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Transformation-based processing of typed resources for multimedia sources in the IoT environment

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Abstract

Web services are middleware designed to support the interoperation between different software systems and devices over the Web. Today, we encounter a variety of situations in which services deployed on the Internet of things (IoT), such as wireless sensor networks, ZigBee networks, and mobile edge computing frameworks, have become a widely used infrastructure that has become more flexible, intelligent and automated. This system supports multimedia applications, E-commerce transactions, business collaborations and information processing. However, how to manage these services has been a popular topic in IoT research. Existing research covers numerous resource models, based on sensors or human interactions. For everything as a service, things are available as a service include products, processes, resource management and security provision. To cope with the challenge of how to manage these services, we present an extension of Data, Information, Knowledge and Wisdom architecture as a resource expression model to construct a systematic approach to modeling both entity and relationship elements. The entity elements are formalized from a fully typed, multiple-related dimensions perspective to obtain a whole frequency-value-based representation of entities in the real world. A relationship model is extended and applied to define resource models based on relationships defined from a semantics perspective that is based on our proposed existence-level reasoning. Then, a processing framework is proposed that seeks to optimize the searching efficiency of typed resources in terms of IoT data, information and knowledge inside an integrated architecture, and the framework includes Data Graph, Information Graph and Knowledge Graph. We concentrate on improving performance in accessing and processing resources and providing resource security protection by utilizing the cost difference of both type conversions of resources and traversing on resources. Finally, an application scenario is simulated to illustrate the usage of the proposed framework. This scenario shows the feasibility and effectiveness of our method, considering the conversion, traversing and storage costs. Our method can help improve the optimization of services and scheduling resources of multimedia systems.

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References

  1. Yin, Y., Chen, L., Xu, Y., Wan, J., Zhang, H., & Mai, Z. (2019). QoS prediction for service recommendation with deep feature learning in edge computing environment. Mobile Networks and Applications. https://doi.org/10.1007/s11036-019-01241-7.

    Article  Google Scholar 

  2. Hussain, C. S., Ahmed, C. S., Akbar, A. H., & Bashir, A. K. (2008). Ubiquitous service discovery in pervasive computing environment. Information Technology Journal, 7(3), 533–536.

    Article  Google Scholar 

  3. Duan, Y., Fu, G., Zhou, N., Sun, X., Narendra, N.C., & Hu, B. (2015). Everything as a service (XaaS) on the cloud: Origins, current and future trends. In: International conference on cloud computing (pp. 621–628). IEEE.

  4. Banerjee, P., Friedrich, R., Bash, C., Goldsack, P., Huberman, B., Manley, J., et al. (2011). Everything as a service: Powering the new information economy. Computer, 44(3), 36–43.

    Article  Google Scholar 

  5. Didoné, D., & Queirozb, R. D. (2011). Forensic as a Service-FaaS. In The sixth international conference on forensic computer science (ICoFCS) (pp. 202–210).

  6. Ferrario, R., Guarino, N., Janiesch, C., Kiemes, T., Oberle, D., & Probst, F. (2011) Towards an ontological founda- tion of services science: The general service model. In 10. Internationale Tagung Wirtschaftsinformatik (p. 47), Zrich, 16–18 Februar.

  7. Duan, Y., Shao, L., Hu, G., Zhou, Z., Zou, Q., & Lin, Z. (2017). Specifying architecture of knowledge graph with data graph, information graph, knowledge graph and wisdom graph. In International conference on software engineering research, management and applications (pp. 327–332).

  8. Shao, L., Duan, Y., Sun, X., & Gao, H. (2017). Answering who/when, what, how, why through constructing data graph, information graph, knowledge graph and wisdom graph. In Proceedings of the international conference on SEKE (pp. 1–7).

  9. Duan, Y., Shao, L., Sun, X., Zhu, D., Yang, X., & Elfaki, A. O. (2017). An investment defined transaction processing towards temporal and spatial optimization with collaborative storage and computation adaptation. In Intelligent data engineering and automated learning-IDEAL (pp. 452–460).

  10. Duan, Y., Sun, X., Gao, H., Che, H., Cao, C., Li, Z., et al. (2019). Modeling data, information and knowledge for security protection of hybrid IoT and edge resources. IEEE Access, 7, 99161–99176.

    Article  Google Scholar 

  11. Duan, Y. (2019). Applications of relationship defined everything of semantics on existence computation. In IEEE SNPD2019 (pp. 184–189), July 8–11, 2019, Toyama, Japan.

  12. Duan, Y., Lu, Z., Zhou, Z., et al. (2019). Data privacy protection for edge computing of smart city in a DIKW architecture. Engineering Application of Artificial Intelligence, 81, 323–335. https://doi.org/10.1016/j.engappai.2019.03.002.

