Skip to main content
SpringerLink
Log in
Book cover

Big Data in Engineering Applications pp 41–63Cite as

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

  • Chapter
  • First Online:

Part of the book series: Studies in Big Data ((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.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  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.

    Article  Google Scholar 

  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. Skiba, D. J. (2013). The Internet of Things (IoT). Nursing Education Perspectives, 34, 63–64.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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. 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.

    Article  Google Scholar 

  9. Park, H. W., & Leydesdorff, L. (2013). Decomposing social and semantic networks in emerging “big data” research. J Informetr, 7, 756–765.

    Article  Google Scholar 

  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. Talia, D. (2013). Clouds for scalable big data analytics. Computer, 46, 98–101. https://doi.org/10.1109/MC.2013.162.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.2013.08.041

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Chapter  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Chapter  Google Scholar 

  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. 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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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. (2012). Text messaging capabilities are added to Chicago’s 311 and city alerts system. US Fed News Service. US State News.

    Google Scholar 

  30. Nuttall, R. (2014). Bring Open311 to Pittsburgh. Pittsburgh City Pap.

    Google Scholar 

  31. (2014). What is Open311? Open311. Retrieved November 21, 2014 from http://www.open311.org/learn/.

  32. (2014). City of Chicago—311 City Services. Retrieved November 7, 2014 from http://www.cityofchicago.org/city/en/depts/311.html.

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  35. Kiran, D. R. (2016). Total quality management: Key concepts and case studies (1st ed.). India: Butterworth-Heinemann.

    Google Scholar 

  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. Breyfogle, F. W. (2003). Implementing six sigma, second edition: Smarter solutions using statistical methods (2nd ed.). Hoboken, NJ: Wiley.

    Google Scholar 

  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. 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. Harrington, H. J. (1991). Business process improvement: The breakthrough strategy for total quality, productivity, and competitiveness. McGraw-Hill Education.

    Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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. van der Aalst, W. (2016). Process mining. Berlin Heidelberg, Berlin, Heidelberg: Springer.

    Book  Google Scholar 

  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 

Download references

Author information

Authors and Affiliations

  1. Universidad Carlos III de Madrid, Getafe, Spain

    Alejandro Vera-Baquero

  2. Østfold University College, Halden, Spain

    Ricardo Colomo-Palacios

Authors
  1. Alejandro Vera-Baquero
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ricardo Colomo-Palacios
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alejandro Vera-Baquero .

Editor information

Editors and Affiliations

  1. School of Computing Science and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India

    Sanjiban Sekhar Roy

  2. Department of Civil Engineering, National Institute of Technology Patna, Patna, Bihar, India

    Pijush Samui

  3. University of Southern Queensland, Springfield, Queensland, Australia

    Ravinesh Deo

  4. Polytechnic University of Milan, Milan, Italy

    Stavros Ntalampiras

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Vera-Baquero, A., Colomo-Palacios, R. (2018). Big-Data Analysis of Process Performance: A Case Study of Smart Cities. In: Roy, S., Samui, P., Deo, R., Ntalampiras, S. (eds) Big Data in Engineering Applications. Studies in Big Data, vol 44. Springer, Singapore. https://doi.org/10.1007/978-981-10-8476-8_3

Download citation

  • DOI: https://doi.org/10.1007/978-981-10-8476-8_3

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-8475-1

  • Online ISBN: 978-981-10-8476-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation