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�ݺ�ߣshows by User: wangmingxue3 / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: wangmingxue3 / Wed, 13 Jun 2018 11:31:44 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: wangmingxue3 2012 CloudCom, RPig: A Scalable Framework for Machine Learning �and Advanced Statistical Functionalities /slideshow/rpig-a-scalable-framework-for-machine-learning-and-advanced-statistical-functionalities/102291288 cloudcom2012embv2final-180613113144
In many domains, such as Telecom, various scenarios necessitate the processing of large amounts of data using statistical and machine learning algorithms. A noticeable effort has been made to move the data management systems into MapReduce parallel processing environments, such as Hadoop, and Pig. Nevertheless, these systems lack the features of advanced machine learning and statistical analysis. Frameworks such as Mahout, on top of Hadoop, support machine learning, but their implementations are at the preliminary stage. For example, Mahout does not provide Support Vector Machine (SVM) algorithms and it is difficult to use. On the other hand, traditional statistical software tools, such as R, containing comprehensive statistical algorithms for advanced analysis, are widely used. But such software can only run on a single computer, and therefore it is not scalable. In this paper, we propose an integrated solution RPig, which takes the advantages of R (for machine learning and statistical analysis capabilities) and parallel data processing capabilities of Pig. The RPig framework offers a scalable, advanced data analysis solution for machine learning and statistical analysis. Analysis jobs can be easily developed with RPig script in high level languages. We describe the design, implementation and an eclipse-based RPigEditor for the RPig framework. Using application scenarios from the Telecom domain we show the usage of RPig and how the framework can significantly reduce the development effort. The results demonstrate the scalability of our framework and the simplicity of deployment for analysis jobs.]]>
In many domains, such as Telecom, various scenarios necessitate the processing of large amounts of data using statistical and machine learning algorithms. A noticeable effort has been made to move the data management systems into MapReduce parallel processing environments, such as Hadoop, and Pig. Nevertheless, these systems lack the features of advanced machine learning and statistical analysis. Frameworks such as Mahout, on top of Hadoop, support machine learning, but their implementations are at the preliminary stage. For example, Mahout does not provide Support Vector Machine (SVM) algorithms and it is difficult to use. On the other hand, traditional statistical software tools, such as R, containing comprehensive statistical algorithms for advanced analysis, are widely used. But such software can only run on a single computer, and therefore it is not scalable. In this paper, we propose an integrated solution RPig, which takes the advantages of R (for machine learning and statistical analysis capabilities) and parallel data processing capabilities of Pig. The RPig framework offers a scalable, advanced data analysis solution for machine learning and statistical analysis. Analysis jobs can be easily developed with RPig script in high level languages. We describe the design, implementation and an eclipse-based RPigEditor for the RPig framework. Using application scenarios from the Telecom domain we show the usage of RPig and how the framework can significantly reduce the development effort. The results demonstrate the scalability of our framework and the simplicity of deployment for analysis jobs.]]> Wed, 13 Jun 2018 11:31:44 GMT /slideshow/rpig-a-scalable-framework-for-machine-learning-and-advanced-statistical-functionalities/102291288 wangmingxue3@slideshare.net(wangmingxue3) 2012 CloudCom, RPig: A Scalable Framework for Machine Learning �and Advanced Statistical Functionalities wangmingxue3 In many domains, such as Telecom, various scenarios necessitate the processing of large amounts of data using statistical and machine learning algorithms. A noticeable effort has been made to move the data management systems into MapReduce parallel processing environments, such as Hadoop, and Pig. Nevertheless, these systems lack the features of advanced machine learning and statistical analysis. Frameworks such as Mahout, on top of Hadoop, support machine learning, but their implementations are at the preliminary stage. For example, Mahout does not provide Support Vector Machine (SVM) algorithms and it is difficult to use. On the other hand, traditional statistical software tools, such as R, containing comprehensive statistical algorithms for advanced analysis, are widely used. But such software can only run on a single computer, and therefore it is not scalable. In this paper, we propose an integrated solution RPig, which takes the advantages of R (for machine learning and statistical analysis capabilities) and parallel data processing capabilities of Pig. The RPig framework offers a scalable, advanced data analysis solution for machine learning and statistical analysis. Analysis jobs can be easily developed with RPig script in high level languages. We describe the design, implementation and an eclipse-based RPigEditor for the RPig framework. Using application scenarios from the Telecom domain we show the usage of RPig and how the framework can significantly reduce the development effort. The results demonstrate the scalability of our framework and the simplicity of deployment for analysis jobs. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/cloudcom2012embv2final-180613113144-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> In many domains, such as Telecom, various scenarios necessitate the processing of large amounts of data using statistical and machine learning algorithms. A noticeable effort has been made to move the data management systems into MapReduce parallel processing environments, such as Hadoop, and Pig. Nevertheless, these systems lack the features of advanced machine learning and statistical analysis. Frameworks such as Mahout, on top of Hadoop, support machine learning, but their implementations are at the preliminary stage. For example, Mahout does not provide Support Vector Machine (SVM) algorithms and it is difficult to use. On the other hand, traditional statistical software tools, such as R, containing comprehensive statistical algorithms for advanced analysis, are widely used. But such software can only run on a single computer, and therefore it is not scalable. In this paper, we propose an integrated solution RPig, which takes the advantages of R (for machine learning and statistical analysis capabilities) and parallel data processing capabilities of Pig. The RPig framework offers a scalable, advanced data analysis solution for machine learning and statistical analysis. Analysis jobs can be easily developed with RPig script in high level languages. We describe the design, implementation and an eclipse-based RPigEditor for the RPig framework. Using application scenarios from the Telecom domain we show the usage of RPig and how the framework can significantly reduce the development effort. The results demonstrate the scalability of our framework and the simplicity of deployment for analysis jobs.

