A linear logic approach to the composition of RESTful web services

Journal article


Zhao, X., Liu, E., Yu, H. and Clapworthy, G.J. 2015. A linear logic approach to the composition of RESTful web services. International Journal of Web Engineering and Technology. 10 (3), pp. 245-271. https://doi.org/10.1504/ijwet.2015.072348
AuthorsZhao, X., Liu, E., Yu, H. and Clapworthy, G.J.
Abstract

RESTful web services, which are declarative, lightweight and easy to access, are already widely used for exposing services on the internet and have attracted increasing interest from industry. The rising number of services being implemented and made available on the web is creating a demand for modelling techniques that can abstract REST design from the implementation in order to better specify, analyse and implement large-scale RESTful web systems. It can also help by providing suitable RESTful web service composition methods which can reduce costs by efficiently re-using the large number of services that are already available and by exploiting existing services for complex business purposes. This paper introduces a formal model of RESTful web services in linear logic and proposes a formal method approach for RESTful web service composition based on planning using linear logic via theorem proving. This is a two-stage planning method that finds resources for the composition at both the abstract resource and the service operation levels. It greatly improves the searching efficiency and guarantees the correctness and completeness of the service composition process. The paper demonstrates a further validation of the planning method through its implementation in the interactive Coq logic proof assistant.

KeywordsRESTful web services; web service composition; linear logic; pi-calculus; theorem proving; Coq
Year2015
JournalInternational Journal of Web Engineering and Technology
Journal citation10 (3), pp. 245-271
PublisherInderscience
ISSN1476-1289
1741-9212
Digital Object Identifier (DOI)https://doi.org/10.1504/ijwet.2015.072348
Web address (URL)https://www.inderscienceonline.com/doi/abs/10.1504/IJWET.2015.072348
Output statusPublished
Publication dates
Online12 Oct 2015
Publication process dates
Deposited24 Jun 2022
Permalink -

https://repository.derby.ac.uk/item/97565/a-linear-logic-approach-to-the-composition-of-restful-web-services

  • 45
    total views
  • 0
    total downloads
  • 0
    views this month
  • 0
    downloads this month

