Computer Science

Computer Science ›› 2024, Vol. 51 ›› Issue (7): 49-58.doi: 10.11896/jsjkx.221200039

• Database & Big Data & Data Science • Previous Articles     Next Articles

Development on Methods and Applications of Cognitive Computing of Urban Big Data

LIU Wei1, SUN Jia2, WANG Peng2, CHEN Yafan1   

  1. 1 School of Automation,Beijing Information and Science & Technology University,Beijing 100192,China
    2 State Key Laboratory of Management and Control for Complex Systems,Institute of Automation,Chinese Academy of Sciences,Beijing 100190,China
  • Received:2022-12-06 Revised:2024-05-09 Online:2024-07-15 Published:2024-07-10
  • About author:LIU Wei,born in 1986,Ph.D,associate professor.Her main research interests include machine learning and natural language processing.
    WANG Peng,born in 1981,Ph.D,professor,Ph.D supervisor,is a member of CCF(No.66049S).His main research interests include perception and control of robot human brain and so on.
  • Supported by:
    National Key Research and Development Program of China(2023YFC2600062).

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Abstract: Urban big data provides theory and action support for urban operation state estimation and comprehensive decision-making,while its characteristics of multi-source heterogeneity,low coupling and dynamic change bring great challenges to traditional integrated analysis.Cognitive computing is applicable to the mining of time-varying multidimensional,complex and diverse data,and can conduct adaptive learning and evolution of problems.Based on the characteristics of different types and structures of urban big data,this paper summarizes the corresponding processing methods according to the four stages of the cognitive process,and further classifies the above specific methods at the conceptual level according two the angle of knowledge driven,data driven and knowledge and data driven.Finally,it forms an organic collaboration between the methods of different driving modes in the cognitive process,and the urban big data cognitive closed-loop from perception and understanding to decision-making behavior.At the same time,it summarizes the research and development status of urban big data cognitive computing in multiple application fields.Finally,the challenges of cognitive computing in the field of urban big data construction are discussed,and the future deve-lopment trend are prospected.

Key words: Smart city, Big data, Cognitive computing, Knowledge-driven, Data-driven

CLC Number: 

  • P208
[1]WANG J Y,LI C,XIONG Z,et al.Survey of Data-Centric Smart City[J].Journal of Computer Research and Development,2014,51(2):239-259.
[2]CHEN Y,ELENEE A J,WEBER G.IBM Watson:How Cognitive Computing Can Be Applied to Big Data Challenges in Life Sciences Research [J].Clinical Therapeutics,2016,38(4):688-701.
[3]HURWITZ J,KAUFMAN M,BOWLES A.Cognitive Compu-ting and Big Data Analytics[M].John Wiley & Sons,2015.
[4]SICATO J,SALIM M M,RATHORE S,et al.Ciot-net:a scalable cognitive iot based smart city network architecture [J].Human-centric Computing and Information Sciences,2019,9(1):1-20.
[5]REYNOLDS H,FELDMAN S.Cognitive computing:Beyondthe hype [J].KM World,2014,23(7):1-22.
[6]YU H,HE D N,WANG G Y,et al.Big Data for Intelligent Decision Making[J].Acta Automatica Sinica,2020,46(5):878-896.
[7]CHEN W H,AN J Y,LI R F,et al.Review on Deep-learning-based Cognitive Computing[J].ACTC AUTOMATICA SINICA,2017,43(11):1886-1897.
[8]YU K,CHEN L,CHEN B,et al.Cognitive Technology in Task-Oriented Dialogue Systems:Concepts,Advances and Future[J].Chinese Journal of Computers,2015,38(12):2333-2348.
[9]JIAO L C,ZHAO J,YANG S Y,et al.Research Advances on Sparse Cognitive Learning,Computing and Recognition [J].Chinese Journal of Computers,2016,39(4):835-852.
[10]ZHOU G L.Study of Urban Traffic Congestion Areas Prediction by Combining Knowledge Graph and Deep Learning [D].Hefei:University of Science and Technology of China,2019.
[11]GUAN J,WANG J B,BIAN Q H.Multi-keyword StreamingParallel Retrieval Algorithm Based on Urban Security Know-ledge Graph[J].Computer Science,2019,46(2):35-41.
[12]SHAO Y T,HONG L H,CHEN J,et al.Medicine Concomitant Modeling and Risk Evaluation Based on Knowledge Graph[J].China Digital Medicine,2018,13(10):44-46.
[13]ZHAO H,YAO Q M,LI J D,et al.Meta-Graph Based Recommendation Fusion over Heterogeneous Information Networks [C]//Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.2017:635-644.
[14]VASWANI A,SHAZEER N,PARMAR N,et al.Attention isall you need [C]//Advances in Neural Information Processing Systems.2017:5998-6008.
[15]ALAHI A,GOEL K,RAMANATHAN V,et al.Social LSTM:Human Trajectory Prediction in Crowded Spaces[C]//Procee-dings of 2016 IEEE Conference on Computer Vision and Pattern Recognition(CVPR).2016:967-971.
[16]HUMPHREYS M S,TEHAN G,BAUMANN O,et al.Explaining short-term memory phenomena with an integrated episo-dic/semantic framework of long-term memory[J].Cognitive Psychology,2020,123(11):101346.
[17]JULIEN L,MELISACHEW W C.Deriving Validity Time inKnowledge Graph[C]//Companion Proceedings of the Web Conference 2018.2018:1771-1776.
[18]MU Y J,DUAN J J.Research on Design of Deep Learning Based on Cognitive Psychology Theory [J].Journal of Inner Mongolia Normal University:Education Science Edition,2012(7):5.
[19]ZHENG Y,LIU F,HSIEH H P.U-air:when urban air quality inference meets big data [C]//Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.2013:1436-1444.
[20]ZHENG Y,YI X,LI M,et al.Forecasting Fine-Grained AirQuality Based on Big Data [C]//Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.2015:2267-2276.
[21]LONIJ V P A.Cognitive Computing:Theory and Applications [M]//Volume 35:Cognitive Systems for the Food-Water-Energy Nexus.2016:255-282.
[22]PENG L,WU T,LI G S,et al.Research on Urban Diseases Based on the Fluctuation Law of Multi-source Spatial Temporal Data of Smart City [J].Geomatics World,2017,24(4):29-35.
[23]ALHUSSEIN M,MUHAMMAD G,HOSSAIN M S,et al.Cognitive ioT-cloud integration for smart healthcare:case study for epileptic seizure detection and monitoring [J].Mobile Networks and Applications,2018,23(6):1624-1635.
[24]WU Q,DING G,XU Y,et.al.Cognitive Internet of Things:A New Paradigm Beyond Connection [J].IEEE Internet of Things Journal,2014,1(2):129-143.
[25]SANGAIAHA K,GOLI A,TIRKOLAEE E B,et al.Big Data-Driven Cognitive Computing System for Optimization of Social Media Analytics [J].IEEE Access,2020,8:82215-82226.
