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Prescriptive Analytics: A Survey of Approaches and Methods

  • Katerina LepeniotiEmail author
  • Alexandros Bousdekis
  • Dimitris Apostolou
  • Gregoris Mentzas
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 339)

Abstract

Data analytics has gathered a lot of attention during the last years. Although descriptive and predictive analytics have become well-established areas, prescriptive analytics has just started to emerge in an increasing rate. In this paper, we present a literature review on prescriptive analytics, we frame the prescriptive analytics lifecycle and we identify the existing research challenges on this topic. To the best of our knowledge, this is the first literature review on prescriptive analytics. Until now, prescriptive analytics applications are usually developed in an ad-hoc way with limited capabilities of adaptation to the dynamic and complex nature of today’s enterprises. Moreover, there is a loose integration with predictive analytics, something which does not enable the exploitation of the full potential of big data.

Keywords

Prescriptive analytics Business analytics Data analytics Big data Literature review 

Notes

Acknowledgements

This work is partly funded by the European Commission project H2020 UPTIME “Unified Predictive Maintenance System” (768634).

References

  1. 1.
    Mikalef, P., Pappas, I., Krogstie, J., Giannakos, M.: Big data analytics capabilities: a systematic literature review and research agenda. Inf. Syst. e-Bus. Manag. 16, 547–578 (2017)CrossRefGoogle Scholar
  2. 2.
    Soltanpoor, R., Sellis, T.: Prescriptive analytics for big data. In: Cheema, M.A., Zhang, W., Chang, L. (eds.) ADC 2016. LNCS, vol. 9877, pp. 245–256. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46922-5_19CrossRefGoogle Scholar
  3. 3.
    Šikšnys, L., Pedersen, T.B.: Prescriptive analytics. In: Liu, L., Özsu, M. (eds.) Encyclopedia of Database Systems. Springer, New York (2016).  https://doi.org/10.1007/978-1-4899-7993-3CrossRefGoogle Scholar
  4. 4.
    Engel, Y., Etzion, O., Feldman, Z.: A basic model for proactive event-driven computing. In: Proceedings of the 6th ACM International Conference on Distributed Event-Based Systems - DEBS 2012 (2012)Google Scholar
  5. 5.
    Basu, A.T.A.N.U.: Five pillars of prescriptive analytics success. Anal. Mag. 8, 8–12 (2013)Google Scholar
  6. 6.
  7. 7.
    Bousdekis, A., Magoutas, B., Apostolou, D., Mentzas, G.: A proactive decision making framework for condition-based maintenance. Ind. Manag. Data Syst. 115, 1225–1250 (2015)CrossRefGoogle Scholar
  8. 8.
    Krumeich, J., Werth, D., Loos, P.: Prescriptive control of business processes. Bus. Inf. Syst. Eng. 58, 261–280 (2015)CrossRefGoogle Scholar
  9. 9.
    Wang, Y., Geng, S., Gao, H.: A proactive decision support method based on deep reinforcement learning and state partition. Knowl.-Based Syst. 143, 248–258 (2018)CrossRefGoogle Scholar
  10. 10.
    Raghupathi, W., Raghupathi, V.: Big data analytics in healthcare: promise and potential. Health Inf. Sci. Syst. 2(1), 3 (2014)CrossRefGoogle Scholar
  11. 11.
    Fink, A.: Conducting Research Literature Reviews. Sage Publications, Thousand Oaks (1998)Google Scholar
  12. 12.
    Nechifor, S., Puiu, D., Tarnauca, B., Moldoveanu, F.: Prescriptive analytics based autonomic networking for urban streams services provisioning. In: 81st Vehicular Technology Conference (VTC Spring), pp. 1–5. IEEE (2015)Google Scholar
  13. 13.
