Advertisement

Customer Experience Management (CEM)

  • Samuel Gallego ChimenoEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 801)

Abstract

The CEM Project arises from the need to create a web app that will enable users to work with the tools necessary for collecting and manipulating statistical data coming from surveys, campaigns and waves, etc. Primarily, our aim is to offer the customer a product that allows to collect data, in a simple and descriptive manner, for their subsequent interpretation with other types of tools that CEM also offers, such as its graphics module.

Keywords

Segment List Server User Project Campaign Wave Survey Template Client Control panel Management module 

Notes

Acknowledgements

“This work has been supported by project “IOTEC: Development of Technological Capacities around the Industrial Application of Internet of Things (IoT)”. 0123_IOTEC_3_E. Project financed with FEDER funds, Interreg Spain-Portugal (PocTep).”

References

  1. 1.
    Mata, A., Lancho, B.P., Corchado, J.M.: Forest fires prediction by an organization based system. In: PAAMS 2010, pp. 135–144 (2010)Google Scholar
  2. 2.
    Baruque, B., Corchado, E., Mata, A., Corchado, J.M.: A forecasting solution to the oil spill problem based on a hybrid intelligent system. Inf. Sci. 180(10), 2029–2043 (2010).  https://doi.org/10.1016/j.ins.2009.12.032CrossRefGoogle Scholar
  3. 3.
    Choon, Y.W., Mohamad, M.S., Deris, S., Illias, R.M., Chong, C.K., Chai, L.E., Corchado, J.M.: Differential bees flux balance analysis with OptKnock for in silico microbial strains optimization. PLoS ONE 9(7) (2014).  https://doi.org/10.1371/journal.pone.0102744CrossRefGoogle Scholar
  4. 4.
    Corchado, J.A., Aiken, J., Corchado, E.S., Lefevre, N., Smyth, T.: Quantifying the Ocean’s CO2 budget with a CoHeL-IBR system. In: Proceedings of Advances in Case-Based Reasoning, vol. 3155, pp. 533–546 (2004)Google Scholar
  5. 5.
    Corchado, J.M., Aiken, J.: Hybrid artificial intelligence methods in oceanographic forecast models. IEEE Trans. Syst. Man Cybern. Part C-Appl. Rev. 32(4), 307–313 (2002).  https://doi.org/10.1109/tsmcc.2002.806072CrossRefGoogle Scholar
  6. 6.
    Corchado, J.M., Fyfe, C.: Unsupervised neural method for temperature forecasting. Artif. Intell. Eng. 13(4), 351–357 (1999).  https://doi.org/10.1016/S0954-1810(99)00007-2CrossRefGoogle Scholar
  7. 7.
    Corchado, J.M., Borrajo, M.L., Pellicer, M.A., Yáñez, J.C.: Neuro-symbolic system for business internal control. In: Industrial Conference on Data Mining, pp. 1–10 (2004).  https://doi.org/10.1007/978-3-540-30185-1_1Google Scholar
  8. 8.
    Corchado, J.M., Corchado, E.S., Aiken, J., Fyfe, C., Fernandez, F., Gonzalez, M.: Maximum likelihood hebbian learning based retrieval method for CBR systems. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 2689, pp. 107–121 (2003).  https://doi.org/10.1007/3-540-45006-8_11
  9. 9.
    Corchado, J.M., Pavón, J., Corchado, E.S., Castillo, L.F.: Development of CBR-BDI agents: a tourist guide application. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3155, pp. 547–559 (2004).  https://doi.org/10.1007/978-3-540-28631-8
  10. 10.
    Corchado, J., Fyfe, C., Lees, B.: Unsupervised learning for financial forecasting. In: Proceedings of the IEEE/IAFE/INFORMS 1998 Conference on Computational Intelligence for Financial Engineering (CIFEr) (Cat. No. 98TH8367), pp. 259–263 (1998).  https://doi.org/10.1109/CIFER.1998.690316
