Reliability Prediction of Ontology-Based Service Compositions Using Petri Net and Time Series Models
OWL-S, one of the most important Semantic Web service ontologies proposed to date, provides a core ontological framework and guidelines for describing the properties and capabilities of their web services in an unambiguous, computer interpretable form. Predicting the reliability of composite service...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/760202 |
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| author | Jia Li Yunni Xia Xin Luo |
| author_facet | Jia Li Yunni Xia Xin Luo |
| author_sort | Jia Li |
| collection | DOAJ |
| description | OWL-S, one of the most important Semantic Web service ontologies proposed to date, provides a core ontological framework and guidelines for describing the properties and capabilities of their web services in an unambiguous, computer interpretable form. Predicting the reliability of composite service processes specified in OWL-S allows service users to decide whether the process meets the quantitative quality requirement. In this study, we consider the runtime quality of services to be fluctuating and introduce a dynamic framework to predict the runtime reliability of services specified in OWL-S, employing the Non-Markovian stochastic Petri net (NMSPN) and the time series model. The framework includes the following steps: obtaining the historical response times series of individual service components; fitting these series with a autoregressive-moving-average-model (ARMA for short) and predicting the future firing rates of service components; mapping the OWL-S process into a NMSPN model; employing the predicted firing rates as the model input of NMSPN and calculating the normal completion probability as the reliability estimate. In the case study, a comparison between the static model and our approach based on experimental data is presented and it is shown that our approach achieves higher prediction accuracy. |
| format | Article |
| id | doaj-art-ea25c7f7c78f44c18333c059e954cdd0 |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-ea25c7f7c78f44c18333c059e954cdd02025-02-03T05:59:30ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/760202760202Reliability Prediction of Ontology-Based Service Compositions Using Petri Net and Time Series ModelsJia Li0Yunni Xia1Xin Luo2Chongqing Technology and Business Institute, Chongqing 400052, ChinaSoftware Theory and Technology Chongqing Key Lab, Chongqing University, Chongqing 40030, ChinaSoftware Theory and Technology Chongqing Key Lab, Chongqing University, Chongqing 40030, ChinaOWL-S, one of the most important Semantic Web service ontologies proposed to date, provides a core ontological framework and guidelines for describing the properties and capabilities of their web services in an unambiguous, computer interpretable form. Predicting the reliability of composite service processes specified in OWL-S allows service users to decide whether the process meets the quantitative quality requirement. In this study, we consider the runtime quality of services to be fluctuating and introduce a dynamic framework to predict the runtime reliability of services specified in OWL-S, employing the Non-Markovian stochastic Petri net (NMSPN) and the time series model. The framework includes the following steps: obtaining the historical response times series of individual service components; fitting these series with a autoregressive-moving-average-model (ARMA for short) and predicting the future firing rates of service components; mapping the OWL-S process into a NMSPN model; employing the predicted firing rates as the model input of NMSPN and calculating the normal completion probability as the reliability estimate. In the case study, a comparison between the static model and our approach based on experimental data is presented and it is shown that our approach achieves higher prediction accuracy.http://dx.doi.org/10.1155/2014/760202 |
| spellingShingle | Jia Li Yunni Xia Xin Luo Reliability Prediction of Ontology-Based Service Compositions Using Petri Net and Time Series Models The Scientific World Journal |
| title | Reliability Prediction of Ontology-Based Service Compositions Using Petri Net and Time Series Models |
| title_full | Reliability Prediction of Ontology-Based Service Compositions Using Petri Net and Time Series Models |
| title_fullStr | Reliability Prediction of Ontology-Based Service Compositions Using Petri Net and Time Series Models |
| title_full_unstemmed | Reliability Prediction of Ontology-Based Service Compositions Using Petri Net and Time Series Models |
| title_short | Reliability Prediction of Ontology-Based Service Compositions Using Petri Net and Time Series Models |
| title_sort | reliability prediction of ontology based service compositions using petri net and time series models |
| url | http://dx.doi.org/10.1155/2014/760202 |
| work_keys_str_mv | AT jiali reliabilitypredictionofontologybasedservicecompositionsusingpetrinetandtimeseriesmodels AT yunnixia reliabilitypredictionofontologybasedservicecompositionsusingpetrinetandtimeseriesmodels AT xinluo reliabilitypredictionofontologybasedservicecompositionsusingpetrinetandtimeseriesmodels |