    Ringsquandl, M., Lamparter, S., Lepratti, R.: Graph-based predictions and recommendations in flexible manufacturing systems. In: 42nd Annual Conference of the IEEE Industrial Electronics Society, pp. 6937–6942. IEEE (2016)Google Scholar
  14. 14.
    Brodsky, A., Shao, G., Krishnamoorthy, M., Narayanan, A., Menascé, D., Ak, R.: Analysis and optimization based on reusable knowledge base of process performance models. Int. J. Adv. Manuf. Technol. 88, 337–357 (2016)CrossRefGoogle Scholar
  15. 15.
    Tan, J.S., Ang, A.K., Lu, L., Gan, S.W., Corral, M.G.: Quality analytics in a big data supply chain: commodity data analytics for quality engineering. In: Region 10 Conference (TENCON), pp. 3455–3463. IEEE (2016)Google Scholar
  16. 16.
    Kawas, B., Squillante, M.S., Subramanian, D., Varshney, K.R.: Prescriptive analytics for allocating sales teams to opportunities. In: 13th International Conference on Data Mining Workshops. IEEE (2013)Google Scholar
  17. 17.
    Shroff, G., Agarwal, P., Singh, K., Kazmi, A.H., Shah, S., Sardeshmukh, A.: Prescriptive information fusion. In: 17th International Conference on Information Fusion (FUSION), pp. 1–8. IEEE (2014)Google Scholar
  18. 18.
    Wang, C., Cheng, H., Deng, Y.: Using Bayesian belief network and time-series model to conduct prescriptive and predictive analytics for computer industries. Comput. Ind. Eng. 115, 486–494 (2018)CrossRefGoogle Scholar
  19. 19.
    Wu, P.J., Yang, C.K.: The green fleet optimization model for a low-carbon economy: a prescriptive analytics. In: International Conference on Applied System Innovation, pp. 107–110. IEEE (2017)Google Scholar
  20. 20.
    Stein, N., Meller, J., Flath, C.: Big data on the shop-floor: sensor-based decision-support for manual processes. J. Bus. Econ. 88, 593–616 (2018)CrossRefGoogle Scholar
  21. 21.
    Ghoniem, A., Ali, A., Al-Salem, M., Khallouli, W.: Prescriptive analytics for FIFA World Cup lodging capacity planning. J. Oper. Res. Soc. 68, 1183–1194 (2017)CrossRefGoogle Scholar
  22. 22.
    Gröger, C., Schwarz, H., Mitschang, B.: Prescriptive analytics for recommendation-based business process optimization. In: Abramowicz, W., Kokkinaki, A. (eds.) BIS 2014. LNBIP, vol. 176, pp. 25–37. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-06695-0_3CrossRefGoogle Scholar
  23. 23.
    Ito, S., Fujimaki, R.: Optimization beyond prediction: prescriptive price optimization. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1833–1841. ACM (2017)Google Scholar
  24. 24.
    Goyal, A., et al.: Asset health management using predictive and prescriptive analytics for the electric power grid. IBM J. Res. Dev. 60, 4:1–4:14 (2016)CrossRefGoogle Scholar
  25. 25.
    Chalamalla, A., Ilyas, I.F., Ouzzani, M., Papotti, P.: Descriptive and prescriptive data cleaning. In: Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data, pp. 445–456. ACM (2014)Google Scholar
  26. 26.
    Varshney, K.R., Varshney, L.R.: Food steganography with olfactory white. In: Workshop on Statistical Signal Processing (SSP), pp. 21–24. IEEE (2014)Google Scholar
  27. 27.
    Lo, V., Pachamanova, D.: From predictive uplift modeling to prescriptive uplift analytics: a practical approach to treatment optimization while accounting for estimation risk. J. Mark. Anal. 3, 79–95 (2015)CrossRefGoogle Scholar
  28. 28.
    Baur, A., Klein, R., Steinhardt, C.: Model-based decision support for optimal brochure pricing: applying advanced analytics in the tour operating industry. OR Spectr. 36, 557–584 (2013)CrossRefGoogle Scholar
  29. 29.