    Article  Google Scholar 

  13. Duan, Y. (2019). Existence computation: revelation on entity vs. relationship for relationship defined everything of semantics. In IEEE SNPD2019 (pp. 139–144), July 8–11, 2019, Toyama, Japan.

  14. Duan, Y. (2019). Towards a periodic table of conceptualization and formalization on state, style, structure, pattern, framework, architecture, service and so on. In IEEE SNPD2019 (pp. 133–138), July 8–11, 2019, Toyama, Japan.

  15. Gorschek, T., & Wohlin, C. (2006). Requirements abstraction model. Requirements Engineering, 11(1), 79–101.

    Article  Google Scholar 

  16. Song, Z., Duan, Y., Wan, S., Sun, X., Zou, Q., Gao, H., et al. (2018). Processing optimization of typed resources with synchronized storage and computation adaptation in for computing. Wireless Communications & Mobile Computing, 2018(2), 1–13.

    Google Scholar 

  17. Horovitz, O., & Karov, Y. (2002). Method for obtaining united information graph from multiple information resources. U.S. Patent No. 6,389,409. Washington, DC: U.S. Patent and Trademark Office.

  18. Clearwater, S. H., & Provost, F. J. (1990). RL4: A tool for knowledge-based induction. In International IEEE conference on TOOLS for artificial intelligence (pp. 24–30).

  19. Agrawal, R., Mannila, H., Srikant, R., Toivonen, H., & Verkamo, A. I. (1996). Fast discovery of association rules. In Advances in knowledge discovery and data mining (pp. 307–328).

  20. Rauch, J. (1999). Deduction in logic of association rules. In Asian computing science conference on advances in computing science (pp. 386–387).

  21. Rauch, J. (2018). Expert deduction rules in data mining with association rules: a case study. Knowledge and Information Systems, 4, 1–29.

    Google Scholar 

  22. Yin, Y., Song, A., Gao, M., Xu, Y., & Wang, S. (2016). QoS prediction for web service recommendation with network location-aware neighbor selection. International Journal of Software Engineering and Knowledge Engineering, 26(4), 611–632.

    Article  Google Scholar 

  23. Yin, Y., Xu, W., Xu, Y., Li, H., & Yu, L. (2017). Collaborative QoS prediction for mobile service with data filtering and SlopeOne model. Mobile Information Systems, 2017, 7356213:1–7356213:14.

    Google Scholar 

  24. Yin, Y., Yu, F., Xu, Y., Yu, L., & Mu, J. (2017). Network location-aware service recommendation with random walk in cyber-physical systems. Sensors, 17(9), 2059.

    Article  Google Scholar 

  25. Alsboui, T., Hammoudeh, M., & Abuarqoub, A. (2011). A service-centric stack for collaborative data sharing and processing. In Green and smart technology with sensor applications (pp. 320-327). Springer, Berlin.

  26. Yin, Y., Xu, Y., Xu, W., Gao, M., Yu, L., & Pei, Y. (2017). Collaborative service selection via ensemble learning in mixed mobile network environments. Entropy, 19(7), 358.

    Article  Google Scholar 

  27. Balzer, S., Liebig, T., & Wagner, M. (2004). Pitfalls of owl-s:a practical semantic web use case. In Service-oriented computing—ICSOC 2004, second international conference (pp. 289–298), New York, NY, USA, November 15–19, 2004.

  28. OConnor, M., Shankar, R., Tu, S., Nyulas, C., Parrish, D., Musen, M., et al. (2007). Using semantic web technologies for knowledge-driven querying of biomedical data. In Conference on artificial intelligence in medicine (pp. 267–276).

  29. Engels, G., Hausmann, J. H., Lohmann, M., & Sauer, S. (2005). Teaching UML is teaching software engineering is teaching abstraction. Lecture Notes in Computer Science, 3844, 306–319.

    Article  Google Scholar 

  30. Gorschek, T., & Wohlin, C. (2006). Requirements abstraction model. Requirements Engineering, 11(1), 79–101.

    Article  Google Scholar 

  31. Chittaro, L., & Ranon, R. (2004). Hierarchical model-based diagnosis based on structural abstraction. Artificial Intelligence, 155(1), 147–182.

    Article  MathSciNet  Google Scholar 

  32. Yin, Y., Chen, L., Xu, Y., & Wan, J. (2018). Location-aware service recommendation with enhanced probabilistic matrix factorization. IEEE Access, 6, 62815–62825.

    Article  Google Scholar 

  33. Athanasopoulos, D., Zarras, A., Issarny, V., & Associated, F. (2009). Foreversoa: Towards the maintenance of service oriented software. In 1st international workshop on ad-hoc ambient computing (pp. 555–559).

