Multimedia Tools and Applications

, Volume 76, Issue 8, pp 10893–10916 | Cite as

City digital pulse: a cloud based heterogeneous data analysis platform

  • Zhongli Li
  • Shiai Zhu
  • Huiwen Hong
  • Yuanyuan Li
  • Abdulmotaleb El Saddik


In recent years, increasing attention has been paid to developing exceptional technologies for efficiently processing massive collection of data. This is essential in the research on smart city, which involves various types of data generated by different kinds of sensors (hard and soft). In this paper, we propose a cloud-based platform named City Digital Pulse (CDP), where a unified mechanism and extensible architecture are provided to facilitate the various aspects in big data analysis, ranging from data acquisition to data visualization. We instantiate the proposed system using multi-model data collected from two social networks, namely Twitter and Instagram, which can provide instant geo-tagged data. Data analysis is performed to detect human affections from user uploaded content. The information revealed from the collected social data can be visualized at multiple dimensions through a well-designed Web application. This allows users to easily sense changes in human affective status and identify the underlying reasons. This offers priceless opportunities to improve the decision making in many critical tasks using the detected attitudes in the social messages, such as promotion strategy for companies or new policy making for the government. Our experiment results confirm the effectiveness of the proposed architecture and algorithms.


Smart sity Cloud-based system Social media Data analytics Data visualization 


  1. 1.
    Agrawal R, Kadadi A, Dai X, Andres F (2015) Challenges and opportunities with big data visualization. In: Proceedings of the 7th International Conference on Management of computational and collective intelligence in digital ecosystems. ACM, pp 169–173Google Scholar
  2. 2.
    Borth D, Ji R, Chen T, Breuel T, Chang SF (2013) Large-scale visual sentiment ontology and detectors using adjective noun pairs. In: ACM MMGoogle Scholar
  3. 3.
    Buzzi M, Buzzi M, Franchi D, Gazzè D., Iervasi G, Marchetti A, Pingitore A, Tesconi M (2014) Big data: a survey. Mobile Netw Appl 19(2):171–209CrossRefGoogle Scholar
  4. 4.
    Buzzi M, Buzzi M, Franchi D, Gazzè D., Iervasi G, Marchetti A, Pingitore A, Tesconi M (2016) Facebook: a new tool for collecting health data? Multimedia Tools Appl 1–24Google Scholar
  5. 5.
    Castro1 M, Jara1 AJ, Skarmeta AFG (2013) Smart lighting solutions for smart cities. In: International conference on advanced information networking and applications workshopsGoogle Scholar
  6. 6.
    Chen H, Chiang RH, Storey VC (2012) Business intelligence and analytics: from big data to big impact. MIS Q 36(4):1165–1188Google Scholar
  7. 7.
    Chen T, Lu D, Kan MY, Cui P (2013) Understanding and classifying image tweets. In: ACM MMGoogle Scholar
  8. 8.
    Chen T, SalahEldeen HM, He X, Kan MY, Lu D (2015) VELDA: relating an image tweet’s text and images. In: AAAIGoogle Scholar
  9. 9.
    Costa C, Santos MY (2015) Improving cities sustainability through the use of data mining in a context of big city data. In: Proceedings of the world congress on engineeringGoogle Scholar
  10. 10.
    Dave K, Lawrence S, Pennock DM (2003) Mining the peanut gallery: opinion extraction and semantic classification of product reviews. In: WWWGoogle Scholar
  11. 11.
