Abstract
This paper proposes a self-adaptive mobile web service (MWS) discovery approach based on the modified negative selection algorithm (M-NSA) to improve the effectiveness and accuracy of MWS discovery in dynamic mobile environment. The main contributions of this work are the service relevance learning model and a MWS matchmaking algorithm that it is capable of changing as soon as the discovery demonstrates the feasibility of attaining improved effectiveness or accuracy. This is achieved by transforming the two stages of modified negative selection algorithm (M-NSA) into service relevance and self-adaptive matchmaking, respectively. The proposed approach is evaluated in terms of both binary and graded relevance. After an experiment with the largest MWS dataset, the proposed approach records better results in comparison with the state-of-the-art approaches. This is owing to the self/nonself discrimination mechanism, in addition to the decent parameter analysis, and the use of more comprehensive information that covers the entire discovery space.
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Bawazir A, Alhalabi W, Mohamed M, Sarirete A (2018) A formal approach for matching and ranking trustworthy context-dependent services. Appl Soft Comput J 73:306–315. https://doi.org/10.1016/j.asoc.2018.07.062
Elgazzar K, Hassanein H, Martin P (2014) Daas: cloud-based mobile web service discovery. Pervasive Mob Comput 13:67–84. https://doi.org/10.1016/j.pmcj.2013.10.015
Ruta M, Scioscia F, Di Sciascio E (2015) A mobile matchmaker for resource discovery in the ubiquitous semantic web. In: Proc—2015 IEEE 3rd Int Conf Mob Serv MS 2015 pp 336–343. https://doi.org/10.1109/MobServ.2015.76
Sivakumaran M, Iacopino P (2018) The mobile economy. GSMA Intell pp 11–11
Bobek S, Nalepa GJ (2017) Uncertain context data management in dynamic mobile environments. Future Gener Comput Syst 66:110–124. https://doi.org/10.1016/j.future.2016.06.007
Verma R, Srivastava A (2018) A dynamic web service registry framework for mobile environments. Peer-to-Peer Netw Appl 11:409–430. https://doi.org/10.1007/s12083-016-0540-6
Barakat L, Miles S, Luck M (2018) Adaptive composition in dynamic service environments. Future Gener Comput Syst 80:215–228. https://doi.org/10.1016/j.future.2016.12.003
Mezni H, Sellami M (2016) AWS-Ont: an ontology for the self-management of service-based systems. In: Proc—2015 IEEE 8th Int Conf Serv Comput Appl SOCA 2015 pp 85–92. https://doi.org/10.1109/SOCA.2015.17
Xiong R, Wang J, Zhang N, Ma Y (2018) Deep hybrid collaborative filtering for web service recommendation. Expert Syst Appl 110:191–205. https://doi.org/10.1016/j.eswa.2018.05.039
Zhang N, Wang J, Ma Y et al (2018) Web service discovery based on goal-oriented query expansion. J Syst Softw 142:73–91. https://doi.org/10.1016/j.jss.2018.04.046
Xie F, Wang J, Xiong R et al (2019) An integrated service recommendation approach for service-based system development. Expert Syst Appl 123:178–194. https://doi.org/10.1016/j.eswa.2019.01.025
Vargas-Santiago M, Morales-Rosales L, Pomares-Hernandez S, Drira K (2018) Autonomic web services enhanced by asynchronous checkpointing. IEEE Access 6:5538–5547. https://doi.org/10.1109/ACCESS.2017.2756867
Vinh PC (2016) Concurrency of self-∗in autonomic systems. Future Gener Comput Syst 56:140–152. https://doi.org/10.1016/j.future.2015.04.017
Klusch M, Kapahnke P, Schulte S et al (2016) Semantic web service search: a brief survey. KI—Künstliche Intell 30:139–147. https://doi.org/10.1007/s13218-015-0415-7
Ghahramani Z (2015) Probabilistic machine learning and artificial intelligence. Nature 521:452–459. https://doi.org/10.1038/nature14541
Muda NA, Muda AK, Huoy CY (2018) Recognizing music features pattern using modified negative selection algorithm for songs genre classification. In: Abraham A, Muhuri PK, Muda AK, Gandhi N (eds) Advances in intelligent systems and computing. Springer International Publishing, Cham, pp 242–251
Forrest S, Perelson AS, Allen L, Cherukuri R (1994) Self-nonself discrimination in a computer. In: Proc 1994 IEEE Comput Soc Symp Res Secur Priv pp 202–212. https://doi.org/10.1109/RISP.1994.296580
Dong H, Hussain FK, Chang E (2013) Semantic Web Service matchmakers: state of the art and challenges. Concurr Comput Pract Exp 25:961–988. https://doi.org/10.1002/cpe.2886
