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Unlocking Value from Ubiquitous Data

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Part of the Communications in Computer and Information Science book series (CCIS,volume 1007)

Abstract

Data is growing at an alarming rate. This growth is spurred by varied array of sources, such as embedded sensors, social media sites, video cameras, the quantified-self and the internet-of-things. This is changing our reliance on data for making decisions, or data analytics, from being mostly carried out by an individual and in limited settings to taking place while on-the-move and in the field of action. Unlocking value from data directs that it must be assessed from multiple dimensions. Data’s value can be primarily classified as “information,” “knowledge” or “wisdom”. Data analytics addresses such matters as what and why, as well as what will and what should be done. In recent days, data analytics is moving from being reserved for domain experts to becoming necessary for the end-user. However, data availability is both a pertinent issue and a great opportunity for global businesses. This paper presents recent examples from work in our research team on ubiquitous data analytics and open up to a discussion on key questions relating methodologies, tools and frameworks to improve ubiquitous data team effectiveness as well as the potential goals for a ubiquitous data process methodology. Finally, we give an outlook on the future of data analytics, suggesting a few research topics, applications, opportunities and challenges. This paper is based on a keynote speech to the 14th International Conference on ICT in Education, Research and Industrial Applications. Integration, Harmonization and Knowledge Transfer, Kyiv, Ukraine on 16 May 2018.

Keywords

  • Ubiquitous data
  • Big data
  • Data analytics
  • Transport sector
  • Smart city
  • Emergency management

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Fig. 1.
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Notes

  1. 1.

    McKinsey&Business: Retrieved from https://www.mckinsey.com/business-functions/operations/our-insights/ops-4-0-fueling-the-next-20-percent-productivity-rise-with-digital-analytics.

  2. 2.

    McKinsey&Business: Retrieved from https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/analytics-comes-of-age.

  3. 3.

    LeMO: Retrieved from https://lemo-h2020.eu/.

  4. 4.

    BDEM: Retrieved from https://bdem.squarespace.com/.

  5. 5.

    All information can be accessed via http://www.mksmart.org/ and were last accessed on September 3, 2018.

  6. 6.

    https://www.vestforsk.no/en/project/ubiquitous-data-driven-urban-mobility.

  7. 7.

    http://setamobility.weebly.com/.

  8. 8.

    https://transformingtransport.eu/.

  9. 9.

    https://data.transformingtransport.eu/ was accessed on September 3, 2018.

  10. 10.

    http://noesis-project.eu/.

  11. 11.

    https://lemo-h2020.eu/overview/.

  12. 12.

    https://bdem.squarespace.com/.

References

  1. Akerkar, R.: Processing big data for emergency management. In: Emergency and Disaster Management: Concepts, Methodologies, Tools, and Applications, pp. 980–1000. IGI Global (2019). https://doi.org/10.4018/978-1-5225-2575-2.ch005

  2. Akerkar, R., Sajja, P.: Knowledge-Based Systems. Jones & Bartlett Publishers, Burlington (2010)

    Google Scholar 

  3. Akerkar, R., Sajja, P.S.: Intelligent Techniques for Data Science. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-29206-9

    CrossRef  MATH  Google Scholar 

  4. Goes, J.D.: Big data is dead. what’s next. Venturebeatcom guest blog post (2013). https://venturebeat.com/2013/02/22/big-data-is-dead-whats-next/?goback=%2Egde_62438_member_217099766

  5. Chauhan, R.: Transforming big data into actionable insights (2015). https://www.mastercardadvisors.com/content/dam/advisors/en-us/documents/150513_Transforming_Big_Data.pdf

  6. Barnaghi, P.M., Sheth, A.P., Henson, C.A.: From data to actionable knowledge: big data challenges in the web of things. IEEE Intell. Syst. 28(6), 6–11 (2013). https://doi.org/10.1109/MIS.2013.142

    CrossRef  Google Scholar 

  7. Carter, K.B.: Actionable Intelligence: A Guide to Delivering Business Results with Big Data Fast!. Wiley, Hoboken (2014)