2012 CloudCom, RPig: A Scalable Framework for Machine Learning and Advanced Statistical Functionalities from MingXue Wang

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0 2011, A POLICY BASED GOVERNANCE FRAMEWORK FOR CLOUD SERVICE PROCESS ARCHITECTURES /slideshow/a-policy-based-governance-framework-for-cloud-service-process-architectures/75595546 vivaforexternalupload-170502102159
In today's environment, the day to day business operations of organisations heavily rely on the automated business processes from enterprise IT infrastructures. The dynamic business environment and the problems with the long time implementation, high cost, etc. of process development and maintenance, are pushing organisations as process consumers to look for ready to use and shared business processes from IT providers for on demand requirements. This is manifest in the rising of Cloud Computing and Business Process Outsourcing with the development of the new concept of (business) Process as a Service. Service-Oriented Architecture (SOA) is an architectural style commonly adopted for enterprise IT infrastructures and the implementation of service based business processes. However, the SOA style and current specifications do not intend for the case of business processes sharing with cross organisational consumers, since various requirements or the business policies of different organisations, are unmanageable to meet on a business process at the same time. In this thesis, we present an architectural solution to address the above issues for the Process as a Service. It consists of a Service Process Architecture (SPA) architectural style designed to extend the SOA style, and a supported architecture framework designed for the specific style. The proposed SPA style has a defined principle for the goal of process customizability and adaptability on process design and development with providers. The supported architecture framework consists of three main parts: a policy specification entails expressing business policies or the requirements of consumers regarding business processes in the cloud; a coordination framework aim to enforce expressed policies on process executions with adaptive business processes for different consumers; finally, an AOP enhanced extension is responsible for the extensibility of the framework to satisfy consumers' possible additional requirements. Our SPA style could extend the SOA style, and has an impact on the SOA principles. With the supported architecture framework, we provided a complete architectural solution for the Process as a Service.]]>
In today's environment, the day to day business operations of organisations heavily rely on the automated business processes from enterprise IT infrastructures. The dynamic business environment and the problems with the long time implementation, high cost, etc. of process development and maintenance, are pushing organisations as process consumers to look for ready to use and shared business processes from IT providers for on demand requirements. This is manifest in the rising of Cloud Computing and Business Process Outsourcing with the development of the new concept of (business) Process as a Service. Service-Oriented Architecture (SOA) is an architectural style commonly adopted for enterprise IT infrastructures and the implementation of service based business processes. However, the SOA style and current specifications do not intend for the case of business processes sharing with cross organisational consumers, since various requirements or the business policies of different organisations, are unmanageable to meet on a business process at the same time. In this thesis, we present an architectural solution to address the above issues for the Process as a Service. It consists of a Service Process Architecture (SPA) architectural style designed to extend the SOA style, and a supported architecture framework designed for the specific style. The proposed SPA style has a defined principle for the goal of process customizability and adaptability on process design and development with providers. The supported architecture framework consists of three main parts: a policy specification entails expressing business policies or the requirements of consumers regarding business processes in the cloud; a coordination framework aim to enforce expressed policies on process executions with adaptive business processes for different consumers; finally, an AOP enhanced extension is responsible for the extensibility of the framework to satisfy consumers' possible additional requirements. Our SPA style could extend the SOA style, and has an impact on the SOA principles. With the supported architecture framework, we provided a complete architectural solution for the Process as a Service.]]