    Schwartz, I., York, P., Nowakowski-Sims, E., Ramos-Hernandez, A.: Predictive and prescriptive analytics, machine learning and child welfare risk assessment: the Broward County experience. Child Youth Serv. Rev. 81, 309–320 (2017)CrossRefGoogle Scholar
  30. 30.
    Lentzakis, A., Ware, S., Su, R., Wen, C.: Region-based prescriptive route guidance for travelers of multiple classes. Transp. Res. Part C: Emerg. Technol. 87, 138–158 (2018)CrossRefGoogle Scholar
  31. 31.
    Christ, M., Krumeich, J., Kempa-Liehr, A.W.: Integrating predictive analytics into complex event processing by using conditional density estimations. In: Enterprise Distributed Object Computing Workshop (EDOCW), pp. 1–8. IEEE (2016)Google Scholar
  32. 32.
    Loh, C.S., Li, I.H.: Using Players’ gameplay action-decision profiles to prescribe training: reducing training costs with serious games analytics. In: International Conference on Data Science and Advanced Analytics (DSAA), pp. 652–661. IEEE (2016)Google Scholar
  33. 33.
    Bertsimas, D., Van Parys, B.: Bootstrap robust prescriptive analytics. arXiv preprint arXiv:1711.09974 (2017)
  34. 34.
    Ghosh, R., Gupta, A., Chattopadhyay, S., Banerjee, A., Dasgupta, K.: CoCOA: a framework for comparing aggregate client operations in BPO services. In: International Conference on Services Computing (SCC), pp. 539–546. IEEE (2016)Google Scholar
  35. 35.
    Hong, S., Shin, S., Kim, Y., Seon, C.N., Um, J., Song, S.: Design of marketing scenario planning based on business big data analysis. In: Nah, F.F.-H., Tan, C.-H. (eds.) HCIB 2015. LNCS, vol. 9191, pp. 585–592. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-20895-4_54CrossRefGoogle Scholar
  36. 36.
    Hupfeld, D., Maccioni, R., Sesemann, R., Ravazzolo, D.: Fleet asset capacity analysis and revenue management optimization using advanced prescriptive analytics. J. Revenue Pricing Manag. 15, 516–522 (2016)CrossRefGoogle Scholar
  37. 37.
    Jiang, C., Jensen, D.L., Cao, H., Kumar, T.: Building business intelligence applications having prescriptive and predictive capabilities. In: Chen, L., Tang, C., Yang, J., Gao, Y. (eds.) WAIM 2010. LNCS, vol. 6184, pp. 376–385. Springer, Heidelberg (2010).  https://doi.org/10.1007/978-3-642-14246-8_37CrossRefGoogle Scholar
  38. 38.
    Bertsimas, D., Kallus, N.: From predictive to prescriptive analytics. arXiv preprint arXiv:1402.5481 (2014)
  39. 39.
    Song, S., Jeong, D.H., Kim, J., Hwang, M., Gim, J., Jung, H.: Research advising system based on prescriptive analytics. In: Park, J., Pan, Y., Kim, C.S., Yang, Y. (eds.) Future Information Technology. LNEE, vol. 309, pp. 569–574. Springer, Heidelberg (2014).  https://doi.org/10.1007/978-3-642-55038-6_89CrossRefGoogle Scholar
  40. 40.
    Lee, M., Cho, M., Gim, J., Jeong, D.H., Jung, H.: Prescriptive analytics system for scholar research performance enhancement. In: Stephanidis, C. (ed.) HCI 2014. CCIS, vol. 434, pp. 186–190. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-07857-1_33CrossRefGoogle Scholar
  41. 41.
    Song, S.-K., et al.: Prescriptive analytics system for improving research power. In: 16th International Conference on Computational Science and Engineering (CSE), pp. 1144–1145. IEEE (2013)Google Scholar
  42. 42.
    de Aguiar, M., Greve, F., Costa, G.: PrescStream: a framework for streaming soft real-time predictive and prescriptive analytics. In: Gervasi, O., et al. (eds.) ICCSA 2017. LNCS, vol. 10404, pp. 325–341. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-62392-4_24CrossRefGoogle Scholar
  43. 43.
