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Big-Data Analysis of Process Performance: A Case Study of Smart Cities

  • Alejandro Vera-Baquero
  • Ricardo Colomo-Palacios
Chapter
Part of the Studies in Big Data book series (SBD, volume 44)

Abstract

This chapter presents a data-centric software architecture that provides timely data access to key performance indicators (KPIs) about process performance. This architecture comes in the form of an analytical framework that lies on big-data and cloud-computing technologies aimed to cope with the demands of the crowd-sourced data analysis in terms of latency and data volume. This framework is proposed to be applied to the Smart Cities and the Internet of Things (IoT) arenas to monitor, analyse and improve the business processes and smart services of the city. Once the framework is presented from the technical standpoint, a case study is rolled out to leverage this process-centric framework and apply its fundamentals to the smart cities realm with the aim of analysing live smart data and improve the efficiency of smart cities. More specifically, this case study is focussed on the improvement of the service delivery process of the Open311 smart services deployed in the city of Chicago. The outcomes of the test show the ability of the systems to generate metrics in nearly real-time for high volumes of data.

Keywords

Smart cities Internet of Things Big data Cloud computing Business process analytics 

References

  1. 1.
    Piro, G., Cianci, I., Grieco, L. A., et al. (2014). Information centric services in smart Cities. Journal of Systems and Software, 88, 169–188.  https://doi.org/10.1016/j.jss.2013.10.029.CrossRefGoogle Scholar
  2. 2.
    da Silva, W. M., Alvaro, A., Tomas, G. H. R. P., et al. (2013). Smart cities software architectures: A survey (pp. 1722–1727). New York, NY, USA: ACM.Google Scholar
  3. 3.
    Skiba, D. J. (2013). The Internet of Things (IoT). Nursing Education Perspectives, 34, 63–64.CrossRefGoogle Scholar
  4. 4.
    Zheng, J., Simplot-Ryl, D., Bisdikian, C., & Mouftah, H. T. (2011). The Internet of Things [Guest Editorial]. IEEE Communications Magazine, 49, 30–31.  https://doi.org/10.1109/MCOM.2011.6069706.CrossRefGoogle Scholar
  5. 5.
    Borkar, V. R., Carey, M. J., & Li, C. (2012). Big data platforms: What’s next? XRDS, 19, 44–49.  https://doi.org/10.1145/2331042.2331057.CrossRefGoogle Scholar
  6. 6.
    Fosso Wamba, S., Akter, S., Edwards, A., et al. (2015). How ‘big data’ can make big impact: Findings from a systematic review and a longitudinal case study. International Journal of Production Economics, 165, 234–246.  https://doi.org/10.1016/j.ijpe.2014.12.031.CrossRefGoogle Scholar
  7. 7.
    Patel, A. B., Birla, M., Nair, U. (2012). Addressing big data problem using Hadoop and Map Reduce. In 2012 Nirma University International Conference on Engineering (NUiCONE) (pp. 1–5).Google Scholar
  8. 8.
    Wamba, S. F., Gunasekaran, A., Akter, S., et al. (2017). Big data analytics and firm performance: Effects of dynamic capabilities. Journal of Business Research, 70, 356–365.  https://doi.org/10.1016/j.jbusres.2016.08.009.CrossRefGoogle Scholar
  9. 9.
    Park, H. W., & Leydesdorff, L. (2013). Decomposing social and semantic networks in emerging “big data” research. J Informetr, 7, 756–765.CrossRefGoogle Scholar
  10. 10.
    Mutschler, B., Reichert, M. U., & Bumiller, J. (2005). Towards an evaluation framework for business process integration and management. Los Alamitos: IEEE Computer Society Press.Google Scholar
  11. 11.
    Talia, D. (2013). Clouds for scalable big data analytics. Computer, 46, 98–101.  https://doi.org/10.1109/MC.2013.162.CrossRefGoogle Scholar
  12. 12.
    Chen, M., Mao, S., & Liu, Y. (2014). Big data: A survey. Mobile Networks and Applications, 19, 171–209.  https://doi.org/10.1007/s11036-013-0489-0.CrossRefGoogle Scholar
  13. 13.
    Jara, A. J., Genoud, D., & Bocchi, Y. (2014). Big data for smart cities with KNIME a real experience in the SmartSantander testbed. Software: Practice and Experience n/a-n/a.  https://doi.org/10.1002/spe.2274.CrossRefGoogle Scholar
  14. 14.
    Paroutis, S., Bennett, M., & Heracleous, L. (2014). A strategic view on smart city technology: The case of IBM Smarter Cities during a recession. Technological Forecasting and Social Change, 89, 262–272. https://doi.org/10.1016/j.techfore.2013.08.041CrossRefGoogle Scholar
  15. 15.
    Kache, F., & Seuring, S. (2017). Challenges and opportunities of digital information at the intersection of big data analytics and supply chain management. International Journal of Operations & Production Management, 37, 10–36.  https://doi.org/10.1108/IJOPM-02-2015-0078.CrossRefGoogle Scholar
  16. 16.
    Qin, S. J. (2014). Process data analytics in the era of big data. AIChE Journal, 60, 3092–3100.  https://doi.org/10.1002/aic.14523.CrossRefGoogle Scholar
  17. 17.
    Alippi, C., Ntalampiras, S., & Roveri, M. (2017). Designing HMMs in the age of big data. In P. Angelov, Y. Manolopoulos, L. Iliadis, et al. (Eds.), Advances in big data (pp. 120–130). Cham: Springer International Publishing.CrossRefGoogle Scholar
  18. 18.
    Janssen, M., van der Voort, H., & Wahyudi, A. (2017). Factors influencing big data decision-making quality. Journal of Business Research, 70, 338–345.  https://doi.org/10.1016/j.jbusres.2016.08.007.CrossRefGoogle Scholar
  19. 19.
    Baesens, B., Bapna, R., Marsden, J., & Vanthienen, J. (2016). Transformational issues of big data and analytics in networked business. Management Information Systems Quarterly, 40, 807–818.CrossRefGoogle Scholar
  20. 20.
    Hamilton, A., Waterson, B., Cherrett, T., et al. (2013). The evolution of urban traffic control: Changing policy and technology. Transportation Planning and Technology, 36, 24–43.  https://doi.org/10.1080/03081060.2012.745318.CrossRefGoogle Scholar
  21. 21.
    Vera-Baquero, A., Colomo-Palacios, R., & Molloy, O. (2013). Business process analytics using a big data approach. IT Professional, 15, 29–35.  https://doi.org/10.1109/MITP.2013.60.CrossRefGoogle Scholar
  22. 22.
    Vera-Baquero, A., Colomo Palacios, R., Stantchev, V., & Molloy, O. (2015). Leveraging big-data for business process analytics. The Learning Organization, 22, 215–228.  https://doi.org/10.1108/TLO-05-2014-0023.CrossRefGoogle Scholar
  23. 23.
    Vera-Baquero, A., Colomo-Palacios, R., & Molloy, O. (2015). Measuring and querying process performance in supply chains: an approach for mining big-data cloud storages. Procedia Computer Science, 64, 1026–1034.  https://doi.org/10.1016/j.procs.2015.08.623.CrossRefGoogle Scholar
  24. 24.
    Vera-Baquero, A., & Molloy, O. (2013). Integration of event data from heterogeneous systems to support business process analysis. In A. Fred, J. L. G. Dietz, K. Liu, & J. Filipe (Eds.), Knowledge discovery, knowledge engineering and knowledge management (pp. 440–454). Berlin, Heidelberg: Springer.CrossRefGoogle Scholar
  25. 25.
    Müehlen, M. zur & Swenson, K. D. (2011) BPAF: A standard for the interchange of process analytics data. In: M zur Muehlen & J Su (Eds.), Business process management workshops (pp. 170–181). Berlin Heidelberg: Springer.Google Scholar
  26. 26.
    Hasan, S., & Curry, E. (2015). Thingsonomy: Tackling variety in Internet of Things events. IEEE Internet Computing, 19, 10–18.  https://doi.org/10.1109/MIC.2015.26.CrossRefGoogle Scholar
  27. 27.
