Qos-based pattern recognition approach for web service discovery: Ar_wsds(Article)
Web service composition requires high levels of integration and reliability of the services involved in its operation, which must meet specific quality criteria to ensure their proper execution and deployment. The discovery and selection of web services currently face optimization problems. Many ser...
- Autores:
-
Adarme Jaimes, Marco Antonio
Jimeno, Miguel
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2021
- Institución:
- Universidad Francisco de Paula Santander
- Repositorio:
- Repositorio Digital UFPS
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.ufps.edu.co:ufps/6611
- Acceso en línea:
- https://repositorio.ufps.edu.co/handle/ufps/6611
- Palabra clave:
- web service composition
cloud computing
pattern recognition
- Rights
- openAccess
- License
- © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
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Qos-based pattern recognition approach for web service discovery: Ar_wsds(Article) |
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Qos-based pattern recognition approach for web service discovery: Ar_wsds(Article) |
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Qos-based pattern recognition approach for web service discovery: Ar_wsds(Article) web service composition cloud computing pattern recognition |
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Qos-based pattern recognition approach for web service discovery: Ar_wsds(Article) |
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Qos-based pattern recognition approach for web service discovery: Ar_wsds(Article) |
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Qos-based pattern recognition approach for web service discovery: Ar_wsds(Article) |
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Qos-based pattern recognition approach for web service discovery: Ar_wsds(Article) |
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Qos-based pattern recognition approach for web service discovery: Ar_wsds(Article) |
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Adarme Jaimes, Marco Antonio Jimeno, Miguel |
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Adarme Jaimes, Marco Antonio Jimeno, Miguel |
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Applied Sciences |
| dc.subject.proposal.eng.fl_str_mv |
web service composition cloud computing pattern recognition |
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web service composition cloud computing pattern recognition |
| description |
Web service composition requires high levels of integration and reliability of the services involved in its operation, which must meet specific quality criteria to ensure their proper execution and deployment. The discovery and selection of web services currently face optimization problems. Many services might satisfy a requirement with similar quality criteria. Because of this, software developers have to choose the most appropriate services for a given composition, complicated by the rapid increase in providers and services available in the cloud. Service composition also implies coupling according to a composition flow and non-functional requirement criteria. Such requirements make selection and composition a complex task not previously solved in the literature. This paper presents Ar_WSDS, a computational approach for web services discovery and selection in cloud environments, which bases its implementation on the brain’s pattern recognition systematic functioning. This process allows classifying web services through recognition modules created dynamically based on their quality parameters, resulting in a set of web services suitable for a web service composition. This approach allows a solution to the selection problem using less complex tasks. This paper introduces an architectural and procedural definition that provides the web service description with a pattern to recognize and select services using different recognition levels. We simulated our approach and evaluated it using a dataset from the QWS project that offers a set of quality criteria collected from different providers. The web services are recognized and classified using different quality criteria for the composition and each of their services. The results demonstrate the effectiveness of the discovery and selection process compared to other approaches. Furthermore, Ar_WSDS allows us to recognize and filter out web services with ambiguity and similarity in their provider information, a process that minimizes the discovery space for services. |
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2021 |
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2021-08-31 |
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2022-11-25T15:42:12Z |
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2022-11-25T15:42:12Z |
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10.3390/app11178092 |
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Applied Sciences Volume 11, Issue 17, No. 8092 (2021) |
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Vol. 17 (11) N0.8092 (2021) |
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24 |
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11, 8092 (2021) |
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Adarme, M., & Jimeno, M. (2021). QoS-Based Pattern Recognition Approach for Web Service Discovery: Ar_WSDS. Applied Sciences, 11(17), 8092. MDPI AG. Retrieved from http://dx.doi.org/10.3390/app11178092 |
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Applied Sciences |
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© 2021 by the authors. Licensee MDPI, Basel, Switzerland. |
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24 Páginas |
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Adarme Jaimes, Marco Antonio364827d63168d535a5b7f6315210f852600Jimeno, Miguel4632df6dc54936b0dabeb14a423d307d600Applied Sciences2022-11-25T15:42:12Z2022-11-25T15:42:12Z2021-08-31https://repositorio.ufps.edu.co/handle/ufps/661110.3390/app11178092Web service composition requires high levels of integration and reliability of the services involved in its operation, which must meet specific quality criteria to ensure their proper execution and deployment. The discovery and selection of web services currently face optimization problems. Many services might satisfy a requirement with similar quality criteria. Because of this, software developers have to choose the most appropriate services for a given composition, complicated by the rapid increase in providers and services available in the cloud. Service composition also implies coupling according to a composition flow and non-functional requirement criteria. Such requirements make selection and composition a complex task not previously solved in the literature. This paper