Scalable Knowledge-Driven Service Automation
Keywords:
Enterprise intelligence fabrics; automated service robotics; operate-sustain service categories; knowledge-oriented automation; service automation; knowledge-oriented services; fabric; enterprise intelligence fabric; enterprise-and-knowledge-oriented fabricsAbstract
This work analyzes scalable enterprise intelligence fabrics designed for high-performance, knowledge-oriented service automation. The core problem is formulated as the definition and architecture of a class of intelligence fabrics capable of automating three key functionalities: service request elucidation, service availability (or execution-path) determination, and execution-time prediction — specifically for enterprise applications built on knowledge-oriented services. A knowledge-oriented service is defined as a functionality that delivers knowledge to a requesting module through an execution path established on-the-fly, including dynamic service orchestration, in response to a request.
Within this context, an enterprise intelligence fabric is defined as a modular set of conformant components, distributed across different locations, that encapsulate the functionality needed to create, configure, monitor, and maintain the data and control infrastructures underlying enterprise intelligence applications and services — all operating under a common governance framework. The paper argues for layered architectures that clearly separate the underlying data and analytical support layer from the application metadata-level service request layer and from the application usage layer. It further discusses the integration of cross-application modules for provenance tracking, service invocation orchestration, security and privacy, and trust.
References
1. Cardoso, S. D., Da Silveira, M., & Pruski, C. (2020). Construction and exploitation of an historical knowledge graph to deal with the evolution of ontologies. Knowledge-Based Systems, 194, 105508.
2. Wang, Q., Ji, Y., Hao, Y., & Cao, J. (2020). GRL: Knowledge graph completion with GAN-based reinforcement learning. Knowledge-Based Systems, 209, 106421.
3. Yandamuri, U. S. (2021). A Comparative Study of Traditional Reporting Systems versus Real-Time Analytics Dashboards in Enterprise Operations. Universal Journal of Business and Management, 1(1), 1-13.
4. Zhao, Y., Zhang, A., Feng, H., Li, Q., Gallinari, P., & Ren, F. (2020). Knowledge graph entity typing via learning connecting embeddings. Knowledge-Based Systems, 196, 105808.
5. Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., & Yu, P. S. (2020). A comprehensive survey on graph neural networks. IEEE Transactions on Neural Networks and Learning Systems, 32(1), 4–24.
6. Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., & Wang, P. (2020). K-BERT: Enabling language representation with knowledge graph. Proceedings of the AAAI Conference on Artificial Intelligence, 34(3), 2901–2908.
7. Mangala, N. (2021). CI/CD Pipeline Automation for Enterprise Data Artifacts Using Azure DevOps. Universal Journal of Business and Management, 1(1), 1-18.
8. Leno, V., Polyvyanyy, A., Dumas, M., La Rosa, M., & Maggi, F. M. (2021). Robotic process mining: Vision and challenges. Business & Information Systems Engineering, 63, 301–314.
9. Kir, H., & Erdogan, N. (2021). A knowledge-intensive adaptive business process management framework. Information Systems, 95, 101639.
10. Rietzke, E., Maletzki, C., Bergmann, R., & Kuhn, N. (2021). Execution of knowledge-intensive processes by utilizing ontology-based reasoning. Journal on Data Semantics, 10, 3–18.
11. Sarazin, A., Bascans, J., Sciau, J.-B., Song, J., Supiot, B., Montarnal, A., Lorca, X., & Truptil, S. (2021). Expert system dedicated to condition-based maintenance based on a knowledge graph approach: Application to an aeronautic system. Expert Systems with Applications, 186, 115767.
12. Sitar-Tăut, D.-A., Mican, D., & Buchmann, R. A. (2021). A knowledge-driven digital nudging approach to recommender systems built on a modified Onicescu method. Expert Systems with Applications, 181, 115170.
13. Sang, L., Xu, M., Qian, S., & Wu, X. (2021). Knowledge graph enhanced neural collaborative recommendation. Expert Systems with Applications, 164, 113992.
