Multimedia Data Mining and Analytics

This chapter gives an overview of multimedia data processing history as a sequence of disruptive innovations and identifies the trends of its future development. Multimedia data processing and mining penetrates into all spheres of human life to improve efficiency of businesses and governments, facilitate social interaction, enhance sporting and entertainment events, and moderate further innovations in science, technology and arts. The disruptive innovations in mobile, social, cognitive, cloud and organic based computing will enable the current and future maturation of multimedia data mining. The chapter concludes with an overview of the other chapters included in the book.

[1]  L Sweeney,et al.  Weaving Technology and Policy Together to Maintain Confidentiality , 1997, Journal of Law, Medicine & Ethics.

[2]  Jaeyoung Choi,et al.  Semantic Computing and Privacy: a Case Study Using Inferred Geo-Location , 2011, Int. J. Semantic Comput..

[3]  Touradj Ebrahimi,et al.  A framework for the validation of privacy protection solutions in video surveillance , 2010, 2010 IEEE International Conference on Multimedia and Expo.

[4]  Miroslav Goljan,et al.  Digital camera identification from sensor pattern noise , 2006, IEEE Transactions on Information Forensics and Security.

[5]  Jean-François Bonastre,et al.  ALIZE, a free toolkit for speaker recognition , 2005, Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005..

[6]  Andrew J. Blumberg,et al.  VPriv: Protecting Privacy in Location-Based Vehicular Services , 2009, USENIX Security Symposium.

[7]  G.E. Moore,et al.  Cramming More Components Onto Integrated Circuits , 1998, Proceedings of the IEEE.

[8]  Gerald Friedland,et al.  Cybercasing the Joint: On the Privacy Implications of Geo-Tagging , 2010, HotSec.

[9]  James Glass,et al.  Research Developments and Directions in Speech Recognition and Understanding, Part 1 , 2009 .

[10]  Michael Stonebraker,et al.  Fault-tolerance in the borealis distributed stream processing system , 2008, ACM Trans. Database Syst..

[11]  Sergey Ioffe,et al.  Probabilistic Linear Discriminant Analysis , 2006, ECCV.

[12]  Markus Jakobsson,et al.  Messin' with Texas Deriving Mother's Maiden Names Using Public Records , 2005, ACNS.

[13]  Vitaly Shmatikov,et al.  De-anonymizing Social Networks , 2009, 2009 30th IEEE Symposium on Security and Privacy.

[14]  Tetsuya Iwasaki,et al.  Serpentine locomotion with robotic snakes , 2002 .

[15]  Carlo Menon,et al.  Abigaille II: toward the development of a spider-inspired climbing robot , 2011, Robotica.

[16]  Jennifer Chu-Carroll,et al.  Building Watson: An Overview of the DeepQA Project , 2010, AI Mag..

[17]  Steven Hand,et al.  Self-adaptive and self-configured CPU resource provisioning for virtualized servers using Kalman filters , 2009, ICAC '09.

[18]  Murray Campbell,et al.  Deep Blue , 2002, Artif. Intell..

[19]  Cynthia Dwork,et al.  Differential Privacy: A Survey of Results , 2008, TAMC.

[20]  Latanya Sweeney,et al.  k-Anonymity: A Model for Protecting Privacy , 2002, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[21]  Guilherme Galante,et al.  A Survey on Cloud Computing Elasticity , 2012, 2012 IEEE Fifth International Conference on Utility and Cloud Computing.

[22]  Justin Basilico,et al.  DeepQA Jeopardy! Gamification: A Machine-Learning Perspective , 2014, IEEE Transactions on Computational Intelligence and AI in Games.

[23]  Mathias Lux,et al.  Lire: lucene image retrieval: an extensible java CBIR library , 2008, ACM Multimedia.

[24]  Irit Dinur,et al.  Revealing information while preserving privacy , 2003, PODS.

[25]  Andreas Stolcke,et al.  Generalized Linear Kernels for One-Versus-All Classification: Application to Speaker Recognition , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

[26]  Jessica Staddon,et al.  Web-Based Inference Detection , 2007, USENIX Security Symposium.

[27]  Deborah A. Frincke,et al.  Relationships and data sanitization: a study in scarlet , 2010, NSPW '10.

[28]  Alexandre M. Bayen,et al.  Virtual trip lines for distributed privacy-preserving traffic monitoring , 2008, MobiSys '08.

[29]  Lukás Burget,et al.  Discriminatively trained Probabilistic Linear Discriminant Analysis for speaker verification , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[30]  Sanjay Ghemawat,et al.  MapReduce: Simplified Data Processing on Large Clusters , 2004, OSDI.

[31]  Carman Neustaedter,et al.  Blur filtration fails to preserve privacy for home-based video conferencing , 2006, TCHI.

[32]  Charu C. Aggarwal,et al.  On k-Anonymity and the Curse of Dimensionality , 2005, VLDB.

[33]  Touradj Ebrahimi,et al.  Scrambling for Video Surveillance with Privacy , 2006, 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'06).

[34]  Vitaly Shmatikov,et al.  Robust De-anonymization of Large Sparse Datasets , 2008, 2008 IEEE Symposium on Security and Privacy (sp 2008).

[35]  G. Church,et al.  Next-Generation Digital Information Storage in DNA , 2012, Science.

[36]  Gerald Friedland,et al.  Sherlock holmes' evil twin: on the impact of global inference for online privacy , 2011, NSPW '11.

[37]  David A. Ferrucci,et al.  Introduction to "This is Watson" , 2012, IBM J. Res. Dev..

[38]  Tomoji Toriyama,et al.  Factors on the sense of privacy in video surveillance , 2006, CARPE '06.