[26]AL-FUQAHA A,KHREISHAH A,GUIZANI M,et al.To-ward better horizontal integration among IoT services [J].IEEE Communications Magazine,2015,53(9):72-79.
[27]D'ONOFRIOSARA,PORTMANN E.Cognitive Computing in Smart Cities [J].Informatik Spektrum,2017,40(1):46-57.
[28]YU Z,HAO L,YIKANG L,et al.What to do next:Modeling user behaviors by time-lstm [C]//Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence.2017:3602-3608.
[29]LI S,JIN X,XUAN Y,et al.Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting[C]//Proceedings of the 33rd International Confe-rence on Neural Information Processing Systems.2019:5243-5253.
[30]TURNER R.Perceptual change-of-mind decisions are sensitive to absolute evidence magnitude[J].Cognitive Psychology,2021,124:101358.
[31]BAKER S T,LESLIE A M,GALLISTEL C R,et al.Bayesian change-point analysis reveals developmental change in a classic theory of mind task[J].Cognitive Psychology,2016,91:124-149.
[32]TANG B,CHEN Z,HEFFERMAN G,et al.Incorporating Intelligence in Fog Computing for Big Data Analysis in Smart Ci-ties [J].IEEE Transactions on Industrial Informatics,2017,13(5):2140-2150.
[33]MARESCA P,COCCOLI M,STANGANELLI L.Cognitivecomputing in education [J].Journal of E-Learning and Know-ledge Society,2016,2(2016):55-69.
[34]ZHU J Y,ZHANG C,ZHANG H,et al.pg-Causality:Identi-fying Spatiotemporal Causal Pathways for Air Pollutants with Urban Big Data [J].IEEE Transactions on Big Data,2018,4(4):571-585.
[35]SCHLEGL T,SEEBCK P,WALDSTEIN S M,et al.Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery [C]//International Conference on Information Processing in Medical Imaging.Springer,Cham,2017.
[36]GE,S,ZHAO,S,LI,C,et al.Low-Resolution Face Recognition in the Wild via Selective Knowledge Distillation [J].IEEE Transactions on image Processing,2019,28:2051-2062.
[37]PAN S J,TSANG I W,KWOK J T,et al.Domain Adaptation via Transfer Component Analysis [J].IEEE Transactions on Neural Networks,2011,22(2):199-210.
[38]FENG S,SETOODEH P,HAYKIN S.Smart Home:Cognitive Interactive People-Centric Internet of Things [J].IEEE Communications Magazine,2017,55(2):34-39.
[39]NING W,GEGE G,BAONAN W,et al.Traffic Clustering Algorithm of Urban Data Brain Based on a Hybrid:Augmented Architecture of Quantum Annealing and Brain-Inspired Cognitive Computing [J].Journal of Tsing University(Science and Technology),2020,(6):813-825.
[40]MNIH V,KAVUKCUOGLU K,SILVER D,et al.Playing Atari with Deep Reinforcement Learning [J].arXiv:1312,5602.2013.
[41]ZHANG W,PAUDEL B,WANG L,et al.Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoning [C]//The World Wide Web Conference.2019.
[42]WANG Q,LIU J,LUO Y F,et al.Knowledge Base Completion via Coupled Path Ranking[C]//Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics(Vo-lume 1:Long Papers).2016:1308-1318.
[43]GALÁRRAGA L A,TEFLIOUDI C,HOSE K,et,al.AMIE:Association rule mining under incomplete evidence in ontological knowledge bases [C]//Proceedings of the 22nd International Conference on World Wide Web.2013:413-422.
[44]BORDES A,USUNIER N,GARCIA-DURAN A,et al.Translating Embeddings for Modeling Multi-relational Data [C]//International Conference on Neural Information Processing Systems.Curran Associates Inc.2013:2787-2795.
[45]ADORNI G,COCCOLI M,TORRE I.Semantic web and internet of things supporting enhanced learning [J].Journal of E-Learning and Knowledge Society,2012,8(8):23-32.
[46]WANG H Q.Study on semantic-based personalized clinicalpathways [D].Hangzhou:Zhejiang University,2015.
[47]COWAN N,ELLIOTT E M,SAULTS J S,et al.On the capacity of attention:Its estimation and its role in working memory and cognitive aptitudes[J].Cognitive Psychology,2005,51(1):42-100.
[48]GUPTA A,JOHNSON J,LI F F,et al.Social GAN:Socially Acceptable Trajectories with Generative Adversarial Networks [C]//2018 IEEE/CVF Conference on Computer Visionand Pattern Recognition(CVPR).IEEE,2018.
[49]PFEIFFER M,PAOLO G,SOMMER H,et al.A Data-driven Model for Interaction-aware Pedestrian Motion Prediction in Object Cluttered Environments [C]//the IEEE International Conference on Robotics and Automation.2018.