presents Ar_WSDS, a computational approach for web services discovery and selection in cloud environments, which bases its implementation on the brain’s pattern recognition systematic functioning. This process allows classifying web services through recognition modules created dynamically based on their quality parameters, resulting in a set of web services suitable for a web service composition. This approach allows a solution to the selection problem using less complex tasks. This paper introduces an architectural and procedural definition that provides the web service description with a pattern to recognize and select services using different recognition levels. We simulated our approach and evaluated it using a dataset from the QWS project that offers a set of quality criteria collected from different providers. The web services are recognized and classified using different quality criteria for the composition and each of their services. The results demonstrate the effectiveness of the discovery and selection process compared to other approaches. Furthermore, Ar_WSDS allows us to recognize and filter out web services with ambiguity and similarity in their provider information, a process that minimizes the discovery space for services.24 Páginasapplication/pdfengApplied Sciences Volume 11, Issue 17, No. 8092 (2021)Vol. 17 (11) N0.8092 (2021)2411, 8092 (2021)1Vol.17Adarme, M., & Jimeno, M. (2021). QoS-Based Pattern Recognition Approach for Web Service Discovery: Ar_WSDS. Applied Sciences, 11(17), 8092. MDPI AG. Retrieved from http://dx.doi.org/10.3390/app11178092Applied Sciences© 2021 by the authors. Licensee MDPI, Basel, Switzerland.https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessAtribución 4.0 Internacional (CC BY 4.0)http://purl.org/coar/access_right/c_abf2https://www.mdpi.com/2076-3417/11/17/8092Qos-based pattern recognition approach for web service discovery: Ar_wsds(Article)Artículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85Suizaweb service compositioncloud computingpattern recognitionHayyolalam, V.; Kazem, A.A.P. A systematic literature review on QoS-aware service composition and selection in cloud environment. J. Netw. Comput. Appl. 2018, 110, 52–74.Dahan, F.; El Hindi, K.; Ghoneim, A.; Alsalman, H. An Enhanced Ant Colony Optimization Based Algorithm to Solve QoS-Aware Web Service Composition. IEEE Access 2021, 9, 34098–34111.Wang, H.; Wang, X.; Hu, X.; Zhang, X.; Gu, M. A multi-agent reinforcement learning approach to dynamic service composition. Inf. Sci. 2016, 363, 96–119.Qi, J.; Xu, B.; Xue, Y.; Wang, K.; Sun, Y. Knowledge based differential evolution for cloud computing service composition. J. Ambient Intell. Humaniz. Comput. 2018, 9, 565–574.Nacer, H.; Beghdad Bey, K.; Djebari, N. Migration from web services to cloud services. Lect. Notes Comput. Sci. 2017, 10542, 179–192._16. [Ramirez, A.; Parejo, J.A.; Romero, J.R.; Segura, S.; Ruiz-Cortés, A. Evolutionary composition of QoS-aware web services: A many-objective perspective. Expert Syst. Appl. 2017, 72, 357–370.Mishra, T.; Raj, G. QoS implementation in Web Services selection and ranking using data analysis. In Proceedings of the 2017 7th International Conference on Cloud Computing, Data Science Engineering Confluence, Noida, India, 12–13 June 2017; pp. 537–542.Puerto Cuadros, E.G.; Aguilar Castro, J.L. Un algoritmo recursivo de reconocimiento de patrones. Rev. Técnica Fac. Ing. Univ. Zulia 2017, 40, 95–104.Ghobaei-Arani, M.; Rahmanian, A.A.; Aslanpour, M.S.; Dashti, S.E. CSA-WSC: Cuckoo search algorithm for web service composition in cloud environments. Soft Comput. 2018, 22, 8353–8378.Di Martino, B.; Cretella, G.; Esposito, A. Cloud services composition through cloud patterns: A semantic-based approach. Soft Comput. 2017, 21, 4557–4570.Rangarajan, S. Qos-Based Web Service Discovery And Selection Using Machine Learning. EAI Endorsed Trans. Scalable Inf. Syst. 2018, 5.Chakravarthy, D.G.; Kannimuthu, S. Extreme Gradient Boost Classification Based Interesting User Patterns Discovery for Web Service Composition. Mob. Netw. Appl. 2019, 24, 1883–1895.Sha, J.; Du, Y.; Qi, L. A user requirement oriented web service discovery approach based on logic and threshold petri net. IEEE/CAA J. Autom. Sin. 2019, 6, 1528–1542.Wu, Y.; Yan, C.; Ding, Z.; Liu, G.; Wang, P.; Jiang, C.; Zhou, M.C. A Multilevel Index Model to Expedite Web Service Discovery and Composition in Large-Scale Service Repositories. IEEE Trans. Serv. Comput. 2016, 9, 330–342.Hasnain, M.; Pasha, M.F.; Ghani, I.; Mehboob, B.; Imran, M.; Ali, A. Benchmark dataset selection of Web services technologies: A factor analysis. IEEE Access 2020, 8, 53649–53665Rathore, M.; Suman, U. Evaluating QoS parameters for ranking Web service. In Proceedings of the 2013 3rd IEEE International Advance Computing Conference (IACC), Ghaziabad, India, 22–23 February 2013; pp. 1437–1442.Chandrashekar, G.; Sahin, F. A survey on feature selection methods. Comput. Electr. Eng. 2014, 40, 16–28.Khalid, S.; Khalil, T.; Nasreen, S. A Survey Of Feature Selection And Feature Extraction Techniques In Machine Learning. In Proceedings of the 2014 Science and Information Conference, London, UK, 27–29 August 2014; pp. 372–378.Oliveri, P.; Malegori, C.; Mustorgi, E.; Casale, M. 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Deep learning architecture for the recursive patterns recognition model. J. Phys. 2018, 1126, 12035.Puerto, E.; Aguilar, J.; Perez, B. Análisis de la teoría de la mente humana basada en el reconocimiento de patrones. In Proceedings of the 2014 Congreso Internacional en Innovación y Apropiación de las Tecnologías de la Información y las Comunicaciones(CIINATIC), Bucaramanga, Colombia, 28–30 October 2014; pp. 101–106.Viriyasitavat, W.; Bi, Z. Service selection and workflow composition in modern business processes. J. Ind. Inf. Integr. 2020, 17, 100126. [Wang, S.; Zheng, Z.; Sun, Q.; Zou, H.; Yang, F. Cloud model for service selection. In Proceedings of the Computer Communications Workshops (INFOCOM WKSHPS), Shanghai, China, 10–15 April 2011; pp. 666–671.Al-Masri, E.; Mahmoud, Q.H. Investigating web services on the world wide web. In Proceedings of the 17th International Conference on World Wide Web, Beijing, China, 21–25 April 2008; pp. 795–804.Al-Masri, E. A Quality-Driven Approach for Ranking Web Services. In New Trends in Networking, Computing, E-learning, Systems Sciences, and Engineering; Khaled, E., Sobh, T., Eds.; Springer International Publishing: Cham, Swizerlands, 2015; pp. 599–606.Devi, M.M. Survey on Choreography for Web Services. Int. J. Future Revolut. Comput. Sci. Commun. Eng. 2018, 4, 149–155.Baryannis, G.; Kritikos, K.; Plexousakis, D. A specification-based QoS-aware design framework for service-based applications. Service Oriented Comput. 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 incorporada en las Obras Colectivas.

