QoS prediction of Web service based on US-AWS
Abstract In parallel with the rapid growth of the number of research works, there exist some disadvantages and advantages of each model, how to combine the strengths of each model to create a new model for more effective. This is an important issue that should be studied in the context of increasing...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Van Thinh, Le [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2016 |
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| Schlagwörter: |
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| Anmerkung: |
© The Society of Service Science and Springer 2016 |
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| Übergeordnetes Werk: |
Enthalten in: Journal of service science - Berlin : Springer, 2009, 8(2016), 2 vom: Dez., Seite 193-205 |
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| Übergeordnetes Werk: |
volume:8 ; year:2016 ; number:2 ; month:12 ; pages:193-205 |
| Links: |
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| DOI / URN: |
10.1007/s12927-016-0010-y |
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SPR028478614 |
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10.1007/s12927-016-0010-y doi (DE-627)SPR028478614 (SPR)s12927-016-0010-y-e DE-627 ger DE-627 rakwb eng Van Thinh, Le verfasserin aut QoS prediction of Web service based on US-AWS 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Society of Service Science and Springer 2016 Abstract In parallel with the rapid growth of the number of research works, there exist some disadvantages and advantages of each model, how to combine the strengths of each model to create a new model for more effective. This is an important issue that should be studied in the context of increasing the number of new models. In this paper, we proposed a new combined model for predicting the QoS values of Web service. We first construct the AWS model based on an AutoenCoder with two predictions: User-based AWS (U-AWS) and Service-based AWS (S-AWS). Then we combine two predictions (U-AWS and S-AWS) to produce a more accurate one. To train the combined model, we generate the level one data by using the J-fold cross-validation data and using regression models to combine predictive modeling. Experimental results showed that the combined method has better results than single methods. Web Service (dpeaa)DE-He213 QoS Prediction (dpeaa)DE-He213 The Combined Model (dpeaa)DE-He213 Wang, HongBing aut Hau, Nguyen Xuan aut Enthalten in Journal of service science Berlin : Springer, 2009 8(2016), 2 vom: Dez., Seite 193-205 (DE-627)641391676 (DE-600)2583905-6 2092-5204 nnns volume:8 year:2016 number:2 month:12 pages:193-205 https://dx.doi.org/10.1007/s12927-016-0010-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_26 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2009 GBV_ILN_2111 GBV_ILN_2129 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4126 AR 8 2016 2 12 193-205 |
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10.1007/s12927-016-0010-y doi (DE-627)SPR028478614 (SPR)s12927-016-0010-y-e DE-627 ger DE-627 rakwb eng Van Thinh, Le verfasserin aut QoS prediction of Web service based on US-AWS 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Society of Service Science and Springer 2016 Abstract In parallel with the rapid growth of the number of research works, there exist some disadvantages and advantages of each model, how to combine the strengths of each model to create a new model for more effective. This is an important issue that should be studied in the context of increasing the number of new models. In this paper, we proposed a new combined model for predicting the QoS values of Web service. We first construct the AWS model based on an AutoenCoder with two predictions: User-based AWS (U-AWS) and Service-based AWS (S-AWS). Then we combine two predictions (U-AWS and S-AWS) to produce a more accurate one. To train the combined model, we generate the level one data by using the J-fold cross-validation data and using regression models to combine predictive modeling. Experimental results showed that the combined method has better results than single methods. Web Service (dpeaa)DE-He213 QoS Prediction (dpeaa)DE-He213 The Combined Model (dpeaa)DE-He213 Wang, HongBing aut Hau, Nguyen Xuan aut Enthalten in Journal of service science Berlin : Springer, 2009 8(2016), 2 vom: Dez., Seite 193-205 (DE-627)641391676 (DE-600)2583905-6 2092-5204 nnns volume:8 year:2016 number:2 month:12 pages:193-205 https://dx.doi.org/10.1007/s12927-016-0010-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_26 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2009 GBV_ILN_2111 GBV_ILN_2129 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4126 AR 8 2016 2 12 193-205 |
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10.1007/s12927-016-0010-y doi (DE-627)SPR028478614 (SPR)s12927-016-0010-y-e DE-627 ger DE-627 rakwb eng Van Thinh, Le verfasserin aut QoS prediction of Web service based on US-AWS 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Society of Service Science and Springer 2016 Abstract In parallel with the rapid growth of the number of research works, there exist some disadvantages and advantages of each model, how to combine the strengths of each model to create a new model for more effective. This is an important issue that should be studied in the context of increasing the number of new models. In this paper, we proposed a new combined model for predicting the QoS values of Web service. We first construct the AWS model based on an AutoenCoder with two predictions: User-based AWS (U-AWS) and Service-based AWS (S-AWS). Then we combine two predictions (U-AWS and S-AWS) to produce a more accurate one. To train the combined model, we generate the level one data by using the J-fold cross-validation data and using regression models to combine predictive modeling. Experimental results showed that the combined method has better results than single methods. Web Service (dpeaa)DE-He213 QoS Prediction (dpeaa)DE-He213 The Combined Model (dpeaa)DE-He213 Wang, HongBing aut Hau, Nguyen Xuan aut Enthalten in Journal of service science Berlin : Springer, 2009 8(2016), 2 vom: Dez., Seite 193-205 (DE-627)641391676 (DE-600)2583905-6 2092-5204 nnns volume:8 year:2016 number:2 month:12 pages:193-205 https://dx.doi.org/10.1007/s12927-016-0010-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_26 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2009 GBV_ILN_2111 GBV_ILN_2129 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4126 AR 8 2016 2 12 193-205 |
