A method towards Web service combination for cross-organisational business process using QoS and cluster

ABSTRACT How to quickly combine various Web services to support cross-organisational business processes is the key technical problem in service computing. Because of the changeability of QoS of Web services, the traditional methods are unadaptable to the new environments. In this paper, a new service composition method, called Improved Self-organising neural network Method for Web Service Composition, is proposed to achieve QoS-aware Web service combination, according to using the clustering technology. First, fuzzy mathematics is used to express each QoS attribute and the improved self-organising neural network is used to cluster services to reduce the number of candidate services. Secondly, all the centre of each cluster is selected and formed a composite service by using an exhaustive algorithm. Thirdly, the service cluster that is represented by the concrete services. Finally, the optimal service combination can be selected using integer programming or genetic algorithm. The experimental result shows the efficiency of Web service composition and demonstrates the applicability.

[1]  Jing Zhao,et al.  A decomposition-based approach for service composition with global QoS guarantees , 2012, Inf. Sci..

[2]  Zhu Zhiliang,et al.  Study and Improvement on Web Services Clustering Approach , 2012 .

[3]  Qibo Sun,et al.  Web Service Dynamic Selection by the Decomposition of Global QoS Constraints: Web Service Dynamic Selection by the Decomposition of Global QoS Constraints , 2011 .

[4]  Athman Bouguettaya,et al.  QoS Analysis for Web Service Compositions Based on Probabilistic QoS , 2011, ICSOC.

[5]  R. J. Kuo,et al.  Combining SOM and evolutionary computation algorithms for RBF neural network training , 2019, J. Intell. Manuf..

[6]  Néjib Moalla,et al.  Dynamic Execution of a Business Process via Web Service Selection and Orchestration , 2015, ICCS.

[7]  Shuanyu Dong,et al.  A QoS Driven Web Service Composition Method Based on ESGA (Elitist Selection Genetic Algorithm) with an Improved Initial Population Selection Strategy , 2009 .

[8]  S. Swamynathan,et al.  Process model-based atomic service discovery and composition of composite semantic web services using web ontology language for services (OWL-S) , 2012, Enterp. Inf. Syst..

[9]  Wang Shang.  Web Service Dynamic Selection by the Decomposition of Global QoS Constraints , 2011 .

[10]  Wattana Viriyasitavat,et al.  Blockchain characteristics and consensus in modern business processes , 2019, J. Ind. Inf. Integr..

[11]  Xiaofei Xu,et al.  Semi-empirical Service Composition: A Clustering Based Approach , 2011, 2011 IEEE International Conference on Web Services.

[12]  Wolfgang Nejdl,et al.  A hybrid approach for efficient Web service composition with end-to-end QoS constraints , 2012, TWEB.

[13]  MengChu Zhou,et al.  Constraint-Aware Approach to Web Service Composition , 2014, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[14]  Xining Li,et al.  An efficient I/O based clustering HTN in Web Service Composition , 2013, 2013 International Conference on Computing, Management and Telecommunications (ComManTel).

[15]  Christos K. Georgiadis,et al.  QoS‐Based Filters in Web Service Compositions: Utilizing Multi‐Criteria Decision Analysis Methods , 2015 .

[16]  Yong Zhang,et al.  A service-cluster based approach to service substitution of web service composition , 2012, Proceedings of the 2012 IEEE 16th International Conference on Computer Supported Cooperative Work in Design (CSCWD).

[17]  Teuvo Kohonen,et al.  Self-organized formation of topologically correct feature maps , 2004, Biological Cybernetics.

[18]  Eyhab Al-Masri,et al.  Investigating web services on the world wide web , 2008, WWW.

[19]  Ahmed Ghoneim,et al.  Enhanced Artificial Bee Colony Algorithm for QoS-aware Web Service Selection problem , 2017, Computing.

[20]  Xinchao Zhao,et al.  An improved discrete immune optimization algorithm based on PSO for QoS-driven web service composition , 2012, Appl. Soft Comput..

[21]  Ying Chen,et al.  A Partial Selection Methodology for Efficient QoS-Aware Service Composition , 2015, IEEE Transactions on Services Computing.

[22]  Zhang Chengwen.  Hybrid QoS-Clustering Web Service Composition , 2011 .

[23]  MengChu Zhou,et al.  Automated web service composition supporting conditional branch structures , 2014, Enterp. Inf. Syst..

[24]  Li D. Xu,et al.  QoS Recommendation in Cloud Services , 2017, IEEE Access.

[25]  Mingdong Tang,et al.  Diversifying Web Service Recommendation Results via Exploring Service Usage History , 2016, IEEE Transactions on Services Computing.

[26]  Lida Xu,et al.  A Novel Architecture for Requirement-Oriented Participation Decision in Service Workflows , 2014, IEEE Transactions on Industrial Informatics.

[27]  Runliang Dou,et al.  A QoS-oriented Web service composition approach based on multi-population genetic algorithm for Internet of things , 2014, Int. J. Comput. Intell. Syst..

[28]  Su Sen,et al.  Fuzzy Multi-Attribute Decision Making-Based Algorithm for Semantic Web Service Composition , 2009 .

[29]  Mengjie Zhang,et al.  Genetic programming for QoS-aware web service composition and selection , 2016, Soft Comput..

[30]  N. Rawal,et al.  Patient controlled regional analgesia after carpal tunnel release: a double-blind study using distal perineural catheters , 2011, The Journal of hand surgery, European volume.

[31]  Chee-Wee Tan,et al.  Traversing knowledge networks: an algorithmic historiography of extant literature on the Internet of Things (IoT) , 2017 .

[32]  Xiao Xue,et al.  Reliable Web service composition based on QoS dynamic prediction , 2015, Soft Comput..

[33]  Manas Ranjan Patra,et al.  Dynamic Web Service Composition with QoS Clustering , 2014, 2014 IEEE International Conference on Web Services.

[34]  Haithem Mezni,et al.  Reusing process fragments for fast service composition: a clustering-based approach , 2019, Enterp. Inf. Syst..

[35]  MengChu Zhou,et al.  A Web service substitution method based on service cluster nets , 2017, Enterp. Inf. Syst..

[36]  Dimitrios Tzovaras,et al.  A framework for inspection of dies attachment on PCB utilizing machine learning techniques , 2018 .

[37]  Thomas Risse,et al.  Combining global optimization with local selection for efficient QoS-aware service composition , 2009, WWW '09.

[38]  M. Melchiori,et al.  An ontology-based method for classifying and searching e-Services , 2006 .

[39]  Athman Bouguettaya,et al.  QoS Analysis for Web Service Compositions with Complex Structures , 2013, IEEE Transactions on Services Computing.

[40]  Lizhen Cui,et al.  Cloud Service Composition Based on Multi-Granularity Clustering , 2014 .

[41]  Lida Xu,et al.  Enterprise Systems: State-of-the-Art and Future Trends , 2011, IEEE Transactions on Industrial Informatics.

[42]  Ling Li,et al.  QoS-Aware Scheduling of Services-Oriented Internet of Things , 2014, IEEE Transactions on Industrial Informatics.

[43]  Zhijun Ding,et al.  A genetic algorithm based approach to transactional and QoS-aware service selection , 2017, Enterp. Inf. Syst..

[44]  Zhen Li,et al.  Fuzzy Multi-Attribute Decision Making-Based Algorithm for Semantic Web Service Composition: Fuzzy Multi-Attribute Decision Making-Based Algorithm for Semantic Web Service Composition , 2010 .

[45]  Adam Prügel-Bennett,et al.  Novel centroid selection approaches for KMeans-clustering based recommender systems , 2015, Inf. Sci..