    Google Scholar 

  8. Hotho, A., Pedersen, R.U., Wurst, M.: Ubiquitous data. In: May, M., Saitta, L. (eds.) Ubiquitous Knowledge Discovery. LNCS (LNAI), vol. 6202, pp. 61–74. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-16392-0_4

    CrossRef  Google Scholar 

  9. Insights MTR: The rise of data capital. Oracle (2016). https://www.technologyreview.com/s/601081/the-rise-of-data-capital/

  10. Senaratne, H., et al.: Urban mobility analysis with mobile network data: a visual analytics approach. IEEE Trans. Intell. Transp. Syst. 19(5), 1537–1546 (2018). https://doi.org/10.1109/TITS.2017.2727281

    CrossRef  Google Scholar 

  11. Song, Y., Hu, Z., Leng, X., Tian, H., Yang, K., Ke, X.: Friendship influence on mobile behavior of location based social network users. J. Commun. Netw. 17(2), 126–132 (2015). https://doi.org/10.1109/JCN.2015.000026

    CrossRef  Google Scholar 

  12. Xia, D., Lu, X., Li, H., Wang, W, Li, Y., Zhang, Z.: A MapReduce-based parallel frequent pattern growth algorithm for spatiotemporal association analysis of mobile trajectory big data. Complexity 2018, 2818,251:1–2818,251:16 (2018). https://doi.org/10.1155/2018/2818251

    MATH  Google Scholar 

  13. Bhattacharya, S., Blunck, H., Kjærgaard, M.B., Nurmi, P.: Robust and energy-efficient trajectory tracking for mobile devices. IEEE Trans. Mob. Comput. 14(2), 430–443 (2015). https://doi.org/10.1109/TMC.2014.2318712

    CrossRef  Google Scholar 

  14. Menouar, H., Güvenç, I., Akkaya, K., Uluagac, A.S., Kadri, A., Tuncer, A.: UAV-enabled intelligent transportation systems for the smart city: applications and challenges. IEEE Commun. Mag. 55(3), 22–28 (2017). https://doi.org/10.1109/MCOM.2017.1600238CM

    CrossRef  Google Scholar 

  15. Chen, L., Englund, C.: Every second counts: integrating edge computing and service oriented architecture for automatic emergency management. J. Adv. Transp. 2018, 1–13 (2018). https://doi.org/10.1155/2018/7592926

    CrossRef  Google Scholar 

  16. Nuaimi, E.A., Neyadi, H.A., Mohamed, N., Al-Jaroodi, J.: Applications of big data to smart cities. J. Internet Serv. Appl. 6(1), 25:1–25:15 (2015). https://doi.org/10.1186/s13174-015-0041-5

    CrossRef  Google Scholar 

  17. Ma, X., Liu, C., Wen, H., Wang, Y., Wu, Y.J.: Understanding commuting patterns using transit smart card data. J. Transp. Geogr. 58, 135–145 (2017). https://doi.org/10.1016/j.jtrangeo.2016.12.001

    CrossRef  Google Scholar 

  18. Bagula, A.B., Castelli, L., Zennaro, M.: On the design of smart parking networks in the smart cities: an optimal sensor placement model. Sensors 15(7), 15,443–15,467 (2015). https://doi.org/10.3390/s150715443

    CrossRef  Google Scholar 

  19. Zhao, Z., Koutsopoulos, H.N., Zhao, J.: Detecting pattern changes in individual travel behavior: a Bayesian approach. Transp. Res. Part B: Methodol. 112, 73–88 (2018). https://doi.org/10.1016/j.trb.2018.03.017

    CrossRef  Google Scholar 

  20. Alam, F., Mehmood, R., Katib, I., Albogami, N.N., Albeshri, A.: Data fusion and iot for smart ubiquitous environments: a survey. IEEE Access 5, 9533–9554 (2017). https://doi.org/10.1109/ACCESS.2017.2697839

