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Integration of Different ERP Systems on Mobile Devices

  • Álvaro Lozano
  • Ana Belen Gil
  • Tiancheng Li
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 293)

Abstract

Nowadays a lot of enterprises work with ERP systems. It usefulness is generally used in office environments and different enterprises which offer this software are developing mobile applications. These mobile applications work with their own system and they don’t usually work in other platforms. Currently any mobile application can communicate with more than one ERP system because each one has its own communications methods. This article presents a system that expect unify the communication between different ERP systems and allows mobile applications to communicate with them in a homogeneous way.

Keywords

ERP Mobile VTD-XML XPATH REST Android 

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References

  1. 1.
    Serdeira Azevedo, P., Romão, M., Rebelo, E.: Advantages, Limitations and Solutions in the Use of ERP Systems (Enterprise Resource Planning) – A Case Study in the Hospitality Industry.  5, 264–272 (2012)Google Scholar
  2. 2.
    Johansson, B., Ruivo, P.: Exploring Factors for Adopting ERP as SaaS (9), 94 – 99 (2013)Google Scholar
  3. 3.
    Clemens, B., Cata, T., Hackbarth, G.: Mobile Device Considerations for Supply Chain and ERP Related Systems,16 pages (2012)Google Scholar
  4. 4.
    Al Bar, A., Mohamed, E., Khursheed Akhtar, M., Abuhashish, F.: A preliminary review of implementing Enterprise Mobile Application in ERP environment 11(04), 77–82 (2011)Google Scholar
  5. 5.
    Zur Muehlen, M., Nickerson, J.V., Swenson, K.D.: Developing web services choreography standards—the case of REST vs. SOAP (40), 9–29 (2005)Google Scholar
  6. 6.
    Agüero, J., Rebollo, M., Carrascosa, C., Julián, V.: MDD-Approach for developing Pervasive Systems based on Service-Oriented Multi-Agent SystemsGoogle Scholar
  7. 7.
    Závodská, A., Šramová, V., Aho, A.-M.: Knowledge in Value Creation Process for Increasing Competitive Advantage. Advances in Distributed Computing and Artificial Intelligence Journal 1(3), 35–47 (2012)Google Scholar
  8. 8.
    Corchado, J.M., Fyfe, C.: Unsupervised neural method for temperature forecasting. Artificial Intelligence in Engineering 13(4), 351–357 (1999)CrossRefGoogle Scholar
  9. 9.
    Fdez-Riverola, F., Corchado, J.M.: CBR based system for forecasting red tides. Knowledge-Based Systems 16(5), 321–328 (2003)CrossRefMathSciNetGoogle Scholar
  10. 10.
    Tapia, D.I., Abraham, A., Corchado, J.M., Alonso, R.S.: Agents and ambient intelligence: case studies. Journal of Ambient Intelligence and Humanized Computing 1(2), 85–93 (2010)CrossRefGoogle Scholar
  11. 11.
    Corchado, J.M., Lees, B.: Adaptation of cases for case based forecasting with neural network support. Soft Computing in Case Based Reasoning, 293–319 (2001)Google Scholar
  12. 12.
    Corchado Rodríguez, J.M.: Redes Neuronales Artificiales: un enfoque práctico. Servicio de Publicacións da Universidade de Vigo, Vigo (2000)Google Scholar
  13. 13.
    Bajo, J., Corchado, J.M.: Evaluation and monitoring of the air-sea interaction using a CBR-agents approach. In: Muñoz-Ávila, H., Ricci, F. (eds.) ICCBR 2005. LNCS (LNAI), vol. 3620, pp. 50–62. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  14. 14.
    Fraile, J.A., Bajo, J., Corchado, J.M., Abraham, A.: Applying wearable solutions in dependent environments. IEEE Transactions on Information Technology in Biomedicine 14(6), 1459–1467 (2011)CrossRefGoogle Scholar
  15. 15.
    Corchado, J.M., De Paz, J.F., Rodríguez, S., Bajo, J.: Model of experts for decision support in the diagnosis of leukemia patients. Artificial Intelligence in Medicine 46(3), 179–200 (2009)CrossRefGoogle Scholar
  16. 16.