    Dey S, Chakraborty A, Naskar S, Misra P (2012) Smart city surveillance: leveraging benefits of cloud data stores. In: IEEE 37th conference on local computer networks workshops (LCN Workshops)Google Scholar
  12. 12.
    Fan M, Sun J, Zhou B, Chen M (2016) The smart health initiative in china: the case of wuhan, hubei province. J Med Syst 40(3):62:1–62:17CrossRefGoogle Scholar
  13. 13.
    Fang X, Zhan J (2015) Sentiment analysis using product review data. J Big Data 1–14Google Scholar
  14. 14.
    Fang Q, Sang J, Xu C, Hossain MS (2015) Relational user attribute inference in social media. IEEE Trans Multimedia 17(7):1031–1044CrossRefGoogle Scholar
  15. 15.
    Go A, Bhayani R, Huang L (2009) Twitter sentiment classification using distant supervision. Processing 1–6Google Scholar
  16. 16.
    Hossain MS, Muhammad G, Al Hamid MF, Song B (2016) Audio-visual emotion recognition using big data towards 5G. Mobile Networks and ApplicationsGoogle Scholar
  17. 17.
    Hossain MS, Muhammad G, Song B, Hassan MM, Alelaiwi A, Almari A (2015) Audio-visual emotion-aware cloud gaming framework. IEEE Trans Circuits Syst Video Technol 25(12):2105–2118CrossRefGoogle Scholar
  18. 18.
    Hromic H, Phuoc DL, Serrano M, Antonic A, Zarko IP, Hayes C, Decker S (2015) Real time analysis of sensor data for the internet of things by means of clustering and event processing. In: ICCGoogle Scholar
  19. 19.
    Hsu CY, Yang CS, Yu LC, Lin CF, Yao HH, Chen DY, Lai KR, Chang PC (2015) Development of a cloud-based service framework for energy conservation in a sustainable intelligent transportation system. Int J Prod Econ 164:454–461CrossRefGoogle Scholar
  20. 20.
    Hu X, Tang L, Tang J, Liu H (2013) Exploiting social relations for sentiment analysis in microblogging. In: WSDMGoogle Scholar
  21. 21.
    Hwang D, Jung JE, Park S, Nguyen HT (2015) Social data visualization system for understanding diffusion patterns on twitter: a case study on korean enterprises. Comput Inf 33(3):591–608Google Scholar
  22. 22.
    Jiang Y, Xu B, Xue X (2014) Predicting emotions in user-generated videos. In: AAAIGoogle Scholar
  23. 23.
    Khan Z, Anjum A, Kiani SL (2013) Cloud based big data analytics for smart future cities. In: International conference on utility and cloud computingGoogle Scholar
  24. 24.
    Lê Tu’n A, Quoc HNM, Serrano M, Hauswirth M, Soldatos J, Papaioannou T, Aberer K (2012) Global sensor modeling and constrained application methods enabling cloud-based open space smart services. In: 9th international conference on ubiquitous intelligence & computing and 9th international conference on autonomic & trusted computing (UIC/ATC), 2012, pp 196–203Google Scholar
  25. 25.
    Lombardia P, Giordanob S, Farouhc H, Yousefd W (2012) Modelling the smart city performance. Innov Eur J Soc Sci Res 25(2):137–149CrossRefGoogle Scholar
  26. 26.
    Ma S, Liang Z (2015) Design and implementation of smart city big data processing platform based on distributed architecture. In: International conference on intelligent systems and knowledge engineeringGoogle Scholar
  27. 27.
    Mell PM, Grance T (2011) Sp 800-145. the nist definition of cloud computing. Tech. rep., Gaithersburg, MD, United StatesGoogle Scholar
  28. 28.
    Mukkamala RR, Sørensen JI, Hussain A, Vatrapu R (2015) Detecting corporate social media crises on facebook using social set analysis. In: 2015 IEEE international congress on big data. IEEE, pp 745– 748Google Scholar
  29. 29.
    Mullen T, Collier N (2004) Sentiment analysis using support vector machines with diverse information sources. In: EMNLPGoogle Scholar
  30. 30.