  11. 11.
    Costa, Â., Novais, P., Corchado, J.M., Neves, J.: Increased performance and better patient attendance in an hospital with the use of smart agendas. Logic J. IGPL 20(4), 689–698 (2012).  https://doi.org/10.1093/jigpal/jzr021MathSciNetCrossRefGoogle Scholar
  12. 12.
    Tapia, D.I., García, Ó., Alonso, R.S., Guevara, F., Catalina, J., Bravo, R.A., Corchado, J.M.: Evaluating the n-Core polaris real-time locating system in an indoor environment. In: PAAMS (Workshops), pp. 29–37 (2012)Google Scholar
  13. 13.
    Tapia, D.I., Alonso, R.S., García, Ó., Corchado, J.M.: HERA: hardware-embedded reactive agents platform. In: PAAMS (Special Sessions), pp. 249–256 (2011)Google Scholar
  14. 14.
    Fdez-Riverola, F., Corchado, J.M.: CBR based system for forecasting red tides. Knowl.-Based Syst. 16(5–6 SPEC.), 321–328 (2003).  https://doi.org/10.1016/S0950-7051(03)00034-0CrossRefGoogle Scholar
  15. 15.
    Fdez-Rtverola, F., Corchado, J.M.: FSfRT: forecasting system for red tides. Appl. Intell. 21(3), 251–264 (2004).  https://doi.org/10.1023/B:APIN.0000043558.52701.b1CrossRefGoogle Scholar
  16. 16.
    Fernández-Riverola, F., Díaz, F., Corchado, J.M.: Reducing the memory size of a Fuzzy case-based reasoning system applying rough set techniques. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 37(1), 138–146 (2007).  https://doi.org/10.1109/TSMCC.2006.876058CrossRefGoogle Scholar
  17. 17.
    Fernandes, F., Gomes, L., Morais, H., Silva, M.R., Vale, Z.A., Corchado, J.M.: Dynamic energy management method with demand response interaction applied in an office building. In: PAAMS (Special Sessions), pp. 69–82 (2016)Google Scholar
  18. 18.
    Fyfe, C., Corchado, J.: A comparison of Kernel methods for instantiating case based reasoning systems. Adv. Eng. Inform. 16(3), 165–178 (2002).  https://doi.org/10.1016/S1474-0346(02)00008-3CrossRefGoogle Scholar
  19. 19.
    Fyfe, C., Corchado, J.M.: Automating the construction of CBR systems using kernel methods. Int. J. Intell. Syst. 16(4), 571–586 (2001).  https://doi.org/10.1002/int.1024CrossRefzbMATHGoogle Scholar
  20. 20.
    García Coria, J.A., Castellanos-Garzón, J.A., Corchado, J.M.: Intelligent business processes composition based on multi-agent systems. Exp. Syst. Appl. 41(4 PART 1), 1189–1205 (2014).  https://doi.org/10.1016/j.eswa.2013.08.003CrossRefGoogle Scholar
  21. 21.
    García, E., Rodríguez, S., Martín, B., Zato, C., Pérez, B.: MISIA: middleware infrastructure to simulate intelligent agents. In: Advances in Intelligent and Soft Computing, vol. 91 (2011).  https://doi.org/10.1007/978-3-642-19934-9_14Google Scholar
  22. 22.
    Glez-Bedia, M., Corchado, J.M., Corchado, E.S., Fyfe, C.: Analytical model for constructing deliberative agents. Int. J. Eng. Intell. Syst. Electr. Eng. Commun. 10(3) (2002)Google Scholar
  23. 23.
    Oliver, M., Molina, J.P., Fernández-Caballero, A., González, P.: Collaborative computer-assisted cognitive rehabilitation system. ADCAIJ Adv. Distrib. Comput. Artif. Intell. J. 6(3) (2017). ISSN: 2255-2863CrossRefGoogle Scholar
  24. 24.
    Ueno, M., Suenaga, T., Isahara, H.: Classification of two comic books based on convolutional neural networks. ADCAIJ Adv. Distrib. Comput. Artif. Intell. J. 6(1) (2017). ISSN: 2255-2863lGoogle Scholar
  25. 25.