Gmati FE, Chakhar S, Ayadi NY, et al (2018) Efficient versus accurate algorithms for computing a semantic logic-based similarity measure. In: International conference on industrial, engineering and other applications of applied intelligent systems. Springer, Cham, pp 808–820
Pal K (2018) Ontology-based web service architecture for retail supply chain management. Procedia Comput Sci 130:985–990. https://doi.org/10.1016/j.procs.2018.04.101
Sambasivam G, Amudhavel J, Vengattaraman T, Dhavachelvan P (2018) An QoS based multifaceted matchmaking framework for web services discovery. Future Comput Inform J 3:371–383. https://doi.org/10.1016/j.fcij.2018.10.007
Abid A, Messai N, Rouached M, et al (2017) Semantic similarity based web services composition framework. In: Proceedings of the symposium on applied computing—SAC ’17. ACM, New York, NY, USA, pp 1319–1325
Corbellini A, Godoy D, Mateos C, et al (2017) Mining social web service repositories for social relationships to aid service discovery. In: IEEE international working conference on mining software repositories. IEEE Press, Piscataway, NJ, USA, pp 75–79
Stavropoulos TG, Andreadis S, Bassiliades N et al (2016) The tomaco hybrid matching framework for SAWSDL semantic web services. IEEE Trans Serv Comput 9:954–967. https://doi.org/10.1109/TSC.2015.2430328
Tripathy AK, Tripathy PK (2018) Fuzzy QoS requirement-aware dynamic service discovery and adaptation. Appl Soft Comput J 68:136–146. https://doi.org/10.1016/j.asoc.2018.03.038
Cheng B, Zhao S, Li C, Chen J (2017) A web services discovery approach based on mining underlying interface semantics. IEEE Trans Knowl Data Eng 29:950–962. https://doi.org/10.1109/TKDE.2016.2645769
Saadon NA, Mohamad R (2015) Semantic-based discovery framework for web services in mobile computing environment. J Teknol 77:25–38. https://doi.org/10.11113/jt.v77.6183
Cheng B, Li C, Zhao S, Chen J (2018) Semantics Mining & Indexing-based Rapid Web Services Discovery Framework. IEEE Trans Serv Comput 14:864–875. https://doi.org/10.1109/TSC.2018.2831678
Win NNH, Jianmin B, Gang C, Rehman SU (2019) Self-adaptive qos-aware web service discovery using ontology approach. In: Information Resources Management Association (IRMA) (ed) Web services: concepts, methodologies, tools, and applications. IGI Global, USA, pp 822–841. https://doi.org/10.4018/978-1-5225-7501-6
Athanasopoulos D (2017) Self-adaptive service organization for pragmatics-aware service discovery. In: Proceedings—2017 IEEE 14th international conference on services computing, SCC 2017. pp 164–171
Nabli H, Cherif S, Djmeaa R Ben, Amor IA Ben (2018) SADICO: self-adaptive approach to the web service composition. In: International conference on intelligent interactive multimedia systems and services. pp 254–267
Kafaf DAL, Kim DK (2017) A web service-based approach for developing self-adaptive systems. Comput Electr Eng 63:260–276. https://doi.org/10.1016/j.compeleceng.2017.06.030
Moreno GA, Cámara J, Garlan D, Klein M (2018) Uncertainty reduction in self-adaptive systems. In: 2018 IEEE/ACM 13th international symposium on software engineering for adaptive and self-managing systems (SEAMS). IEEE, pp 51–57
Paz A, Arboleda H (2016) A model to guide dynamic adaptation planning in self-adaptive systems. Electron Notes Theor Comput Sci 321:67–88
Di Nitto E, Ghezzi C, Metzger A et al (2008) A journey to highly dynamic, self-adaptive service-based applications. Autom Softw Eng 15:313–341. https://doi.org/10.1007/s10515-008-0032-x
He X, Liao L, Zhang H, et al (2017) Neural collaborative filtering. In: 26th Int World Wide Web Conf WWW 2017 pp 173–182. https://doi.org/10.1145/3038912.3052569
Cao B, Liu J, Wen Y et al (2019) QoS-aware service recommendation based on relational topic model and factorization machines for IoT Mashup applications. J Parallel Distrib Comput 132:177–189. https://doi.org/10.1016/j.jpdc.2018.04.002
Klusch M, Kapahnke P (2012) The iSeM matchmaker: a flexible approach for adaptive hybrid semantic service selection. J Web Semant 15:1–14. https://doi.org/10.1016/j.websem.2012.07.003
Ramdane C, Chikhi S (2017) Negative selection algorithm: recent improvements and its application in intrusion detection system. Int J Comput Acad Res 6:20–30