Export as

Related outputs

Explainable DCNN Decision Framework for Breast Lesion Classification from Ultrasound Images Based on Cancer Characteristics
AlZoubi, A., Eskandari, A., Yu, H. and Du, H. 2024. Explainable DCNN Decision Framework for Breast Lesion Classification from Ultrasound Images Based on Cancer Characteristics . Bioengineering. 11 (5), pp. 1-23. https://doi.org/10.3390/bioengineering11050453
An Experimental Study of Integrating Fine-tuned LLMs and Prompts for Enhancing Mental Health Support Chatbot System
Yu, H. and McGuinness, S. 2024. An Experimental Study of Integrating Fine-tuned LLMs and Prompts for Enhancing Mental Health Support Chatbot System. Journal of Medical Artificial Intelligence. pp. 1-16. https://doi.org/10.21037/jmai-23-1
Deep Recognition of Chinese Herbal Medicines Based on a Caputo Fractional Order Convolutional Neural Network
Tao Li, Jiawei Yang, Chenxi Li, Lulu Lv, Kang Liu, Zhipeng Yuan, Youyong Li, Hongqing Yu and Yu, H. 2024. Deep Recognition of Chinese Herbal Medicines Based on a Caputo Fractional Order Convolutional Neural Network. International Workshop on Internet of Things of Big Data for Healthcare. Springer. https://doi.org/10.1007/978-3-031-52216-1_4
Evaluation of Integrated XAI Frameworks for Explaining Disease Prediction Models in Healthcare
Yu, H., Adebola Alaba and Ebere Eziefuna 2024. Evaluation of Integrated XAI Frameworks for Explaining Disease Prediction Models in Healthcare. International Workshop on Internet of Things of Big Data for Healthcare. Springer. https://doi.org/10.1007/978-3-031-52216-1_2
Attention Enhanced Siamese Neural Network for Face Validation
Yu, H. 2023. Attention Enhanced Siamese Neural Network for Face Validation. Artificial Intelligence and Applications. 2 (1), pp. 21-27. https://doi.org/10.47852/bonviewAIA32021018
IoTBDH-2023: The 5th International Workshop on Internet of Things of Big Data for Healthcare
Qi, J., Yu, H., Yang, P., Yang, Y. and Pang, Z. 2023. IoTBDH-2023: The 5th International Workshop on Internet of Things of Big Data for Healthcare. 32nd ACM International Conference on Information and Knowledge Management (CIKM’23), Birmingham, UK. ACM. https://doi.org/10.1145/3583780.3615299
AIMS: An Automatic Semantic Machine Learning Microservice Framework to Support Biomedical and Bioengineering Research
Yu, H., O'Neill, S. and Kermanizadeh, A. 2023. AIMS: An Automatic Semantic Machine Learning Microservice Framework to Support Biomedical and Bioengineering Research. Bioengineering. 10 (10), pp. 1-18. https://doi.org/10.3390/bioengineering10101134
Learning Disease Causality Knowledge from Web of Health Data
Yu, H. and Reiff-Marganiec, S. 2022. Learning Disease Causality Knowledge from Web of Health Data. International journal on semantic web and information systems. 18 (1), pp. 1-19. https://doi.org/10.4018/IJSWIS.297145
Experimental Disease Prediction Research on Combining Natural Language Processing and Machine Learning
Yu, H. 2020. Experimental Disease Prediction Research on Combining Natural Language Processing and Machine Learning. IEEE 7th International Conference on Computer Science and Network Technology (ICCSNT). IEEE Xplore. https://doi.org/10.1109/iccsnt47585.2019.8962507
Dynamic Causality Knowledge Graph Generation for Supporting the Chatbot Healthcare System
Yu, H. 2020. Dynamic Causality Knowledge Graph Generation for Supporting the Chatbot Healthcare System. in: Arai, Kohei, Kapoor, Supriya and Bhatia, Rahul (ed.) Proceedings of the Future Technologies Conference (FTC) 2020, Volume 3 New York Springer.
Low-Cost and Data Anonymised City Traffic Flow Data Collection to Support Intelligent Traffic System
Handscombe, J. and Yu, H. 2019. Low-Cost and Data Anonymised City Traffic Flow Data Collection to Support Intelligent Traffic System. Sensors. 19 (2), p. 347. https://doi.org/10.3390/s19020347
Semantic Lifting and Reasoning on the Personalised Activity Big Data Repository for Healthcare Research
Yu, H. and Dong, F. 2019. Semantic Lifting and Reasoning on the Personalised Activity Big Data Repository for Healthcare Research. International Journal of Web Engineering and Technology. 14 (2), pp. 103 - 121.
Mining Symptom and Disease Web Data with NLP and Open Linked Data
Yu, H. 2019. Mining Symptom and Disease Web Data with NLP and Open Linked Data. 5th World Congress on Electrical Engineering and Computer Systems and Sciences (EECSS’19) Lisbon, Portugal – August, 2019. https://doi.org/10.11159/mvml19.108
Socio-semantic Integration of Educational Resources - the Case of the mEducator Project
Dietze, Stefan, Kaldoudi, Eleni, Dovrolis, Nikolas, Giordano, Daniela, Spampinato, Concetto, Hendrix, Maurice, Protopsaltis, Aristidis, Taibi, v and Yu, H. 2013. Socio-semantic Integration of Educational Resources - the Case of the mEducator Project. Journal of Universal Computer Science. 19 (11), pp. 1-27. https://doi.org/10.3217/jucs-019-11-1543
Interlinking educational resources and the web of data
Dietze, S., Sanchez‐Alonso, S., Ebner, H., Yu, H., Giordano, D., Marenzi, I. and Pereira Nunes, B. 2013. Interlinking educational resources and the web of data. Program. 47 (1). https://doi.org/10.1108/00330331211296312
Using Linked Data to Annotate and Search Educational Video Resources for Supporting Distance Learning
Yu, H., Pedrinaci, C., Dietze, S. and Domingue, J. 2012. Using Linked Data to Annotate and Search Educational Video Resources for Supporting Distance Learning. IEEE Transactions on Learning Technologies. 5 (2), pp. 130-142. https://doi.org/10.1109/tlt.2012.1
An automated approach to Semantic Web Services Mediation
Dietze, S., Gugliotta, A., Domingue, J., Yu, H. and Mrissa, M. 2010. An automated approach to Semantic Web Services Mediation. Service Oriented Computing and Applications. 4, p. 261–275. https://doi.org/10.1007/s11761-010-0070-7