    Blanco Valencia, X.P., Becerra, M.A., Castro Ospina, A.E., Ortega Adarme, M., Viveros Melo, D., Peluffo Ordóñez, D.H.: Kernel-based framework for spectral dimensionality reduction and clustering formulation: a theoretical study. Adv. Distrib. Comput. Artif. Intell. J. (2017). ISSN: 2255-2863Google Scholar
  26. 26.
    Bullón, J., Arrieta, A.G., Encinas, A.H., Dios, A.Q.: Manufacturing processes in the textile industry. Expert Systems for fabrics production. Adv. Distrib. Comput. Artif. Intell. J. 6(1) (2017). ISSN: 2255-2863Google Scholar
  27. 27.
    Griol, D., Molina, J.M.: Simulating heterogeneous user behaviors to interact with conversational interfaces. ADCAIJ Adv. Distrib. Comput. Artif. Intell. J. 5(4) (2016). ISSN: 2255-2863Google Scholar
  28. 28.
    Glez-Peña, D., Díaz, F., Hernández, J.M., Corchado, J.M., Fdez-Riverola, F.: geneCBR: a translational tool for multiple-microarray analysis and integrative information retrieval for aiding diagnosis in cancer research. BMC Bioinform. 10 (2009).  https://doi.org/10.1186/1471-2105-10-187CrossRefGoogle Scholar
  29. 29.
    Román, J.Á., Rodríguez, S., Corchado, J.M.: Improving intelligent systems: specialization. In: PAAMS (Workshops), pp. 378–385 (2014)Google Scholar
  30. 30.
    Li, T., Sun, S., Bolić, M., Corchado, J.M.: Algorithm design for parallel implementation of the SMC-PHD filter. Sig. Process. 119, 115–127 (2016).  https://doi.org/10.1016/j.sigpro.2015.07.013CrossRefGoogle Scholar
  31. 31.
    Li, T., Sun, S., Corchado, J.M., Siyau, M.F.: A particle dyeing approach for track continuity for the SMC-PHD filter. In: FUSION 2014 - 17th International Conference on Information Fusion (2014). https://www.scopus.com/inward/record.uri?eid=2-s2.0-84910637583&partnerID=40&md5=709eb4815eaf544ce01a2c21aa749d8f
  32. 32.
    Li, T., Sun, S., Corchado, J.M., Siyau, M.F.: Random finite set-based Bayesian filters using magnitude-adaptive target birth intensity. In: FUSION 2014 - 17th International Conference on Information Fusion (2014). https://www.scopus.com/inward/record.uri?eid=2-s2.0-84910637788&partnerID=40&md5=bd8602d6146b014266cf07dc35a681e0
  33. 33.
    Li, T.-C., Su, J.-Y., Liu, W., Corchado, J.M.: Approximate Gaussian conjugacy: parametric recursive filtering under nonlinearity, multimodality, uncertainty, and constraint, and beyond. Front. Inf. Technol. Electr. Eng. 18(12), 1913–1939 (2017)CrossRefGoogle Scholar
  34. 34.
    Lima, A.C.E.S., De Castro, L.N., Corchado, J.M.: A polarity analysis framework for Twitter messages. Appl. Math. Comput. 270, 756–767 (2015).  https://doi.org/10.1016/j.amc.2015.08.059CrossRefGoogle Scholar
  35. 35.
    Mata, A., Corchado, J.M.: Forecasting the probability of finding oil slicks using a CBR system. Expert Syst. Appl. 36(4), 8239–8246 (2009).  https://doi.org/10.1016/j.eswa.2008.10.003CrossRefGoogle Scholar
  36. 36.
    Méndez, J.R., Fdez-Riverola, F., Díaz, F., Iglesias, E.L., Corchado, J.M.: A comparative performance study of feature selection methods for the anti-spam filtering domain. In: Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). LNAI, vol. 4065, pp. 106–120 (2006). https://www.scopus.com/inward/record.uri?eid=2-s2.0-33746435792&partnerID=40&md5=25345ac884f61c182680241828d448c5CrossRefGoogle Scholar
  37. 37.
    Morente-Molinera, J.A., Kou, G., González-Crespo, R., Corchado, J.M., Herrera-Viedma, E.: Solving multi-criteria group decision making problems under environments with a high number of alternatives using fuzzy ontologies and multi-granular linguistic modelling methods. Knowl.-Based Syst. 137, 54–64 (2017)CrossRefGoogle Scholar