    CrossRef  Google Scholar 

  21. Nandury, S.V., Begum, B.A.: Smart WSN-based ubiquitous architecture for smart cities. In: Mauri, J.L., et al. (eds.) Proceedings of the International Conference on Advances in Computing, Communications and Informatics, ICACCI 2015, pp. 2366–2373. IEEE, Kochi (2015). https://doi.org/10.1109/ICACCI.2015.7275972

  22. Yi, S., Li, C., Li, Q.: A survey of fog computing: concepts, applications and issues. In: Li, Q., Xuan, D. (eds.) Proceedings of the 2015 Workshop on Mobile Big Data, Mobidata@MobiHoc 2015, pp. 37–42. ACM, Hangzhou (2015). https://doi.org/10.1145/2757384.2757397

  23. Thakuriah, P.V., Geers, D.G.: Data sources and management. In: Thakuriah, P., Geers, D.G. (eds.) Transportation and Information. BRIEFSCOMPUTER, pp. 15–34. Springer, New York (2013). https://doi.org/10.1007/978-1-4614-7129-5_2

    CrossRef  Google Scholar 

  24. Taylor, N., et al.: The transport data revolution: investigation into the data required to support and drive intelligent mobility (2015). https://ts.catapult.org.uk/wp-content/uploads/2016/04/The-Transport-Data-Revolution.pdf

  25. Chen, N., Chen, Y., You, Y., Ling, H., Liang, P., Zimmermann, R.: Dynamic urban surveillance video stream processing using fog computing. In: IEEE Second International Conference on Multimedia Big Data, BigMM 2016, Taipei, Taiwan, 20–22 April 2016, pp. 105–112. IEEE Computer Society (2016). https://doi.org/10.1109/BigMM.2016.53

  26. Anantharam, P., Barnaghi, P.M., Thirunarayan, K., Sheth, A.P.: Extracting city traffic events from social streams. ACM Trans. Intell. Syst. Technol. 6(4), 43:1–43:27 (2015). https://doi.org/10.1145/2717317

    CrossRef  Google Scholar 

  27. Costa, D.G., Duran-Faundez, C., Andrade, D.C., Rocha-Junior, J.B., Peixoto, J.P.J.: TwitterSensing: an event-based approach for wireless sensor networks optimization exploiting social media in smart city applications. Sensors 18(4), 1080 (2018). https://doi.org/10.3390/s18041080

    CrossRef  Google Scholar 

  28. Poblet, M., García-Cuesta, E., Casanovas, P.: Crowdsourcing roles, methods and tools for data-intensive disaster management. Inf. Syst. Front. 20, 1–17 (2017). https://doi.org/10.1007/s10796-017-9734-6

    CrossRef  Google Scholar 

  29. Luna, S., Pennock, M.J.: Social media applications and emergency management: a literature review and research agenda. Int. J. Disaster Risk Reduct. 28, 565–577 (2018). https://doi.org/10.1016/j.ijdrr.2018.01.006

    CrossRef  Google Scholar 

  30. Burton, S.H., Tanner, K.W., Giraud-Carrier, C.G., West, J.H., Barnes, M.D.: “Right time, right place” health communication on twitter: value and accuracy of location information. J. Med. Internet Res. 14(6), e156:1–e156:11 (2012). https://doi.org/10.2196/jmir.2121

    CrossRef  Google Scholar 

  31. Kim, J., Hastak, M.: Social network analysis: characteristics of online social networks after a disaster. Int. J. Inf. Manag. 38(1), 86–96 (2018). https://doi.org/10.1016/j.ijinfomgt.2017.08.003

    CrossRef  Google Scholar 

  32. Imran, M., Castillo, C., Diaz, F., Vieweg, S.: Processing social media messages in mass emergency: a survey. ACM Comput. Surv. (CSUR) 47(4), 67 (2015). https://doi.org/10.1145/2771588

    CrossRef  Google Scholar 

  33. Yin, C., Xiong, Z., Chen, H., Wang, J., Cooper, D., David, B.: A literature survey on smart cities. Sci. China Inf. Sci. 58(10), 1–18 (2015). https://doi.org/10.1007/s11432-015-5397-4