    Ramannavar, M., Sidnal, N.S.: A proposed contextual model for big data analysis using advanced analytics. In: Aggarwal, V.B., Bhatnagar, V., Mishra, D.K. (eds.) Big Data Analytics. AISC, vol. 654, pp. 329–339. Springer, Singapore (2018).  https://doi.org/10.1007/978-981-10-6620-7_32CrossRefGoogle Scholar
  44. 44.
    Aref, M., et al.: Design and implementation of the LogicBlox system. In: Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, pp. 1371–1382. ACM (2015)Google Scholar
  45. 45.
    Osmani, V., Forti, S., Mayora, O., Conforti, D.: Enabling prescription-based health apps. arXiv preprint arXiv:1706.09407 (2017)
  46. 46.
    Ceravolo, P., Zavatarelli, F.: Knowledge acquisition in process intelligence. In: International Conference on Information and Communication Technology Research (ICTRC), pp. 218–221. IEEE (2015)Google Scholar
  47. 47.
    von Bischhoffshausen, J.K., Paatsch, M., Reuter, M., Satzger, G., Fromm, H.: An information system for sales team assignments utilizing predictive and prescriptive analytics. In: 17th Conference on Business Informatics (CBI), pp. 68–76. IEEE (2015)Google Scholar
  48. 48.
    Du, F., Plaisant, C., Spring, N., Shneiderman, B.: EventAction: visual analytics for temporal event sequence recommendation. In: Conference on Visual Analytics Science and Technology (VAST), pp. 61–70. IEEE (2016)Google Scholar
  49. 49.
    Anderson, R.N.: ‘Petroleum analytics learning machine’ for optimizing the internet of things of today’s digital oil field-to-refinery petroleum system. In: International Conference on Big Data (Big Data), pp. 4542–4545. IEEE (2017)Google Scholar
  50. 50.
    Matyas, K., Nemeth, T., Kovacs, K., Glawar, R.: A procedural approach for realizing prescriptive maintenance planning in manufacturing industries. CIRP Ann. 66, 461–464 (2017)CrossRefGoogle Scholar
  51. 51.
    Giurgiu, I., et al.: On the adoption and impact of predictive analytics for server incident reduction. IBM J. Res. Dev. 61, 9:98–9:109 (2017)CrossRefGoogle Scholar
  52. 52.
    Cho, M., Song, S.K., Weber, J., Jung, H., Lee, M.: Prescriptive analytics for planning research-performance strategy. In: Park, J., Stojmenovic, I., Jeong, H., Yi, G. (eds.) Computer Science and Its Applications. LNEE, vol. 330, pp. 1123–1129. Springer, Berlin (2015).  https://doi.org/10.1007/978-3-662-45402-2_159CrossRefGoogle Scholar
  53. 53.
    Mendes, P.N., et al.: Sonora: a prescriptive model for message authoring on Twitter. In: Benatallah, B., Bestavros, A., Manolopoulos, Y., Vakali, A., Zhang, Y. (eds.) WISE 2014. LNCS, vol. 8787, pp. 17–31. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-11746-1_2CrossRefGoogle Scholar
  54. 54.
    Delen, D., Demirkan, H.: Data, information and analytics as services. Decis. Support Syst. 55, 359–363 (2013)CrossRefGoogle Scholar
  55. 55.
    Sun, Z., Strang, K., Firmin, S.: Business analytics-based enterprise information systems. J. Comput. Inf. Syst. 57, 169–178 (2016)Google Scholar
  56. 56.
    Bärmann, A., Pokutta, S., Schneider, O.: Emulating the expert: inverse optimization through online learning. In: International Conference on Machine Learning, pp. 400–410 (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Katerina Lepenioti
    • 1
    Email author
  • Alexandros Bousdekis
    • 1
  • Dimitris Apostolou
    • 1
    • 2
  • Gregoris Mentzas
    • 1
  1. 1.Information Management Unit (IMU), Institute of Communication and Computer Systems (ICCS)National Technical University of Athens (NTUA)Zografou, AthensGreece
  2. 2.Department of InformaticsUniversity of PiraeusPiraeusGreece

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