    Niu T, Zhu S, Pang L, El-Saddik A (2016) Sentiment analysis on multi-view social data. In: MultiMedia modeling, pp 15–27Google Scholar
  31. 31.
    Nuaimi EA, Neyadi HA, Mohamed N, Al-Jaroodi J (2015) Applications of big data to smart cities. J Internet Serv Appl 6–25Google Scholar
  32. 32.
    Palmieri F, Ficco M, Pardi S, Castiglione A (2016) A cloud-based architecture for emergency management and first responders localization in smart city environments. Comput Electr EngGoogle Scholar
  33. 33.
    Pang B, Lee L (2007) Opinion mining and sentiment analysis. Found Trends Inf Retr 2(1–2):1–135Google Scholar
  34. 34.
    Pang B, Lee L, Vaithyanathan S (2002) Thumbs up? Sentiment classification using machine learning techniques. In: EMNLPGoogle Scholar
  35. 35.
    Plotnikova N, Kohl M, Volkert K, Lerner A, Dykes N, Ermer H, Evert S (2015) KLUEless: polarity classification and association. SemEval 2015 workshopGoogle Scholar
  36. 36.
    Rosenthal S, Nakov P, Kiritchenko S, Mohammad SM, Ritter A, Stoyanov V (2015) SemEval-2015 task 10: sentiment analysis in twitter. SemEval 2015 workshopGoogle Scholar
  37. 37.
    Saif H, Fernandez M, He Y, Alani H (2013) Evaluation datasets for twitter sentiment analysis: a survey and a new dataset, the sts-gold. ESSEM workshopGoogle Scholar
  38. 38.
    Saini M, Alam KM, Guo H, Alelaiwi A, Saddik AE (2016) Incloud: a cloud-based middleware for vehicular infotainment systems. Multimedia Tools Appl 1–29Google Scholar
  39. 39.
    Scholl HJ, AlAwadhi S (2016) Smart governance as key to multi-jurisdictional smart city initiatives: The case of the ecitygov alliance. Soc Sci Inf 55(2):255–277CrossRefGoogle Scholar
  40. 40.
    Su K, Li J, Fu H (2011) Smart city and the applications. In: ICECCGoogle Scholar
  41. 41.
    Sudhof M, Goméz Emilsson A, Maas AL, Potts C (2014) Sentiment expression conditioned by affective transitions and social forces. In: SIGKDDGoogle Scholar
  42. 42.
    Taherkordi A, Eliassen F (2016) Scalable modeling of cloud-based iot services for smart cities. In: 2016 IEEE international conference on pervasive computing and communication workshops, percom workshops 2016, pp 1–6Google Scholar
  43. 43.
    Tedeschi A, Benedetto F (2014) A cloud-based tool for brand monitoring in social networks. In: International conference on future internet of things and cloud (FiCloud), 2014, pp 541–546Google Scholar
  44. 44.
    Wen Y, Zhu X, Rodrigues JJPC, Chen CW (2014) Cloud mobile media: reflections and outlook. IEEE Trans Multimedia 16(4):885–902CrossRefGoogle Scholar
  45. 45.
    Yamamoto S, Nakamura M, Matsumoto S (2012) Using cloud technologies for large-scale house data in smart city. In: International conference on cloud computing technology and science (CloudCom)Google Scholar
  46. 46.
    Yang J, He S, Lin Y, Lv Z (2015) Multimedia cloud transmission and storage system based on internet of things. Multimedia Tools Appl 1–16Google Scholar
  47. 47.
    Zanella A, Bui N, Castellani A, Vangelista L, Zorzi M (2014) Internet of things for smart cities. IEEE Internet Things J 1(1):22–32CrossRefGoogle Scholar
  48. 48.
    Zhao Y, Qin B, Liu T, Tang D (2014) Social sentiment sensor: a visualization system for topic detection and topic sentiment analysis on microblog. Multimedia Tools Appl 1–18Google Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Zhongli Li
    • 1
  • Shiai Zhu
    • 1
  • Huiwen Hong
    • 1
  • Yuanyuan Li
    • 1
  • Abdulmotaleb El Saddik
    • 1
  1. 1.Multimedia Computing Research Laboratories (MCRLab)University of OttawaOttawaCanada

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