Automatic Web service composition driven by keyword query

Service-based systems (SBSs) reuse existing loosely coupled Web services to provide value-added composite ones, which brings about much flexibility when the business changes frequently. The advent of automatic Web service composition technology allows system designers to quickly build SBSs without having to manually create process models. Despite the large number of strategies proposed so far, most of them compose Web services through the user-provided initial inputs and expected target outputs, which is not convenient for users to express their functional requirements. To address this issue, we allow users to employ keywords to represent key tasks of the composed Web services. To automatically compose Web services based on the given keywords, we study a new problem of keyword search in the AND/OR graph constructed through semantically matching input-output interfaces of existing related Web services. Due to the complexity of the problem, we propose a heuristic search approach, called UP-DFS. To improve the performance of UP-DFS, we further design two types of pruning strategies. The empirical study shows that our approach can efficiently generate a semantic input-output-based Web service composition that contains all the key tasks in the right order required by users while minimizing the number of services in the composition.

[1]  Soundar R. T. Kumara,et al.  Web Service Planner (WSPR): An Effective and Scalable Web Service Composition Algorithm , 2007, Int. J. Web Serv. Res..

[2]  Wei Jiang,et al.  QSynth: A Tool for QoS-aware Automatic Service Composition , 2010, 2010 IEEE International Conference on Web Services.

[3]  Takahiro Kawamura,et al.  Semantic Matching of Web Services Capabilities , 2002, SEMWEB.

[4]  Manuel Mucientes,et al.  Automatic Web Service Composition with a Heuristic-Based Search Algorithm , 2011, 2011 IEEE International Conference on Web Services.

[5]  Mohamed Graiet,et al.  Formal Behavioral Modeling for Verifying SCA Composition with Event-B , 2015, 2015 IEEE International Conference on Web Services.

[6]  Bernd Kleinjohann,et al.  Automatic Composition of Service-Based Image Processing Applications , 2016, 2016 IEEE International Conference on Services Computing (SCC).

[7]  S. Sudarshan,et al.  Keyword searching and browsing in databases using BANKS , 2002, Proceedings 18th International Conference on Data Engineering.

[8]  S. E. Dreyfus,et al.  The steiner problem in graphs , 1971, Networks.

[9]  Min Chen,et al.  Redundant Service Removal in QoS-Aware Service Composition , 2012, 2012 IEEE 19th International Conference on Web Services.

[10]  Aijun An,et al.  Keyword Search in Graphs: Finding r-cliques , 2011, Proc. VLDB Endow..

[11]  Lu Fang,et al.  Towards Automatic Tagging for Web Services , 2012, 2012 IEEE 19th International Conference on Web Services.

[12]  Xuanzhe Liu,et al.  Data-Driven Composition for Service-Oriented Situational Web Applications , 2015, IEEE Transactions on Services Computing.

[13]  Jianxin Li,et al.  Finding smallest k-Compact tree set for keyword queries on graphs using mapreduce , 2015, World Wide Web.

[14]  Manuel Mucientes,et al.  Hybrid Optimization Algorithm for Large-Scale QoS-Aware Service Composition , 2015, IEEE Transactions on Services Computing.

[15]  Matthias Klusch,et al.  OWLS-MX: A hybrid Semantic Web service matchmaker for OWL-S services , 2009, J. Web Semant..

[16]  Eric Bouillet,et al.  Wishful search: interactive composition of data mashups , 2008, WWW.

[17]  Qiang He,et al.  Keyword Search for Building Service-Based Systems , 2017, IEEE Transactions on Software Engineering.

[18]  Jianxin Li,et al.  Efficient Batch Processing for Multiple Keyword Queries on Graph Data , 2016, CIKM.

[19]  Michael Luck,et al.  Efficient Multi-granularity Service Composition , 2011, 2011 IEEE International Conference on Web Services.

[20]  Matthias Klusch,et al.  WSMO-MX: A hybrid Semantic Web service matchmaker , 2009, Web Intell. Agent Syst..

[21]  Jeffrey Xu Yu,et al.  Efficient and Progressive Group Steiner Tree Search , 2016, SIGMOD Conference.

[22]  M. Brian Blake,et al.  Model-Based Automated Navigation and Composition of Complex Service Mashups , 2015, IEEE Transactions on Services Computing.

[23]  Abdullah Abdullah,et al.  Agent-Based Model to Web Service Composition , 2016, 2016 IEEE International Conference on Services Computing (SCC).

[24]  Yuhong Yan,et al.  An Efficient Syntactic Web Service Composition Algorithm Based on the Planning Graph Model , 2008, 2008 IEEE International Conference on Web Services.

[25]  Matthias Klusch,et al.  Hybrid Adaptive Web Service Selection with SAWSDL-MX and WSDL-Analyzer , 2009, ESWC.

[26]  Mohamed Graiet,et al.  Formal Modeling for Verifying SCA Dynamic Composition with Event-B , 2015, 2015 IEEE 24th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises.

[27]  Manuel Mucientes,et al.  An Integrated Semantic Web Service Discovery and Composition Framework , 2015, IEEE Transactions on Services Computing.

[28]  Keqing He,et al.  Web service discovery based on goal-oriented query expansion , 2018, J. Syst. Softw..

[29]  Chengfei Liu,et al.  No-but-semantic-match: computing semantically matched xml keyword search results , 2017, World Wide Web.

[30]  Matthias Klusch,et al.  Adaptive signature-based semantic selection of services with OWLS-MX3 , 2012, Multiagent Grid Syst..

[31]  S. Sudarshan,et al.  Bidirectional Expansion For Keyword Search on Graph Databases , 2005, VLDB.

[32]  Shan Wang,et al.  Finding Top-k Min-Cost Connected Trees in Databases , 2007, 2007 IEEE 23rd International Conference on Data Engineering.

[33]  Marta Rukoz,et al.  Modeling dynamic recovery strategy for composite web services execution , 2015, World Wide Web.

[34]  Mohamed Abid,et al.  Semantic similarity based web services composition framework , 2017, SAC.

[35]  Qiang He,et al.  Efficient Keyword Search for Building Service-Based Systems Based on Dynamic Programming , 2017, ICSOC.