Cognitive Applications and Their Supporting Architecture for Smart Cities

A smart city enables a new and comprehensive approach to manage infrastructural, social, and institutional aspects of an urban ecosystem. The state information on technological, economic, and social factors forms the basis for such management, which is normally tracked by sensors. It is expected that billions of sensors and controllers will be connected and share knowledge. The proliferation of sensor networks and their different characteristics pose new challenges in relation to resource management in the context of smart cities but also lead to the rise of a new type of data on social networks—location-based social network (LBSN). To tackle these challenges, this chapter proposes a cognitive architecture to enable big data applications to largely manage themselves and to deal with organization, configuration, security, and optimization. Specially, this architecture work supports anomaly detection, which is essential to ensure quality of service and reduce cost of smart city services. Meanwhile, to harness the power of LBSN, this chapter presents solutions to identify functional regions of modern cities and discovery urban patterns, which enable us to better understand complex cities and activity behaviors.

[1]  Geoffrey E. Hinton,et al.  Discovering Binary Codes for Documents by Learning Deep Generative Models , 2011, Top. Cogn. Sci..

[2]  Haimonti Dutta,et al.  Distributed Top-K Outlier Detection from Astronomy Catalogs using the DEMAC System , 2007, SDM.

[3]  Philip Chan,et al.  Learning States and Rules for Time Series Anomaly Detection , 2004, FLAIRS.

[4]  Florian Metze,et al.  Generalized radial basis function networks for classification and novelty detection: self-organization of optimal Bayesian decision , 2000, Neural Networks.

[5]  Ulrich Nehmzow,et al.  A Model of Habituation Applied to Mobile Robots , 2007 .

[6]  Stephen R. Marsland,et al.  A tale of two filters-on-line novelty detection , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[7]  Jason Weston,et al.  A unified architecture for natural language processing: deep neural networks with multitask learning , 2008, ICML '08.

[8]  Xing Xie,et al.  Discovering spatio-temporal causal interactions in traffic data streams , 2011, KDD.

[9]  Yoshua Bengio,et al.  An empirical evaluation of deep architectures on problems with many factors of variation , 2007, ICML '07.

[10]  Kyumin Lee,et al.  Exploring Millions of Footprints in Location Sharing Services , 2011, ICWSM.

[11]  Fahui Wang,et al.  Urban land uses and traffic 'source-sink areas': Evidence from GPS-enabled taxi data in Shanghai , 2012 .

[12]  David Haussler,et al.  Unsupervised learning of distributions on binary vectors using two layer networks , 1991, NIPS 1991.

[13]  Yoshua. Bengio,et al.  Learning Deep Architectures for AI , 2007, Found. Trends Mach. Learn..

[14]  Jimeng Sun,et al.  Neighborhood formation and anomaly detection in bipartite graphs , 2005, Fifth IEEE International Conference on Data Mining (ICDM'05).

[15]  Anil K. Jain,et al.  Artificial Neural Networks: A Tutorial , 1996, Computer.

[16]  Yoshua Bengio,et al.  Greedy Layer-Wise Training of Deep Networks , 2006, NIPS.

[17]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[18]  R. Sekar,et al.  A fast automaton-based method for detecting anomalous program behaviors , 2001, Proceedings 2001 IEEE Symposium on Security and Privacy. S&P 2001.

[19]  Hisashi Kashima,et al.  Eigenspace-based anomaly detection in computer systems , 2004, KDD.

[20]  Geoffrey E. Hinton,et al.  Restricted Boltzmann machines for collaborative filtering , 2007, ICML '07.

[21]  Yuichiro Anzai,et al.  Pattern Recognition and Machine Learning , 1992, Springer US.

[22]  Sanjay Chawla,et al.  Mining for Outliers in Sequential Databases , 2006, SDM.

[23]  Jussara M. Almeida,et al.  Visualizing the Invisible Image of Cities , 2012, 2012 IEEE International Conference on Green Computing and Communications.

[24]  Sanjay Chawla,et al.  On local spatial outliers , 2004, Fourth IEEE International Conference on Data Mining (ICDM'04).

[25]  Stephen R. Marsland,et al.  A Real-Time Novelty Detector for a Mobile Robot , 2000, ArXiv.

[26]  Geoffrey E. Hinton,et al.  Semantic hashing , 2009, Int. J. Approx. Reason..

[27]  Yu Zheng,et al.  Detecting collective anomalies from multiple spatio-temporal datasets across different domains , 2015, SIGSPATIAL/GIS.

[28]  Zengyou He,et al.  Mining class outliers: concepts, algorithms and applications in CRM , 2004, Expert Syst. Appl..