    De Paz, J.F., Rodríguez, S., Bajo, J., Corchado, J.M.: Case-based reasoning as a decision support system for cancer diagnosis: A case study. International Journal of Hybrid Intelligent Systems 6(2), 97–110 (2009)Google Scholar
  17. 17.
    Tapia, D.I., Rodríguez, S., Bajo, J., Corchado, J.M.: FUSION@, a SOA-based multi-agent architecture. In: International Symposium on Distributed Computing and Artificial Intelligence (DCAI 2008), pp. 9–107 (2008)Google Scholar
  18. 18.
    Corchado, J.M., Aiken, J.: Hybrid artificial intelligence methods in oceanographic forecast models. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 32(4), 307–313 (2002)CrossRefGoogle Scholar
  19. 19.
    Corchado, J.M., Aiken, J., Rees, N.: Artificial intelligence models for oceanographic forecasting. Plymouth Marine Laboratory (2001)Google Scholar
  20. 20.
    Rodríguez, S., Pérez-Lancho, B., De Paz, J.F., Bajo, J., Corchado, J.M.: Ovamah: Multiagent-based adaptive virtual organizations. In: 12th International Conference on Information Fusion, FUSION 2009, pp. 990–997 (2009)Google Scholar
  21. 21.
    Tapia, D.I., Alonso, R.S., De Paz, J.F., Corchado, J.M.: Introducing a distributed architecture for heterogeneous wireless sensor networks. In: Omatu, S., Rocha, M.P., Bravo, J., Fernández, F., Corchado, E., Bustillo, A., Corchado, J.M. (eds.) IWANN 2009, Part II. LNCS, vol. 5518, pp. 116–123. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  22. 22.
    Rodríguez, S., de Paz, Y., Bajo, J., Corchado, J.M.: Social-based planning model for multiagent systems. Expert Systems with Applications 38(10), 13005–13023 (2011)CrossRefGoogle Scholar
  23. 23.
    Pinzón, C.I., Bajo, J., De Paz, J.F., Corchado, J.M.: S-MAS: An adaptive hierarchical distributed multi-agent architecture for blocking malicious SOAP messages within Web Services environments. Expert Systems with Applications 38(5), 5486–5499Google Scholar
  24. 24.
    Studebaker, D.: Programming Microsoft® DynamicsTM NAV 2009. Packt Publishing Ltd., Birmingham (2009) ISBN 978-1-847196-52-1Google Scholar
  25. 25.
    Ansari, A.: Inside Microsoft Dynamics AX 2012. Microsoft Press, United States of America (2012) ISBN: 978-0-7356-6710-5Google Scholar
  26. 26.
    Niefert, W.: SAP® Business ONE Implementation. Packt Publishing Ltd., Birmingham (2009) ISBN 978-1-847196-38-5Google Scholar
  27. 27.
    Zeitler, A., Kheyrollahi, A.: Pro ASP.NET Web API: HTTP Web Services in ASP.NET (2013) ISBN-13: 978-1430247258Google Scholar
  28. 28.
    Tapia, D.I., De Paz, J.F., Rodríguez, S., Bajo, J., Corchado, J.M.: Multi-agent system for security control on industrial environments. International Transactions on System Science and Applications Journal 4(3), 222–226 (2008)Google Scholar
  29. 29.
    Borrajo, M.L., Baruque, B., Corchado, E., Bajo, J., Corchado, J.M.: Hybrid neural intelligent system to predict business failure in small-to-medium-size enterprises. International Journal of Neural Systems 21(04), 277–296 (2011)CrossRefGoogle Scholar
  30. 30.
    De Paz, J.F., Rodríguez, S., Bajo, J., Corchado, J.M.: Mathematical model for dynamic case-based planning. International Journal of Computer Mathematics 86(10-11), 1719–1730 (2009)CrossRefMATHGoogle Scholar
  31. 31.
    Bajo, J., De Paz, J.F., Rodríguez, S., González, A.: Multi-agent system to monitor oceanic environments. Integrated Computer-Aided Engineering 17(2), 131–144 (2010)Google Scholar
  32. 32.
    Corchado, J.M., Bajo, J., De Paz, J.F., Rodríguez, S.: An execution time neural-CBR guidance assistant. Neurocomputing 72(13), 2743–2753 (2009)CrossRefGoogle Scholar
  33. 33.
    Panorama Consulting. Panorama Consulting, http://panorama-consulting.com/
  34. 34.
  35. 35.
  36. 36.
  37. 37.
  38. 38.
  39. 39.

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of Computer Science and AutomationUniversity of SalamancaSalamancaSpain
  2. 2.School of MechatronicsNorthwestern Polytechnical UniversityXi’anP.R. China

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