b.	Distribuir copias o fonogramas de las Obras, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública, incluyéndolas como incorporadas en Obras Colectivas, según corresponda.

c.	Distribuir copias de las Obras Derivadas que se generen, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública.
Los derechos mencionados anteriormente pueden ser ejercidos en todos los medios y formatos, actualmente conocidos o que se inventen en el futuro. Los derechos antes mencionados incluyen el derecho a realizar dichas modificaciones en la medida que sean técnicamente necesarias para ejercer los derechos en otro medio o formatos, pero de otra manera usted no está autorizado para realizar obras derivadas. Todos los derechos no otorgados expresamente por el Licenciante quedan por este medio reservados, incluyendo pero sin limitarse a aquellos que se mencionan en las secciones 4(d) y 4(e).

4. Restricciones.
La licencia otorgada en la anterior Sección 3 está expresamente sujeta y limitada por las siguientes restricciones:

a.	Usted puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra sólo bajo las condiciones de esta Licencia, y Usted debe incluir una copia de esta licencia o del Identificador Universal de Recursos de la misma con cada copia de la Obra que distribuya, exhiba públicamente, ejecute públicamente o ponga a disposición pública. No es posible ofrecer o imponer ninguna condición sobre la Obra que altere o limite las condiciones de esta Licencia o el ejercicio de los derechos de los destinatarios otorgados en este documento. No es posible sublicenciar la Obra. Usted debe mantener intactos todos los avisos que hagan referencia a esta Licencia y a la cláusula de limitación de garantías. Usted no puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra con alguna medida tecnológica que controle el acceso o la utilización de ella de una forma que sea inconsistente con las condiciones de esta Licencia. Lo anterior se aplica a la Obra incorporada a una Obra Colectiva, pero esto no exige que la Obra Colectiva aparte de la obra misma quede sujeta a las condiciones de esta Licencia. Si Usted crea una Obra Colectiva, previo aviso de cualquier Licenciante debe, en la medida de lo posible, eliminar de la Obra Colectiva cualquier referencia a dicho Licenciante o al Autor Original, según lo solicitado por el Licenciante y conforme lo exige la cláusula 4(c).