    CrossRef  Google Scholar 

  34. Yin, J., Yu, D., Yin, Z., Liu, M., He, Q.: Evaluating the impact and risk of pluvial flash flood on intra-urban road network: a case study in the city center of Shanghai, China. J. Hydrol. 537, 138–145 (2016). https://doi.org/10.1016/j.jhydrol.2016.03.037

    CrossRef  Google Scholar 

  35. Ko, E.B., Lee, J.W.: Accuracy improvement methods for string similarity measurement in poi (point of interest) data retrieval. KIISE Trans. Comput. Pract. 20(9), 498–506 (2014). https://doi.org/10.5626/KTCP.2014.20.9.498

    CrossRef  Google Scholar 

  36. Jiang, S., Alves, A.O., Rodrigues, F., Ferreira Jr., J., Pereira, F.C.: Mining point-of-interest data from social networks for urban land use classification and disaggregation. Comput. Environ. Urban Syst. 53, 36–46 (2015). https://doi.org/10.1016/j.compenvurbsys.2014.12.001

    CrossRef  Google Scholar 

  37. Mosley, M., Brackett, M.H., Earley, S., Henderson, D.: DAMA Guide to the Data Management Body of Knowledge. Technics Publications, Basking Ridge (2010)

    Google Scholar 

  38. Lathrop, D., Ruma, L.: Open government: collaboration, transparency, and participation in practice. Govern. Inf. Q. 28(1), 129–130 (2011). https://doi.org/10.1016/j.giq.2010.08.001

    CrossRef  Google Scholar 

  39. Townsend, A.M.: Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia. WW Norton & Company, New York (2013)

    Google Scholar 

  40. Barkham, R., Bokhari, S., Saiz, A.: Urban big data: city management and real estate markets. GovLab Digest, New York (2018)

    Google Scholar 

  41. Marjani, M., et al.: Big IoT data analytics: architecture, opportunities, and open research challenges. IEEE Access 5, 5247–5261 (2017). https://doi.org/10.1109/ACCESS.2017.2689040

    CrossRef  Google Scholar 

  42. May, M., Berendt, B., Cornue, A., et al.: Research challenges in ubiquitous knowledge discovery. In: Next Generation of Data Mining, pp. 154–173. Chapman and Hall/CRC (2008). https://doi.org/10.1201/9781420085877.ch7

    Google Scholar 

  43. Ramírez-Gallego, S., Krawczyk, B., García, S., Wozniak, M., Herrera, F.: A survey on data preprocessing for data stream mining: current status and future directions. Neurocomputing 239, 39–57 (2017). https://doi.org/10.1016/j.neucom.2017.01.078

    CrossRef  Google Scholar 

  44. Satyanarayanan, M., et al.: Edge analytics in the Internet of Things. IEEE Perv. Comput. 14(2), 24–31 (2015). https://doi.org/10.1109/MPRV.2015.32

    CrossRef  Google Scholar 

  45. Akerkar, R.: Privacy and security in data-driven urban mobility. In: Utilizing Big Data Paradigms for Business Intelligence, pp. 106–128. IGI Global (2019). https://doi.org/10.4018/978-1-5225-4963-5.ch004

    Google Scholar 

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Acknowledgements

This work is partially supported by the European Union’s Horizon 2020 research and innovation programme under grant agreement No 770038, UBIMOB project (270785) funded by the Norwegian Research Council in 2017 through the IKTPLUSS programme, and BDEM project funded by the Research Council of Norway (RCN) and the Norwegian Centre for International Cooperation in Education (SiU) through the INTPART programme. Authors contributed equally to this work.

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Akerkar, R., Hong, M. (2019). Unlocking Value from Ubiquitous Data. In: Ermolayev, V., Suárez-Figueroa, M., Yakovyna, V., Mayr, H., Nikitchenko, M., Spivakovsky, A. (eds) Information and Communication Technologies in Education, Research, and Industrial Applications. ICTERI 2018. Communications in Computer and Information Science, vol 1007. Springer, Cham. https://doi.org/10.1007/978-3-030-13929-2_1

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