Advertisement

A Deep Knowledge-Based Evaluation of Enterprise Applications Interoperability

  • Andrius Valatavičius
  • Saulius GudasEmail author
Chapter
  • 21 Downloads
Part of the Studies in Computational Intelligence book series (SCI, volume 869)

Abstract

Enterprise is a dynamic and self-managed system, and the applications are an integral part of this complex system. The integration and interoperability of enterprise software are two essential aspects that are at the core of system efficiency. This research focuses on the interoperability evaluation methods for the sole purpose of evaluating multiple enterprise applications interoperability capabilities in the model-driven software development environment. The peculiarity of the method is that it links the causality modeling of the real world (domain) with the traditional MDA. The discovered domain causal knowledge transferring to CIM layer of MDA form the basis for designing application software that is integrated and interoperable. The causal (deep) knowledge of the subject domain is used to evaluate the capability of interoperability between software components. The management transaction concept reveals causal dependencies and the goal-driven in-formation transformations of the enterprise management activities (an in-depth knowledge). An assumption is that autonomic interoperability is achievable by gathering knowledge from different sources in an organization, particularly enterprise architecture, and software architecture analysis through web services can help gather required knowledge for automated solutions. In this interoperability capability evaluation research, 13 different enterprise applications were surveyed. Initially, the interoperability capability evaluation was performed using four know edit distance calculations: Levenshtein, Jaro-Winkler, Longest common subsequence, and Jaccard. These research results are a good indicator of software interoperability capability. Combining these results with a bag of words library gathered from “Schema.org” and included as an addition to the evaluation system, we improve our method by moving more closely to semantic similarity analysis. The prototype version for testing of enterprise applications integration solution is under development, but it already allows us to collect data and help research this domain. This research paper summarizes the conclusions of our research towards the autonomic evaluation of interoperability capability between different enterprise applications. It reveals basic concepts on which we proved our assumption that enterprise application could be evaluated in a more objective, calculable manner.

Keywords

Enterprise application interoperability Measurement of interoperability capability Edit distance calculation Autonomic interoperability component 

References

  1. ЛEBEHШTEЙH, Bлaдимиp Иocифoвич. Двoичныe кoды c иcпpaвлeниeм выпaдeний, вcтaвoк и зaмeщeний cимвoлoв. In: Дoклaды Aкaдeмии нayк. Poccийcкaя aкaдeмия нayк, 1965, pp 845–848Google Scholar
  2. Chen D, Doumeingts G, Vernadat F (2008) Architectures for enterprise integration and interoperability: past, present and future. Comput Ind 59(7):647–659CrossRefGoogle Scholar
  3. Cintuglu MH, Youssef T, Mohammed OA (2018) Development and application of a real-time testbed for multiagent system interoperability: a case study on hierarchical microgrid control. IEEE Trans Smart Grid 9(3):1759–1768CrossRefGoogle Scholar
  4. Dong XL, Srivastava D (2013) Big data integration. In: IEEE 29th International conference on Data engineering (ICDE), pp. 1245–1248Google Scholar
  5. Dzemydienė D, Naujikienė R (2009) Elektroninių viešųjų paslaugų naudojimo ir informacinių sistemų sąveikumo vertinimas. Informacijos mokslai, 50Google Scholar
  6. El-Halwagi MM (2016) Process integration, vol 7. Academic Press. ISBN 0-12-370532-0Google Scholar
  7. European Commission. New European Interoperability Framework. Interoperability solutions for public administrations, businesses and citizens (ISA2). https://ec.europa.eu/isa2/sites/isa/files/eif_brochure_final.pdf. Accessed 9 Mar 2019
  8. Ford T, Colombi J, Graham S, Jacques D (2008) Measuring system interoperability. In: Proceedings CSERGoogle Scholar
  9. Heylighen F, Joslyn C (2001) Cybernetics, and second-order cybernetics. In: Encyclopedia of physical science and technology, vol 4, pp 155–170Google Scholar
  10. Hohpe G, Woolf B (2002) Enterprise integration patterns. In: 9th Conference on pattern language of programs, pp 1–9Google Scholar
  11. IDABC E, Industry DG (2004) European interoperability framework for pan-European e-government services. European Communities. http://ec.europa.eu/idabc/servlets/Docd552.pdf. Accessed June 3 2017. ISBN 92-894-8389-X
  12. International Organisation for Standardization, ISO/IEC 2382:2015 Information technology—Vocabulary, 2015. https://www.iso.org/obp/ui/#iso:std:iso-iec:2382:ed-1:v1:en. Accessed 9 Mar 2019
  13. Kasunic M, Anderson W (2004) Measuring systems interoperability: challenges and opportunities. Carnegie-Mellon Univ Pittsburgh Pa Software Engineering InstGoogle Scholar
  14. Krafzig D, Banke K, Slama D (2005) Enterprise SOA: service-oriented architecture best practices. Prentice Hall ProfessionalGoogle Scholar
  15. Kutsche R-D, Milanovic N (eds) (2008) Model-based software and data integration: first international workshop. In: Proceedings, vol 8. Springer Science & Business Media. MBSDIGoogle Scholar
  16. Li L, Wu B, Yang Y (2005) Agent-based ontology integration for ontology-based applications. In: Proceedings of the 2005 Australasian ontology workshop-volume 58. Australian Computer Society, Inc., pp 53–59Google Scholar
  17. Mccann R et al (2005) Mapping maintenance for data integration systems. In: Proceedings of the 31st international conference on very large data bases. VLDB Endowment, pp 1018–1029Google Scholar
  18. Morkevičius A (2014) Business and information systems alignment method based on enterprise architecture models. Doctoral dissertation, KaunasGoogle Scholar
  19. Overeinder BJ, Verkaik PD, Brazier FMT(2008) Web service access management for integration with agent systems. In: Proceedings of the 2008 ACM symposium on applied computing. ACM, pp 1854–1860Google Scholar
  20. Pavlin G, Kamermans M, Scafes M (2010) Dynamic process integration framework: toward efficient information processing in complex distributed systems. Informatica 34(4):477–490Google Scholar
  21. Peukert E, Eberius J, Rahm E (2012) A self-configuring schema matching system. In: 2012 IEEE 28th international conference on data engineering. IEEE, pp 306–317Google Scholar
  22. Rahm E, Bernstein PA (2001) A survey of approaches to automatic schema matching. VLDB J 10(4):334–350CrossRefGoogle Scholar
  23. Shvaiko P, Euzenat J (2013) Ontology matching: state of the art and future challenges. IEEE Trans Knowl Data Eng 25(1):158–176CrossRefGoogle Scholar
  24. Silverston L, Inmon WH, Graziano K (1997) The data model resource book: a library of logical data models and data warehouse designs. Wiley & Sons, Inc, ISBN: 0471153672Google Scholar
  25. Tolk A, Muguira JA (2003) The levels of conceptual interoperability model. In: Proceedings of the 2003 fall simulation interoperability workshop, vol 7, pp 1–11. CiteseerGoogle Scholar
  26. Valatavičius A, Gudas S (2015) Enterprise software system integration using autonomic computing. CEUR-WS. org, 1420, pp 156–163Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Institute of Data Science and Digital Technologies, Vilnius UniversityVilniusLithuania

Personalised recommendations