b.	Usted no puede ejercer ninguno de los derechos que le han sido otorgados en la Sección 3 precedente de modo que estén principalmente destinados o directamente dirigidos a conseguir un provecho comercial o una compensación monetaria privada. El intercambio de la Obra por otras obras protegidas por derechos de autor, ya sea a través de un sistema para compartir archivos digitales (digital file-sharing) o de cualquier otra manera no será considerado como estar destinado principalmente o dirigido directamente a conseguir un provecho comercial o una compensación monetaria privada, siempre que no se realice un pago mediante una compensación monetaria en relación con el intercambio de obras protegidas por el derecho de autor.

c.	Si usted distribuye, exhibe públicamente, ejecuta públicamente o ejecuta públicamente en forma digital la Obra o cualquier Obra Derivada u Obra Colectiva, Usted debe mantener intacta toda la información de derecho de autor de la Obra y proporcionar, de forma razonable según el medio o manera que Usted esté utilizando: (i) el nombre del Autor Original si está provisto (o seudónimo, si fuere aplicable), y/o (ii) el nombre de la parte o las partes que el Autor Original y/o el Licenciante hubieren designado para la atribución (v.g., un instituto patrocinador, editorial, publicación) en la información de los derechos de autor del Licenciante, términos de servicios o de otras formas razonables; el título de la Obra si está provisto; en la medida de lo razonablemente factible y, si está provisto, el Identificador Uniforme de Recursos (Uniform Resource Identifier) que el Licenciante especifica para ser asociado con la Obra, salvo que tal URI no se refiera a la nota sobre los derechos de autor o a la información sobre el licenciamiento de la Obra; y en el caso de una Obra Derivada, atribuir el crédito identificando el uso de la Obra en la Obra Derivada (v.g., "Traducción Francesa de la Obra del Autor Original," o "Guión Cinematográfico basado en la Obra original del Autor Original"). Tal crédito puede ser implementado de cualquier forma razonable; en el caso, sin embargo, de Obras Derivadas u Obras Colectivas, tal crédito aparecerá, como mínimo, donde aparece el crédito de cualquier otro autor comparable y de una manera, al menos, tan destacada como el crédito de otro autor comparable.

d.	Para evitar toda confusión, el Licenciante aclara que, cuando la obra es una composición musical:

i.	Regalías por interpretación y ejecución bajo licencias generales. El Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública o la ejecución pública digital de la obra y de recolectar, sea individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, SAYCO), las regalías por la ejecución pública o por la ejecución pública digital de la obra (por ejemplo Webcast) licenciada bajo licencias generales, si la interpretación o ejecución de la obra está primordialmente orientada por o dirigida a la obtención de una ventaja comercial o una compensación monetaria privada.

ii.	Regalías por Fonogramas. El Licenciante se reserva el derecho exclusivo de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, los consagrados por la SAYCO), una agencia de derechos musicales o algún agente designado, las regalías por cualquier fonograma que Usted cree a partir de la obra (“versión cover”) y distribuya, en los términos del régimen de derechos de autor, si la creación o distribución de esa versión cover está primordialmente destinada o dirigida a obtener una ventaja comercial o una compensación monetaria privada.

e.	Gestión de Derechos de Autor sobre Interpretaciones y Ejecuciones Digitales (WebCasting). Para evitar toda confusión, el Licenciante aclara que, cuando la obra sea un fonograma, el Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública digital de la obra (por ejemplo, webcast) y de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, ACINPRO), las regalías por la ejecución pública digital de la obra (por ejemplo, webcast), sujeta a las disposiciones aplicables del régimen de Derecho de Autor, si esta ejecución pública digital está primordialmente dirigida a obtener una ventaja comercial o una compensación monetaria privada.