  34. Bartolini, C., Bertolino, A., Elbaum, S., & Marchetti, E. (2009). Whitening SOA testing. In The joint meeting of the european software engineering conference and the ACM sigsoft symposium on the foundations of software engineering (pp. 161–170).

  35. Li, L. (2003). A software framework for matchmaking based on semantic web technology. In International conference on world wide web (pp. 331–339).

  36. Chen, W., Han, Y., Liu, C., Wang, J., & Yan, S.: ASM-TL: An abstract service model enabling adaptive matchmaking. In IEEE international conference on e-business engineering (pp. 559–562).

  37. Berardi, D., Calvanese, D., Giacomo, G. D., Hull, R., & Mecella, M. (2005). Automatic composition of web services incolombo. In SEBD2015 (pp. 8–15).

  38. Berardi, D., Calvanese, D., Giacomo, G.D., Lenzerini, M., & Mecella, M. (2003). Automatic composition of e -services that export their behavior. In International conference on service-oriented computing (pp. 43–58).

  39. Bultan, T., Fu, X., Hull, R., & Su, J. (2003). Conversation specification: a new approach to design and analysis of e-service composition. In Proceedings of international conference on world wide web (pp. 403–410). ACM.

  40. Burstein, M., Hobbs, J., Lassila, O., Mcder mott, D., Mcilraith, S., Narayanan, S., et al. (2004). Owl-s: semantic markup for web services. In International semantic web working symposium (SWWS) (pp. 72–75).

  41. Isern, D., & Moreno, A. (2016). A systematic literature review of agents applied in healthcare. Journal of Medical Systems, 40(2), 1–14.

    Article  Google Scholar 

  42. Maglio, P. P., Vargo, S. L., Caswell, N., & Spohrer, J. (2009). The service system is markuthe basic abstraction of service science. Information Systems and e-Business Management, 7(4), 395–406.

    Article  Google Scholar 

  43. Baccarelli, E., Chiti, F., Cordeschi, N., & Fantacci, R. (2014). Green multimedia wireless sensor networks: distributed intelligent data fusion, in-network processing, and optimized resource management. Wireless Communications IEEE, 21(4), 20–26.

    Article  Google Scholar 

  44. Che, X., Liu, X., Ju, X., & Zhang, H. (2010). Adaptive instantiation of the protocol interference model in mission-critical wireless networks. In IEEE communications society conference on sensor, mesh and ad hoc communications and networks (pp. 1–9).

  45. Baskarada, S., & Koronios, A. (2013). Data, information, knowledge, wisdom (DIKW): A semiotic theoretical and empirical exploration of the hierarchy and its quality dimension. Australasian Journal of Information Systems, 18(1), 5–24.

  46. Aven, T. (2013). A conceptual framework for linking risk and the elements of the data-information-knowledge-wisdom (DIKW) hierarchy. Reliability Engineering & System Safety, 111, 30–36.

    Article  Google Scholar 

  47. Conger, S., & Probst, J.: Knowledge management in ITSM: Applying the DIKW Model. In Engineering and management of IT-based service systems (pp. 1–18) (2014)

  48. Tomita, Y., Watanabe, K., Shirasaka, S., & Maeno, T. (2017). Applying design thinking in systems engineering process as an extended version of DIKW model. Incose International Symposium, 27(1), 858–870.

    Article  Google Scholar 

  49. Merkus, J., Helms, R., & Kusters, R. (2019). Data governance and information governance: Set of definitions in relation to data and information as part of DIKW. In ICEIS2019 (pp. 143–154).

  50. Santoso, H. A., Haw, S. C., & Abdul-Mehdi, Z. T. (2011). Ontology extraction from relational database: Concept hierarchy as background knowledge. Knowledge-Based Systems, 24(3), 457–464.

    Article  Google Scholar 

  51. Abbott, M. B. (2003). The conversion of data into information for public participation in decision making processes. In Integrated technologies for environmental monitoring and information production (pp. 17–24). Dordrecht: Springer.

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Acknowledgments

This paper is supported by NSFC under Grant (Nos. 61363007, 61662021, 61661019), and Cernet Project Nos. NGII20180607 and NGII20170513.

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Authors and Affiliations

  1. Computing Center, Shanghai University, Shanghai, China

    Honghao Gao

  2. College of Information Science and Technology, Hainan University, Haikou, China

    Yucong Duan & Lixu Shao

  3. College of Computer and Information, Yangzhou University, Yangzhou, China

    Xiaobing Sun

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  1. Honghao Gao
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Correspondence to Yucong Duan.

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Gao, H., Duan, Y., Shao, L. et al. Transformation-based processing of typed resources for multimedia sources in the IoT environment. Wireless Netw 27, 3377–3393 (2021). https://doi.org/10.1007/s11276-019-02200-6

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