> Tue, 02 May 2017 10:21:59 GMT /slideshow/a-policy-based-governance-framework-for-cloud-service-process-architectures/75595546 wangmingxue3@slideshare.net(wangmingxue3) 2011, A POLICY BASED GOVERNANCE FRAMEWORK FOR CLOUD SERVICE PROCESS ARCHITECTURES wangmingxue3 In today's environment, the day to day business operations of organisations heavily rely on the automated business processes from enterprise IT infrastructures. The dynamic business environment and the problems with the long time implementation, high cost, etc. of process development and maintenance, are pushing organisations as process consumers to look for ready to use and shared business processes from IT providers for on demand requirements. This is manifest in the rising of Cloud Computing and Business Process Outsourcing with the development of the new concept of (business) Process as a Service. Service-Oriented Architecture (SOA) is an architectural style commonly adopted for enterprise IT infrastructures and the implementation of service based business processes. However, the SOA style and current specifications do not intend for the case of business processes sharing with cross organisational consumers, since various requirements or the business policies of different organisations, are unmanageable to meet on a business process at the same time. In this thesis, we present an architectural solution to address the above issues for the Process as a Service. It consists of a Service Process Architecture (SPA) architectural style designed to extend the SOA style, and a supported architecture framework designed for the specific style. The proposed SPA style has a defined principle for the goal of process customizability and adaptability on process design and development with providers. The supported architecture framework consists of three main parts: a policy specification entails expressing business policies or the requirements of consumers regarding business processes in the cloud; a coordination framework aim to enforce expressed policies on process executions with adaptive business processes for different consumers; finally, an AOP enhanced extension is responsible for the extensibility of the framework to satisfy consumers' possible additional requirements. Our SPA style could extend the SOA style, and has an impact on the SOA principles. With the supported architecture framework, we provided a complete architectural solution for the Process as a Service. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/vivaforexternalupload-170502102159-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> In today's environment, the day to day business operations of organisations heavily rely on the automated business processes from enterprise IT infrastructures. The dynamic business environment and the problems with the long time implementation, high cost, etc. of process development and maintenance, are pushing organisations as process consumers to look for ready to use and shared business processes from IT providers for on demand requirements. This is manifest in the rising of Cloud Computing and Business Process Outsourcing with the development of the new concept of (business) Process as a Service. Service-Oriented Architecture (SOA) is an architectural style commonly adopted for enterprise IT infrastructures and the implementation of service based business processes. However, the SOA style and current specifications do not intend for the case of business processes sharing with cross organisational consumers, since various requirements or the business policies of different organisations, are unmanageable to meet on a business process at the same time. In this thesis, we present an architectural solution to address the above issues for the Process as a Service. It consists of a Service Process Architecture (SPA) architectural style designed to extend the SOA style, and a supported architecture framework designed for the specific style. The proposed SPA style has a defined principle for the goal of process customizability and adaptability on process design and development with providers. The supported architecture framework consists of three main parts: a policy specification entails expressing business policies or the requirements of consumers regarding business processes in the cloud; a coordination framework aim to enforce expressed policies on process executions with adaptive business processes for different consumers; finally, an AOP enhanced extension is responsible for the extensibility of the framework to satisfy consumers' possible additional requirements. Our SPA style could extend the SOA style, and has an impact on the SOA principles. With the supported architecture framework, we provided a complete architectural solution for the Process as a Service.