5. Representaciones, Garantías y Limitaciones de Responsabilidad.
A MENOS QUE LAS PARTES LO ACORDARAN DE OTRA FORMA POR ESCRITO, EL LICENCIANTE OFRECE LA OBRA (EN EL ESTADO EN EL QUE SE ENCUENTRA) “TAL CUAL”, SIN BRINDAR GARANTÍAS DE CLASE ALGUNA RESPECTO DE LA OBRA, YA SEA EXPRESA, IMPLÍCITA, LEGAL O CUALQUIERA OTRA, INCLUYENDO, SIN LIMITARSE A ELLAS, GARANTÍAS DE TITULARIDAD, COMERCIABILIDAD, ADAPTABILIDAD O ADECUACIÓN A PROPÓSITO DETERMINADO, AUSENCIA DE INFRACCIÓN, DE AUSENCIA DE DEFECTOS LATENTES O DE OTRO TIPO, O LA PRESENCIA O AUSENCIA DE ERRORES, SEAN O NO DESCUBRIBLES (PUEDAN O NO SER ESTOS DESCUBIERTOS). ALGUNAS JURISDICCIONES NO PERMITEN LA EXCLUSIÓN DE GARANTÍAS IMPLÍCITAS, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.

6. Limitación de responsabilidad.
A MENOS QUE LO EXIJA EXPRESAMENTE LA LEY APLICABLE, EL LICENCIANTE NO SERÁ RESPONSABLE ANTE USTED POR DAÑO ALGUNO, SEA POR RESPONSABILIDAD EXTRACONTRACTUAL, PRECONTRACTUAL O CONTRACTUAL, OBJETIVA O SUBJETIVA, SE TRATE DE DAÑOS MORALES O PATRIMONIALES, DIRECTOS O INDIRECTOS, PREVISTOS O IMPREVISTOS PRODUCIDOS POR EL USO DE ESTA LICENCIA O DE LA OBRA, AUN CUANDO EL LICENCIANTE HAYA SIDO ADVERTIDO DE LA POSIBILIDAD DE DICHOS DAÑOS. ALGUNAS LEYES NO PERMITEN LA EXCLUSIÓN DE CIERTA RESPONSABILIDAD, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.

7. Término.

a.	Esta Licencia y los derechos otorgados en virtud de ella terminarán automáticamente si Usted infringe alguna condición establecida en ella. Sin embargo, los individuos o entidades que han recibido Obras Derivadas o Colectivas de Usted de conformidad con esta Licencia, no verán terminadas sus licencias, siempre que estos individuos o entidades sigan cumpliendo íntegramente las condiciones de estas licencias. Las Secciones 1, 2, 5, 6, 7, y 8 subsistirán a cualquier terminación de esta Licencia.

b.	Sujeta a las condiciones y términos anteriores, la licencia otorgada aquí es perpetua (durante el período de vigencia de los derechos de autor de la obra). No obstante lo anterior, el Licenciante se reserva el derecho a publicar y/o estrenar la Obra bajo condiciones de licencia diferentes o a dejar de distribuirla en los términos de esta Licencia en cualquier momento; en el entendido, sin embargo, que esa elección no servirá para revocar esta licencia o que deba ser otorgada , bajo los términos de esta licencia), y esta licencia continuará en pleno vigor y efecto a menos que sea terminada como se expresa atrás. La Licencia revocada continuará siendo plenamente vigente y efectiva si no se le da término en las condiciones indicadas anteriormente.

8. Varios.

a.	Cada vez que Usted distribuya o ponga a disposición pública la Obra o una Obra Colectiva, el Licenciante ofrecerá al destinatario una licencia en los mismos términos y condiciones que la licencia otorgada a Usted bajo esta Licencia.

b.	Si alguna disposición de esta Licencia resulta invalidada o no exigible, según la legislación vigente, esto no afectará ni la validez ni la aplicabilidad del resto de condiciones de esta Licencia y, sin acción adicional por parte de los sujetos de este acuerdo, aquélla se entenderá reformada lo mínimo necesario para hacer que dicha disposición sea válida y exigible.

c.	Ningún término o disposición de esta Licencia se estimará renunciada y ninguna violación de ella será consentida a menos que esa renuncia o consentimiento sea otorgado por escrito y firmado por la parte que renuncie o consienta.

d.	Esta Licencia refleja el acuerdo pleno entre las partes respecto a la Obra aquí licenciada. No hay arreglos, acuerdos o declaraciones respecto a la Obra que no estén especificados en este documento. El Licenciante no se verá limitado por ninguna disposición adicional que pueda surgir en alguna comunicación emanada de Usted. Esta Licencia no puede ser modificada sin el consentimiento mutuo por escrito del Licenciante y Usted.
0000-0002-2121-1208364827d63168d535a5b7f6315210f852600 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