  38. 38.
    Dang, N.C., de la Prieta, F., Corchado, J.M., Moreno, M.N.: Framework for retrieving relevant contents related to fashion from online social network data. In: PAAMS (Special Sessions), pp. 335–347 (2016)Google Scholar
  39. 39.
    Chamoso, P., de la Prieta, F., de Paz, F., Corchado, J.M.: Swarm agent-based architecture suitable for internet of things and smartcities. In: DCAI 2015, pp. 21–29 (2015)CrossRefGoogle Scholar
  40. 40.
    Pinto, T., Gazafroudi, A.S., Prieto-Castrillo, F., Santos, G., Silva, F., Corchado, J.M., Vale, Z.: Reserve costs allocation model for energy and reserve market simulation. In: 2017 19th International Conference on Intelligent System Application to Power Systems, ISAP 2017, art. no. 8071410 (2017)Google Scholar
  41. 41.
    Redondo-Gonzalez, E., De Castro, L.N., Moreno-Sierra, J., Maestro De Las Casas, M.L., Vera-Gonzalez, V., Ferrari, D.G., Corchado, J.M.: Bladder carcinoma data with clinical risk factors and molecular markers: a cluster analysis. BioMed Res. Int. (2015).  https://doi.org/10.1155/2015/168682CrossRefGoogle Scholar
  42. 42.
    Rodríguez, S., De La Prieta, F., Tapia, D.I., Corchado, J.M.: Agents and computer vision for processing stereoscopic images. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). LNAI, vol. 6077 (2010).  https://doi.org/10.1007/978-3-642-13803-4_12Google Scholar
  43. 43.
    Rodríguez, S., Gil, O., De La Prieta, F., Zato, C., Corchado, J.M., Vega, P., Francisco, M.: People detection and stereoscopic analysis using MAS. In: Proceedings of INES 2010 - 14th International Conference on Intelligent Engineering Systems (2010).  https://doi.org/10.1109/INES.2010.5483855
  44. 44.
    Omatu, S., Wada, T., Rodríguez, S., Chamoso, P., Corchado, J.M.: Multi-agent technology to perform odor classification. In: ISAmI 2014, pp. 241–252 (2014)Google Scholar
  45. 45.
    Lim, S.Y., Mohamad, M.S., En Chai, L., Deris, S., Chan, W.H., Omatu, S., Corchado, J.M., Sjaugi, M.F., Mahfuz Zainuddin, M., Rajamohan, G., Ibrahim, Z., Md Yusof, Z.: Investigation of the effects of imputation methods for gene regulatory networks modelling using dynamic Bayesian networks. In: DCAI 2016, pp. 413–421 (2016)CrossRefGoogle Scholar
  46. 46.
    Tapia, D.I., Corchado, J.M.: An ambient intelligence based multi-agent system for alzheimer health care. Int. J. Ambient Comput. Intell. 1(1), 15–26 (2009).  https://doi.org/10.4018/jaci.2009010102CrossRefGoogle Scholar
  47. 47.
    Tapia, D.I., Fraile, J.A., Rodríguez, S., Alonso, R.S., Corchado, J.M.: Integrating hardware agents into an enhanced multi-agent architecture for Ambient Intelligence systems. Inf. Sci. 222, 47–65 (2013).  https://doi.org/10.1016/j.ins.2011.05.002CrossRefGoogle Scholar
  48. 48.
    López Batista, V.F., Aguilar, R., Alonso, L., Moreno García, M.N., Corchado, J.M.: Data mining for grammatical inference with bioinformatics criteria. In: HAIS (2), pp. 53–60 (2010)Google Scholar
  49. 49.
    Wang, X., Li, T., Sun, S., Corchado, J.M.: A survey of recent advances in particle filters and remaining challenges for multitarget tracking. Sensors 17(12), art. no. 2707 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.BISITE Digital Innovation HubUniversity of Salamanca. Edificio Multiusos I+D+ISalamancaSpain

Personalised recommendations