2011, A POLICY BASED GOVERNANCE FRAMEWORK FOR CLOUD SERVICE PROCESS ARCHITECTURES from MingXue Wang

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0 SCC conference 2010, Process as a service -Distributed Multi-tenant Policy-based Process Runtime Governance /slideshow/process-as-a-service-distributed-multitenant-policybased-process-runtime-governance/75595231 scc2010-170502101317
With the emergence of Business Process Outsourcing and Cloud Computing, enterprises are looking for available business processes outside of their organizations to quickly adopt to new business requirements and also reduce process development and maintenance costs. The process execution needs to be governed as policy enforcement might differ between different clients. Since a process is deployed outside of the organizations and serves multiple process clients, distribution and multi-tenancy have become two requirements for runtime governance of service processes. We address this problem by introducing a policy-oriented aspectual business process framework. The runtime governancefrom process clients are integrated as aspects through dynamic weaving into process execution.]]>
With the emergence of Business Process Outsourcing and Cloud Computing, enterprises are looking for available business processes outside of their organizations to quickly adopt to new business requirements and also reduce process development and maintenance costs. The process execution needs to be governed as policy enforcement might differ between different clients. Since a process is deployed outside of the organizations and serves multiple process clients, distribution and multi-tenancy have become two requirements for runtime governance of service processes. We address this problem by introducing a policy-oriented aspectual business process framework. The runtime governancefrom process clients are integrated as aspects through dynamic weaving into process execution.]]> Tue, 02 May 2017 10:13:17 GMT /slideshow/process-as-a-service-distributed-multitenant-policybased-process-runtime-governance/75595231 wangmingxue3@slideshare.net(wangmingxue3) SCC conference 2010, Process as a service -Distributed Multi-tenant Policy-based Process Runtime Governance wangmingxue3 With the emergence of Business Process Outsourcing and Cloud Computing, enterprises are looking for available business processes outside of their organizations to quickly adopt to new business requirements and also reduce process development and maintenance costs. The process execution needs to be governed as policy enforcement might differ between different clients. Since a process is deployed outside of the organizations and serves multiple process clients, distribution and multi-tenancy have become two requirements for runtime governance of service processes. We address this problem by introducing a policy-oriented aspectual business process framework. The runtime governancefrom process clients are integrated as aspects through dynamic weaving into process execution. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/scc2010-170502101317-thumbnail.jpg?width=120&height=120&fit=bounds" /><br> With the emergence of Business Process Outsourcing and Cloud Computing, enterprises are looking for available business processes outside of their organizations to quickly adopt to new business requirements and also reduce process development and maintenance costs. The process execution needs to be governed as policy enforcement might differ between different clients. Since a process is deployed outside of the organizations and serves multiple process clients, distribution and multi-tenancy have become two requirements for runtime governance of service processes. We address this problem by introducing a policy-oriented aspectual business process framework. The runtime governancefrom process clients are integrated as aspects through dynamic weaving into process execution.

SCC conference 2010, Process as a service -Distributed Multi-tenant Policy-based Process Runtime Governance from MingXue Wang

]]> 69 2 https://cdn.slidesharecdn.com/ss_thumbnails/scc2010-170502101317-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post

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0 https://cdn.slidesharecdn.com/profile-photo-wangmingxue3-48x48.jpg?cb=1670341840 9+ years research and development in academia and industry 10 international patents and applications 30+ peer reviewed publications Design and development of POC and production grade systems Distributed systems and big data analytics (data mining and machine learning) Protocol and algorithm design Service-Oriented computation and Cloud computing Member of Technical Program Committee and Reviewer of IEEE and other international journals and conferences Co-chair and Track Chair of IEEE and other international conferences and workshops https://cdn.slidesharecdn.com/ss_thumbnails/cloudcom2012embv2final-180613113144-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/rpig-a-scalable-framework-for-machine-learning-and-advanced-statistical-functionalities/102291288 2012 CloudCom, RPig: ... https://cdn.slidesharecdn.com/ss_thumbnails/vivaforexternalupload-170502102159-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/a-policy-based-governance-framework-for-cloud-service-process-architectures/75595546 2011, A POLICY BASED G... https://cdn.slidesharecdn.com/ss_thumbnails/scc2010-170502101317-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/process-as-a-service-distributed-multitenant-policybased-process-runtime-governance/75595231 SCC conference 2010, P...