Liu Z, Li TAO, Yang JIN, Yang TAO (2017) An improved negative selection algorithm based on subspace density seeking. IEEE Access 5:12189–12198
Mohi-Aldeen SM, Mohamad R, Deris S (2016) Application of negative selection algorithm (NSA) for test data generation of path testing. Appl Soft Comput J 49:1118–1128. https://doi.org/10.1016/j.asoc.2016.09.044
Ji Z, Dasgupta D (2004) Real-valued negative selection algorithm with variable-sized detectors. Lect Notes Comput Sc 3102:287–298. https://doi.org/10.1007/978-3-540-24854-5_30
Fouladvand S, Osareh A, Shadgar B et al (2017) DENSA: an effective negative selection algorithm with flexible boundaries for self-space and dynamic number of detectors. Eng Appl Artif Intell 62:359–372. https://doi.org/10.1016/j.engappai.2016.08.014
Zeng J, Liu X, Li T et al (2009) A self-adaptive negative selection algorithm used for anomaly detection. Prog Nat Sci 19:261–266. https://doi.org/10.1016/j.pnsc.2008.06.008
Nanda SJ, Panda G (2014) A survey on nature inspired metaheuristic algorithms for partitional clustering. Swarm Evol Comput 16:1–18. https://doi.org/10.1016/j.swevo.2013.11.003
Dasgupta D, Yu S, Nino F (2011) Recent advances in artificial immune systems: Models and applications. Appl Soft Comput J 11:1574–1587. https://doi.org/10.1016/j.asoc.2010.08.024
Cui L, Pi D, Chen C (2015) BIORV-NSA: bidirectional inhibition optimization r-variable negative selection algorithm and its application. Appl Soft Comput J 32:544–552. https://doi.org/10.1016/j.asoc.2015.03.031
Zhu F, Chen W, Yang H et al (2017) A quick negative selection algorithm for one-class classification in big data era. Math Probl Eng. https://doi.org/10.1155/2017/3956415
Idris I, Selamat A, Thanh Nguyen N et al (2015) A combined negative selection algorithm-particle swarm optimization for an email spam detection system. Eng Appl Artif Intell 39:33–44. https://doi.org/10.1016/j.engappai.2014.11.001
Dong L, Liu S, Zhang H (2016) A boundary-fixed negative selection algorithm with online adaptive learning under small samples for anomaly detection. Eng Appl Artif Intell 50:93–105. https://doi.org/10.1016/j.engappai.2015.12.014
Dong L, Liu S, Zhang H (2017) A method of anomaly detection and fault diagnosis with online adaptive learning under small training samples. Pattern Recognit 64:374–385. https://doi.org/10.1016/j.patcog.2016.11.026
Zhao X, Wen Z, Li X (2014) QoS-aware web service selection with negative selection algorithm. Knowl Inf Syst 40:349–373. https://doi.org/10.1007/s10115-013-0642-x
Zhao X, Li R, Zuo X (2019) Advances on QoS-aware web service selection and composition with nature-inspired computing. CAAI Trans Intell Technol 4:159–174. https://doi.org/10.1049/trit.2019.0018
Garba S, Mohamad R, Saadon NA (2020) Search space reduction approach for self-adaptive web service discovery in dynamic mobile environment. In: Saeed F, Mohammed F, Gazem N (eds) Emerging trends in intelligent computing and informatics. Springer International Publishing, Cham, pp 1111–1121
Abid A, Khan MT, de Silva CW (2017) Layered and real-valued negative selection algorithm for fault detection. IEEE Syst J. https://doi.org/10.1109/JSYST.2017.2753851
Garba S, Mohamad R, Saadon NA (2020) Self-adaptive MWS matchmaker. GitHub Repos
Cao B, Frank Liu X, Liu J, Tang M (2017) Domain-aware Mashup service clustering based on LDA topic model from multiple data sources. Inf Softw Technol 90:40–54. https://doi.org/10.1016/j.infsof.2017.05.001
Xu Y, Goodacre R (2018) On splitting training and validation set: a comparative study of cross-validation, bootstrap and systematic sampling for estimating the generalization performance of supervised learning. J Anal Test 2:249–262. https://doi.org/10.1007/s41664-018-0068-2
Tian G, Zhao S, Wang J et al (2019) Semantic sparse service discovery using word embedding and Gaussian LDA. IEEE Access 7:88231–88242. https://doi.org/10.1109/ACCESS.2019.2926559
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Garba, S., Mohamad, R. & Saadon, N.A. Self-adaptive mobile web service discovery approach based on modified negative selection algorithm. Neural Comput & Applic 34, 2007–2029 (2022). https://doi.org/10.1007/s00521-021-06486-6
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DOI: https://doi.org/10.1007/s00521-021-06486-6