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City digital pulse: a cloud based heterogeneous data analysis platform

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Abstract

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.

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Notes

  1. http://citypulse1.site.uottawa.ca

  2. http://citydigitalpulse.us-west-2.elasticbeanstalk.com

  3. http://sentiwordnet.isti.cnr.it/

  4. http://sentistrength.wlv.ac.uk/

  5. https://wordnet.princeton.edu/

  6. http://alt.qcri.org/semeval2016/

  7. http://www.redblobgames.com/grids/hexagons/

  8. https://aws.amazon.com/

  9. http://citypulse1.site.uottawa.ca

  10. http://citydigitalpulse.us-west-2.elasticbeanstalk.com

References

  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 169–173

  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 MM

  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):171–209

    Article  Google Scholar 

  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 1–24

  5. Castro1 M, Jara1 AJ, Skarmeta AFG (2013) Smart lighting solutions for smart cities. In: International conference on advanced information networking and applications workshops

  6. Chen H, Chiang RH, Storey VC (2012) Business intelligence and analytics: from big data to big impact. MIS Q 36(4):1165–1188

    Google Scholar 

  7. Chen T, Lu D, Kan MY, Cui P (2013) Understanding and classifying image tweets. In: ACM MM

  8. Chen T, SalahEldeen HM, He X, Kan MY, Lu D (2015) VELDA: relating an image tweet’s text and images. In: AAAI

  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 engineering

  10. Dave K, Lawrence S, Pennock DM (2003) Mining the peanut gallery: opinion extraction and semantic classification of product reviews. In: WWW

  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)

  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):62:1–62:17

    Article  Google Scholar 

  13. Fang X, Zhan J (2015) Sentiment analysis using product review data. J Big Data 1–14

  14. Fang Q, Sang J, Xu C, Hossain MS (2015) Relational user attribute inference in social media. IEEE Trans Multimedia 17(7):1031–1044

    Article  Google Scholar 

  15. Go A, Bhayani R, Huang L (2009) Twitter sentiment classification using distant supervision. Processing 1–6

  16. Hossain MS, Muhammad G, Al Hamid MF, Song B (2016) Audio-visual emotion recognition using big data towards 5G. Mobile Networks and Applications

  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):2105–2118

    Article  Google Scholar 

  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: ICC

  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 164:454–461

    Article  Google Scholar 

  20. Hu X, Tang L, Tang J, Liu H (2013) Exploiting social relations for sentiment analysis in microblogging. In: WSDM

  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):591–608

    Google Scholar 

  22. Jiang Y, Xu B, Xue X (2014) Predicting emotions in user-generated videos. In: AAAI

  23. Khan Z, Anjum A, Kiani SL (2013) Cloud based big data analytics for smart future cities. In: International conference on utility and cloud computing

  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 196–203

  25. Lombardia P, Giordanob S, Farouhc H, Yousefd W (2012) Modelling the smart city performance. Innov Eur J Soc Sci Res 25(2):137–149

    Article  Google Scholar 

  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 engineering

  27. Mell PM, Grance T (2011) Sp 800-145. the nist definition of cloud computing. Tech. rep., Gaithersburg, MD, United States

  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– 748

  29. Mullen T, Collier N (2004) Sentiment analysis using support vector machines with diverse information sources. In: EMNLP

  30. Niu T, Zhu S, Pang L, El-Saddik A (2016) Sentiment analysis on multi-view social data. In: MultiMedia modeling, pp 15–27

  31. Nuaimi EA, Neyadi HA, Mohamed N, Al-Jaroodi J (2015) Applications of big data to smart cities. J Internet Serv Appl 6–25

  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 Eng

  33. Pang B, Lee L (2007) Opinion mining and sentiment analysis. Found Trends Inf Retr 2(1–2):1–135

    Google Scholar 

  34. Pang B, Lee L, Vaithyanathan S (2002) Thumbs up? Sentiment classification using machine learning techniques. In: EMNLP

  35. Plotnikova N, Kohl M, Volkert K, Lerner A, Dykes N, Ermer H, Evert S (2015) KLUEless: polarity classification and association. SemEval 2015 workshop

  36. Rosenthal S, Nakov P, Kiritchenko S, Mohammad SM, Ritter A, Stoyanov V (2015) SemEval-2015 task 10: sentiment analysis in twitter. SemEval 2015 workshop

  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 workshop

  38. Saini M, Alam KM, Guo H, Alelaiwi A, Saddik AE (2016) Incloud: a cloud-based middleware for vehicular infotainment systems. Multimedia Tools Appl 1–29

  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):255–277

    Article  Google Scholar 

  40. Su K, Li J, Fu H (2011) Smart city and the applications. In: ICECC

  41. Sudhof M, Goméz Emilsson A, Maas AL, Potts C (2014) Sentiment expression conditioned by affective transitions and social forces. In: SIGKDD

  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 1–6

  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 541–546

  44. Wen Y, Zhu X, Rodrigues JJPC, Chen CW (2014) Cloud mobile media: reflections and outlook. IEEE Trans Multimedia 16(4):885–902

    Article  Google Scholar 

  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)

  46. Yang J, He S, Lin Y, Lv Z (2015) Multimedia cloud transmission and storage system based on internet of things. Multimedia Tools Appl 1–16

  47. Zanella A, Bui N, Castellani A, Vangelista L, Zorzi M (2014) Internet of things for smart cities. IEEE Internet Things J 1(1):22–32

    Article  Google Scholar 

  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 1–18

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Authors and Affiliations

  1. Multimedia Computing Research Laboratories (MCRLab), University of Ottawa, Ottawa, ON, Canada

    Zhongli Li, Shiai Zhu, Huiwen Hong, Yuanyuan Li & Abdulmotaleb El Saddik

Authors
  1. Zhongli Li
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  2. Shiai Zhu
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  3. Huiwen Hong
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  4. Yuanyuan Li
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  5. Abdulmotaleb El Saddik
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Correspondence to Shiai Zhu.

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Li, Z., Zhu, S., Hong, H. et al. City digital pulse: a cloud based heterogeneous data analysis platform. Multimed Tools Appl 76, 10893–10916 (2017). https://doi.org/10.1007/s11042-016-4038-2

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  • DOI: https://doi.org/10.1007/s11042-016-4038-2

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