[50]LIU C Y,WANG P,XU J,et al.Automatic Dialogue Summary Generation for Customer Service [C]//Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery& Data Mining.ACM,2019.
[51]WOOJEONG J,HE J,MENG Q,et al.Recurrent Event Network:Global Structure Inference over Temporal Knowledge Graph23 [C]//ICLR-RLGM.2019.
[52]IRFAN M T,GUDIVADA V N.Cognitive Computing Applications in Education and Learning [J].Cognitive Computing:Theo-ry and Applications,Handbook of Statistics,2016,35:283-300.
[53]HUANG X,ZHANG J,LI D,et al.Knowledge graph embedding based question answering [C]//Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining.2019:105-113.
[54]HAMAGUCHI T,OIWA H,SHIMBO M,et al.KnowledgeBase Completion with Out-of-Knowledge-Base Entities:A Graph Neural Network Approach [C]//Proceedings of the 26th International Joint Conference on Artificial Intelligencem.2017,1802-1808.
[55]TENISON C,FINCHAM J M,ANDERSON J R.Phases oflearning:How skill acquisition impacts cognitive processing-Science Direct[J].Cognitive Psychology,2016,87:1-28.
[56]LILLICRAP,TIMOTHY P,et al.Continuous control with deep reinforcement learning [J].Computer Science,2015.
[57]MOHAMMADI M,FUQAHA A A.Enabling Cognitive SmartCities Using Big Data and Machine Learning:Approaches and Challenges [J].IEEE Communications Magazine,2018,56(2):94-104.
[58]PERTICONE V,TABACCHI M E.Towards the Improvement of Citizen Communication Through Computational Intelligence [M]//Towards Cognitive Cities.Springer International Publi-shing,2016.
[59]JIANG X,SU X N,TANG M W,et al.Research on Collaborative Architecture of Emergence Decision-making Knowledge Base Adapting to the Scenario Evolution and Deduction [J].Information Studies:Theory & Application,2017,40(11):67-72.
[60]MA C,WEN X X,TIAN C D.A Knowledge Graph System Architecture for City Service Resources [J].Technology of IoT & AI,2019,51(2):22-26.
[61]JING Y,YU Z,XING X.Discovering regions of different functions in a city using human mobility and POIs [C]//Proceedings of KDD.2012:186-194.
[62]GIOVANNI A,SERENA B,DIEGO B.Caddie and iwt:two different ontology-based approaches to anytime,anywhere and anybody learning [J].Journal of E-Learning and Knowledge Society,2010,6(2):53-66.
[63]ZHANG X,YANG S,SRIVASTAVA G,et al.Hybridization of cognitive computing for food services [J].Applied Soft Computing,2020,89:106051.
[64]SULTANA M,PAUL P P,GAVRILOVA M L.User Recognition From Social Behavior in Computer-Mediated Social Context [J].IEEE Transactions on Human-Machine Systems,2017(3):1-12.
[65]HORNG G J,CHENG S T.Using Intelligent VehicleInfra-structure Integration for Reducing Congestion in Smart City [J].Wireless Personal Communications:An Internaional Journal,2016,91(2):861-883.
[66]CHEN M,TIAN Y,FORTINO G,et al.Cognitive internet of vehicles [J].Computer Communications,2018,120(MAY):58-70.
[67]CHEN N C,LIU Y G,SHENG H,et al.Key Techniques andSystem for Comprehensive Decision-Making of Spatio-Temporal Informationin Smart City [J].Geomatics and Information Science of Wuhan University,2018,43(12):2278-2286.
[68]XU L D,HE W,LI S.Internet of Things in Industries:A Survey[J].IEEE Transactions on Industrial Informatics,2014,10(4):2233-2243.
[69]LIU M,HUANG J F,GAO H.An acoustic activity recognition based on deep reinforcement learning [J].Journal of Shanghai Normal University(Natural Sciences),2020,49(1):109-115.
[70]CHEN T,MA M,XU X L.Research on the Application ofBlockchain in Smart City Information Sharing and Use [J].E-government,2018(7):28-37.
[71]LI B B,WU B,CHEN P.Research on the construction of smart cities based on block chain based big data platforms [J].Finance and Economics,2020(30):2.
[72]SHI J,ZHENG P,CHANG D Y.Governance of urban publicsafety in context of big data:block chain technology enablement [J].China Safety Science Journal,2020,31(2):24-32.
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