    Hasan, S. & Curry, E. (2014). Approximate semantic matching of events for the Internet of Things. ACM Transactions on Internet Technology, 14, 2:1–2:23.  https://doi.org/10.1145/2633684.CrossRefGoogle Scholar
  28. 28.
    (2012). Code for America innovation team arrives in Chicago to develop new open 311 system. US Fed News Service. US State News.Google Scholar
  29. 29.
    (2012). Text messaging capabilities are added to Chicago’s 311 and city alerts system. US Fed News Service. US State News.Google Scholar
  30. 30.
    Nuttall, R. (2014). Bring Open311 to Pittsburgh. Pittsburgh City Pap.Google Scholar
  31. 31.
    (2014). What is Open311? Open311. Retrieved November 21, 2014 from http://www.open311.org/learn/.
  32. 32.
    (2014). City of Chicago—311 City Services. Retrieved November 7, 2014 from http://www.cityofchicago.org/city/en/depts/311.html.
  33. 33.
    Vera-Baquero, A., Colomo-Palacios, R., & Molloy, O. (2014). Towards a process to guide big data based decision support systems for business processes. Procedia Technology, 16, 11–21.  https://doi.org/10.1016/j.protcy.2014.10.063.CrossRefGoogle Scholar
  34. 34.
    Dale, B. G. & Cooper, C. L. (1994). Introducing TQM: The role of senior management. Management Decision, 32, 20–26.  https://doi.org/10.1108/00251749410050660.CrossRefGoogle Scholar
  35. 35.
    Kiran, D. R. (2016). Total quality management: Key concepts and case studies (1st ed.). India: Butterworth-Heinemann.Google Scholar
  36. 36.
    Pande, P. S., Neuman, R. P., & Cavanagh, R. R. (2000). The six sigma way: How GE, Motorola, and other top companies are honing their performance. McGraw Hill Professional.Google Scholar
  37. 37.
    Breyfogle, F. W. (2003). Implementing six sigma, second edition: Smarter solutions using statistical methods (2nd ed.). Hoboken, NJ: Wiley.Google Scholar
  38. 38.
    Harry, M., & Schroeder, R. (2006). Six sigma: The breakthrough management strategy revolutionizing the world’s top corporations (51634th ed.). New York: Crown Business.Google Scholar
  39. 39.
    De Bruin, T., & Rosemann, M. (2005). Towards a business process management maturity model. In D. Bartmann, F. Rajola, J. Kallinikos, et al. (Eds.), Faculty of science and technology (pp. 1–12). CD Rom: Verlag and the London School of Economics.Google Scholar
  40. 40.
    Harrington, H. J. (1991). Business process improvement: The breakthrough strategy for total quality, productivity, and competitiveness. McGraw-Hill Education.Google Scholar
  41. 41.
    James Harrington, H. (1995). Continuous versus breakthrough improvement: Finding the right answer. Business Process Re-engineering & Management Journal, 1, 31–49.  https://doi.org/10.1108/14637159510103211.CrossRefGoogle Scholar
  42. 42.
    Damij, N., Damij, T., Grad, J., & Jelenc, F. (2008). A methodology for business process improvement and IS development. Information and Software Technology, 50, 1127–1141.  https://doi.org/10.1016/j.infsof.2007.11.004.CrossRefGoogle Scholar
  43. 43.
    Vera-Baquero, A., Colomo-Palacios, R., & Molloy, O. (2014). towards a process to guide big data based decision support systems for business processes. Toria, Portugal: SciTePress—Science and and Technology Publications.Google Scholar
  44. 44.
    van der Aalst, W. (2016). Process mining. Berlin Heidelberg, Berlin, Heidelberg: Springer.CrossRefGoogle Scholar
  45. 45.
    Molloy, O. & Sheridan, C. (2010). A framework for the use of business activity monitoring in process improvement. E-Strategies for Resource Management Systems: Planning and Implementation, 21–46.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Alejandro Vera-Baquero
    • 1
  • Ricardo Colomo-Palacios
    • 2
  1. 1.Universidad Carlos III de MadridGetafeSpain
  2. 2.Østfold University CollegeHaldenSpain

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