[29]  Daqing Zhang,et al.  Measuring social functions of city regions from large-scale taxi behaviors , 2011, 2011 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops).

[30]  Catherine Morency,et al.  Smart card data use in public transit: A literature review , 2011 .

[31]  Christos Faloutsos,et al.  FUNNEL: automatic mining of spatially coevolving epidemics , 2014, KDD.

[32]  Dietmar Bauer,et al.  Inferring land use from mobile phone activity , 2012, UrbComp '12.

[33]  Cyrus Shahabi,et al.  Crowd sensing of traffic anomalies based on human mobility and social media , 2013, SIGSPATIAL/GIS.

[34]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[35]  Jae-Gil Lee,et al.  Trajectory Outlier Detection: A Partition-and-Detect Framework , 2008, 2008 IEEE 24th International Conference on Data Engineering.

[36]  Cecilia Mascolo,et al.  Exploiting Semantic Annotations for Clustering Geographic Areas and Users in Location-based Social Networks , 2011, The Social Mobile Web.

[37]  Chang-Tien Lu,et al.  Algorithms for spatial outlier detection , 2003, Third IEEE International Conference on Data Mining.

[38]  Laura Ferrari,et al.  Discovering daily routines from Google Latitude with topic models , 2011, 2011 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops).

[39]  J J Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.

[40]  Franco Zambonelli,et al.  Extracting urban patterns from location-based social networks , 2011, LBSN '11.

[41]  Norman M. Sadeh,et al.  The Livehoods Project: Utilizing Social Media to Understand the Dynamics of a City , 2012, ICWSM.

[42]  Geoffrey E. Hinton.  A Practical Guide to Training Restricted Boltzmann Machines , 2012, Neural Networks: Tricks of the Trade.

[43]  Xing Xie,et al.  Urban computing with taxicabs , 2011, UbiComp '11.

[44]  Stephan Sigg,et al.  An Alignment Approach for Context Prediction Tasks in UbiComp Environments , 2010, IEEE Pervasive Computing.

[45]  Padhraic Smyth,et al.  Adaptive event detection with time-varying poisson processes , 2006, KDD '06.

[46]  Ahmed H. Tewfik,et al.  Robust clustering of acoustic emission signals using the Kohonen network , 2000, 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100).

[47]  Chang-Tien Lu,et al.  Spatial Weighted Outlier Detection , 2006, SDM.

[48]  Hugo Jair Escalante,et al.  A Comparison of Outlier Detection Algorithms for Machine Learning , 2005 .

[49]  Lun Wu,et al.  Intra-Urban Human Mobility and Activity Transition: Evidence from Social Media Check-In Data , 2014, PloS one.

[50]  Paul A. Crook,et al.  A Robot Implementation of a Biologically Inspired Method for Novelty Detection , 2002 .

[51]  Franco Zambonelli,et al.  Supporting location-aware services for mobile users with the whereabouts diary , 2008 .

[52]  Ashok N. Srivastava,et al.  Nonlinear gated experts for time series: discovering regimes and avoiding overfitting , 1995, Int. J. Neural Syst..

[53]  VARUN CHANDOLA,et al.  Anomaly detection: A survey , 2009, CSUR.

[54]  Geoffrey E. Hinton,et al.  Replicated Softmax: an Undirected Topic Model , 2009, NIPS.

[55]  Geoffrey E. Hinton.  Training Products of Experts by Minimizing Contrastive Divergence , 2002, Neural Computation.

[56]  Carlo Ratti,et al.  Eigenplaces: Analysing Cities Using the Space–Time Structure of the Mobile Phone Network , 2009 .

[57]  Mohamed F. Mokbel,et al.  Recommendations in location-based social networks: a survey , 2015, GeoInformatica.

[58]  Paul Smolensky,et al.  Information processing in dynamical systems: foundations of harmony theory , 1986 .

[59]  Felix Kling,et al.  When a city tells a story: urban topic analysis , 2012, SIGSPATIAL/GIS.

[60]  Zhi-Hua Zhou,et al.  iBAT: detecting anomalous taxi trajectories from GPS traces , 2011, UbiComp '11.

[61]  Sanjay Chawla,et al.  Inferring the Root Cause in Road Traffic Anomalies , 2012, 2012 IEEE 12th International Conference on Data Mining.

[62]  Muttukrishnan Rajarajan,et al.  A survey of intrusion detection techniques in Cloud , 2013, J. Netw. Comput. Appl..

[63]  K. Kadota,et al.  Detecting outlying samples in microarray data: A critical assessment of the effect of outliers on sample classification , 2003 .

[64]  Peter Norvig,et al.  The Unreasonable Effectiveness of Data , 2009, IEEE Intelligent Systems.