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10.1007/s12927-016-0010-y doi (DE-627)SPR028478614 (SPR)s12927-016-0010-y-e DE-627 ger DE-627 rakwb eng Van Thinh, Le verfasserin aut QoS prediction of Web service based on US-AWS 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Society of Service Science and Springer 2016 Abstract In parallel with the rapid growth of the number of research works, there exist some disadvantages and advantages of each model, how to combine the strengths of each model to create a new model for more effective. This is an important issue that should be studied in the context of increasing the number of new models. In this paper, we proposed a new combined model for predicting the QoS values of Web service. We first construct the AWS model based on an AutoenCoder with two predictions: User-based AWS (U-AWS) and Service-based AWS (S-AWS). Then we combine two predictions (U-AWS and S-AWS) to produce a more accurate one. To train the combined model, we generate the level one data by using the J-fold cross-validation data and using regression models to combine predictive modeling. Experimental results showed that the combined method has better results than single methods. Web Service (dpeaa)DE-He213 QoS Prediction (dpeaa)DE-He213 The Combined Model (dpeaa)DE-He213 Wang, HongBing aut Hau, Nguyen Xuan aut Enthalten in Journal of service science Berlin : Springer, 2009 8(2016), 2 vom: Dez., Seite 193-205 (DE-627)641391676 (DE-600)2583905-6 2092-5204 nnns volume:8 year:2016 number:2 month:12 pages:193-205 https://dx.doi.org/10.1007/s12927-016-0010-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_26 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2009 GBV_ILN_2111 GBV_ILN_2129 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4126 AR 8 2016 2 12 193-205 |
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10.1007/s12927-016-0010-y doi (DE-627)SPR028478614 (SPR)s12927-016-0010-y-e DE-627 ger DE-627 rakwb eng Van Thinh, Le verfasserin aut QoS prediction of Web service based on US-AWS 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Society of Service Science and Springer 2016 Abstract In parallel with the rapid growth of the number of research works, there exist some disadvantages and advantages of each model, how to combine the strengths of each model to create a new model for more effective. This is an important issue that should be studied in the context of increasing the number of new models. In this paper, we proposed a new combined model for predicting the QoS values of Web service. We first construct the AWS model based on an AutoenCoder with two predictions: User-based AWS (U-AWS) and Service-based AWS (S-AWS). Then we combine two predictions (U-AWS and S-AWS) to produce a more accurate one. To train the combined model, we generate the level one data by using the J-fold cross-validation data and using regression models to combine predictive modeling. Experimental results showed that the combined method has better results than single methods. Web Service (dpeaa)DE-He213 QoS Prediction (dpeaa)DE-He213 The Combined Model (dpeaa)DE-He213 Wang, HongBing aut Hau, Nguyen Xuan aut Enthalten in Journal of service science Berlin : Springer, 2009 8(2016), 2 vom: Dez., Seite 193-205 (DE-627)641391676 (DE-600)2583905-6 2092-5204 nnns volume:8 year:2016 number:2 month:12 pages:193-205 https://dx.doi.org/10.1007/s12927-016-0010-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_26 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_152 GBV_ILN_161 GBV_ILN_171 GBV_ILN_187 GBV_ILN_224 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2009 GBV_ILN_2111 GBV_ILN_2129 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4126 AR 8 2016 2 12 193-205 |
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Abstract In parallel with the rapid growth of the number of research works, there exist some disadvantages and advantages of each model, how to combine the strengths of each model to create a new model for more effective. This is an important issue that should be studied in the context of increasing the number of new models. In this paper, we proposed a new combined model for predicting the QoS values of Web service. We first construct the AWS model based on an AutoenCoder with two predictions: User-based AWS (U-AWS) and Service-based AWS (S-AWS). Then we combine two predictions (U-AWS and S-AWS) to produce a more accurate one. To train the combined model, we generate the level one data by using the J-fold cross-validation data and using regression models to combine predictive modeling. Experimental results showed that the combined method has better results than single methods. © The Society of Service Science and Springer 2016 |
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Abstract In parallel with the rapid growth of the number of research works, there exist some disadvantages and advantages of each model, how to combine the strengths of each model to create a new model for more effective. This is an important issue that should be studied in the context of increasing the number of new models. In this paper, we proposed a new combined model for predicting the QoS values of Web service. We first construct the AWS model based on an AutoenCoder with two predictions: User-based AWS (U-AWS) and Service-based AWS (S-AWS). Then we combine two predictions (U-AWS and S-AWS) to produce a more accurate one. To train the combined model, we generate the level one data by using the J-fold cross-validation data and using regression models to combine predictive modeling. Experimental results showed that the combined method has better results than single methods. © The Society of Service Science and Springer 2016 |
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Abstract In parallel with the rapid growth of the number of research works, there exist some disadvantages and advantages of each model, how to combine the strengths of each model to create a new model for more effective. This is an important issue that should be studied in the context of increasing the number of new models. In this paper, we proposed a new combined model for predicting the QoS values of Web service. We first construct the AWS model based on an AutoenCoder with two predictions: User-based AWS (U-AWS) and Service-based AWS (S-AWS). Then we combine two predictions (U-AWS and S-AWS) to produce a more accurate one. To train the combined model, we generate the level one data by using the J-fold cross-validation data and using regression models to combine predictive modeling. Experimental results showed that the combined method has better results than single methods. © The Society of Service Science and Springer 2016 |
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