Special Topics 214, 109 (2012)
106. D. Kossman, et al., Eur. Phys. J. Special Topics 214, 77 (2012)
107. M. San Miguel, et al., Eur. Phys. J. Special Topics 214, 245 (2012)
108. S. Havlin, et al., Eur. Phys. J. Special Topics 214, 273 (2012)
109. J. van den Hoven, et al., Eur. Phys. J. Special Topics 214, 153 (2012)

Metrics



Back to previous page
BibTeX entry
@article{oai:it.cnr:prodotti:216448,
	title = {A planetary nervous system for social mining and collective awareness},
	author = {Giannotti F. and Pedreschi D. and Pentland A. and Lukowicz P. and Kossmann D. and Crowley J. and Helbing D.},
	publisher = {Springer Verlag (distrib.),, Heidelberg , Francia},
	doi = {10.1140/epjst/e2012-01688-9 and 10.3929/ethz-b-000061808 and 10.48550/arxiv.1304.3700},
	journal = {The European physical journal. Special topics (Online)},
	volume = {214},
	pages = {49–75},
	year = {2012}
}

FUTURICT
The FuturICT Knowledge Accelerator: Creating Socially Interactive Information Technologies for a Sustainable Future


OpenAIRE