14. Mangalampalli, B. M. (2022). Automated Invoice Validation Systems Using Advanced SQL Analytics in Healthcare Insurance. Front Health Inform, 11.
15. Wang, Y., Wang, H., He, J., Lu, W., & Gao, S. (2021). TAGAT: Type-aware graph attention networks for reasoning over knowledge graphs. Knowledge-Based Systems, 233, 107500.
16. Hogan, A., Blomqvist, E., Cochez, M., D’Amato, C., de Melo, G., Gutiérrez, C., Kirrane, S., Gayo, J. E. L., Navigli, R., Neumaier, S., Ngonga Ngomo, A.-C., Polleres, A., Rashid, S. M., Rula, A., Schmelzeisen, L., Sequeda, J., Staab, S., & Zimmermann, A. (2021). Knowledge graphs. ACM Computing Surveys, 54(4), 1–37.
17. Ji, S., Pan, S., Cambria, E., Marttinen, P., & Yu, P. S. (2022). A survey on knowledge graphs: Representation, acquisition, and applications. IEEE Transactions on Neural Networks and Learning Systems, 33(2), 494–514.
18. Berniak-Woźny, J., & Szelągowski, M. (2022). Towards the assessment of business process knowledge intensity: A systematic literature review. Business Process Management Journal, 28(1), 40–61.
19. Mangalampalli, B. M. (2021). Scalable Data Warehouse Architecture for Population Health Management and Predictive Analytics. World Journal of Clinical Medicine Research, 1(1), 1-18.
20. Szelągowski, M., Lupeikiene, A., & Berniak-Woźny, J. (2022). Drivers and evolution paths of BPMS: State-of-the-art and future research directions. Informatica, 33(2), 399–420.
21. Sun, T., Xu, J., & Hu, C. (2022). An efficient algorithm of star subgraph queries on urban traffic knowledge graph. Data Science and Engineering, 7, 383–401.
22. Li, Y., Ma, Y., Huo, X., & Wu, X. (2022). Remote object navigation for service robots using hierarchical knowledge graph in human-centered environments. Intelligent Service Robotics, 15, 459–473.
23. Ni, P., Okhrati, R., Guan, S., & Chang, V. (2022). Knowledge graph and deep learning-based text-to-GraphQL model for intelligent medical consultation chatbot. Information Systems Frontiers, 26, 137–156.
24. Ahmed, I. A., Al-Aswadi, F. N., Noaman, K. M. G., & Alma’aitah, W. Z. (2022). Arabic knowledge graph construction: A close look in the present and into the future. Journal of King Saud University—Computer and Information Sciences, 34, 6505–6523.
25. Yandamuri, U. S. (2022). Big Data Pipelines for Cross-Domain Decision Support: A Cloud-Centric Approach. International Journal of Scientific Research and Modern Technology, 1(12), 227-237.
26. Simsek, U., Kärle, E., Angele, K., Huaman, E., Opdenplatz, J., Sommer, D., Umbrich, J., & Fensel, D. (2022). A knowledge graph perspective on knowledge engineering. SN Computer Science, 4, Article 16.
27. Qian, F., Zhu, Y., Chen, H., Chen, J., Zhao, S., & Zhang, Y. (2022). Reduce unrelated knowledge through attribute collaborative signal for knowledge graph recommendation. Expert Systems with Applications, 201, 117078.
28. Cai, X., Xie, L., Tian, R., & Cui, Z. (2022). Explicable recommendation based on knowledge graph. Expert Systems with Applications, 200, 117035.
29. Liu, C., & Yang, S. (2022). Using text mining to establish knowledge graph from accident/incident reports in risk assessment. Expert Systems with Applications, 202, 117991.
30. Mangala, N. (2021). Optimizing Large-Scale ETL Pipelines Using Medallion Architecture on Azure Data Lake. Journal of Artificial Intelligence and Big Data, 1(1), 1-20.
31. Leno, V., Polyvyanyy, A., Dumas, M., La Rosa, M., & Maggi, F. M. (2022). The performance assessment framework (PPAFR) for RPA implementation in a loan application process using process mining. Information Systems and e-Business Management.
