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19
H.L.: Virtualizing software and humans for elastic processes in multiple clouds – a service management perspective
- International Journal of Next-Generation Computing
, 2012
"... There is a growing trend of combining human-based computation with machine-based computation to solve complex problems which cannot be answered with machine-based computation alone. From the computing perspective, integrating machine-based computing elements with human-based computing elements and p ..."
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Cited by 10 (8 self)
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There is a growing trend of combining human-based computation with machine-based computation to solve complex problems which cannot be answered with machine-based computation alone. From the computing perspective, integrating machine-based computing elements with human-based computing elements and provisioning them under the same model will facilitate the resource elasticity required by complex applications. Although certain works investigate techniques for integrating human-based computing elements with machine-based computing elements, existing computing models for such integrated computing systems are very limited. In fact, the architectures, interconnections, non-functional properties of human-based computing elements are very different from that of contemporary machine-based counterparts. Human-based computing elements are built based on social and bio concepts, thus their architectures, interconnects and non-functional properties are extremely complex and dynamic, compared with that of machine-based computing elements. In this paper, we examine fundamental issues in virtualizing human-based computing elements and machine-based computing elements using service-oriented computing concepts in order to create highly scalable computing systems of hybrid services to support the elasticity of software and people in complex applications. We will outline our Vienna Elastic Computing Model which aims at introducing techniques and frameworks to support multi-dimensional elastic processes atop multiple cloud systems of software-based and human-based services. This paper will analyze several service management issues to support the virtualization of machine-based and human-based computing elements to support such elastic processes.
On Analyzing and Developing Data Contracts in Cloud-based Data Marketplaces
"... Abstract—Currently, rich and diverse data types have been increasingly provided using the Data-as-a-Service (DaaS) model, a form of cloud computing services. However, data offered by DaaS are constrained by several data concerns that, if not automatically being reasoned properly, will lead to a wron ..."
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Cited by 8 (2 self)
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Abstract—Currently, rich and diverse data types have been increasingly provided using the Data-as-a-Service (DaaS) model, a form of cloud computing services. However, data offered by DaaS are constrained by several data concerns that, if not automatically being reasoned properly, will lead to a wrong way of using them. In this paper, we support the assumption that data concerns should be explicitly modeled and specified in data contracts to support concern-aware data selection and utilization. Instead of relying on a specific definition of data contracts, we model for data contracts. Based on the abstract model, we propose several techniques for evaluating data contracts that can be integrated into data service selection and composition frameworks. We also illustrate our approach with some realworld scenarios. I.
Quality-aware Service-Oriented Data Integration: Requirements, State of the Art and Open Challenges
"... With a multitude of data sources available online, data consumers might find it hard to select the best combination of sources for their needs. Aspects such as price, licensing, service and data quality play a major role in selecting data sources. We therefore advocate qualityaware data services as ..."
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Cited by 7 (0 self)
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With a multitude of data sources available online, data consumers might find it hard to select the best combination of sources for their needs. Aspects such as price, licensing, service and data quality play a major role in selecting data sources. We therefore advocate qualityaware data services as a natural data source model for complex data integration tasks and mash-ups. This paper focuses on requirements, state of the art, and the main research challenges on the way to the realization of such services. 1.
WS-Aggregation: Distributed Aggregation of Web Services Data
"... Recent trends of Web-based data processing (e.g., service mashups, Data-as-a-Service) call for techniques to collect and process heterogeneous data from distributed sources in a uniform way. In this paper we present WS-Aggregation, a general purpose framework for aggregation of data exposed as Web s ..."
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Cited by 6 (5 self)
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Recent trends of Web-based data processing (e.g., service mashups, Data-as-a-Service) call for techniques to collect and process heterogeneous data from distributed sources in a uniform way. In this paper we present WS-Aggregation, a general purpose framework for aggregation of data exposed as Web services. WS-Aggregation provides clients with a single-site interface to execute multi-site queries. The framework autonomously collects and processes the requested data using a set of cooperative aggregator nodes. The query distribution is configurable using strategies, e.g., QoS-based or location-based. We introduce WAQL as a specialized query language for Web service data aggregation that is based on XQuery. 3-way querying is a possibility to optimize requests by reducing the amount of data transferred between aggregator nodes. A Web-based graphical user interface facilitates composing aggregation requests. Our performance evaluation, which comprises aggregation scenarios with different settings, shows the good scalability of WS-Aggregation.
Privacy model and annotation for daas
- In Proc
, 2010
"... Abstract—Data as a Service (DaaS) builds on serviceoriented technologies to enable fast access to data resources on the Web. However, this paradigm raises several new concerns that traditional privacy models for Web services do not handle. First, the distinction between the roles of service provider ..."
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Abstract—Data as a Service (DaaS) builds on serviceoriented technologies to enable fast access to data resources on the Web. However, this paradigm raises several new concerns that traditional privacy models for Web services do not handle. First, the distinction between the roles of service providers and data providers is unclear, leaving the latter helpless for specifying and verifying the enforcement of their data privacy requirements. Second, traditional models for privacy policies focus only on the service interface without taking into account privacy policies related to data resources. Third, unstructured data resources, as well as user permissions and obligations related to data that are utilized in DaaS are not taken into account. In this paper, we study data privacy as one of these concerns, which relates to the management of private information. The main contribution of this paper consists in: 1) devising a model for making explicit privacy constraints of DaaS, and 2) on the basis of the proposed privacy model, developing techniques that allow handling the privacy concern when querying DaaS. We validate the applicability of our proposal with some experiments. I.
Privacy-aware daas services composition
- in Database and Expert Systems Applications
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DEMODS: a description model for data-as-a-service
- in The 26th IEEE International Conference on Advanced Information Networking and Applications (AINA-2012), IEEE Computer
, 2012
"... has become popular. Several data assets have been released in DaaSes across different cloud platforms. Nevertheless, there are no well-defined ways to describe DaaSes and their associated data assets. On the one hand, existing DaaS providers simply use HTML documents to describe their service. This ..."
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has become popular. Several data assets have been released in DaaSes across different cloud platforms. Nevertheless, there are no well-defined ways to describe DaaSes and their associated data assets. On the one hand, existing DaaS providers simply use HTML documents to describe their service. This simple way of service description requires user to manually perform service lookup by reading the HTML documents to understand DaaSes as well as their provided data assets. On the other hand, existing service description techniques are not suitable for describing DaaSes because they consider only service information. The lack of well-structured/linked model to describe DaaSes hinders the automatic service lookup for DaaSes and the integration of DaaSes into data composition and analytic tools. In this paper, we propose DEMODS, a DEscription MOdel for DaaS, which introduces a general linked model to cover all basic information of a DaaS. Besides the basic DaaS description model, we also introduce an extended model that integrates existing work in describing quality of data, data and service contract, data dependency, and Quality of Service (QoS). We present a mechanism to incorporate DEMODS into both new and existing DaaSes. Finally, a prototype of DEMODS has been developed to evaluate the effectiveness of the proposed model. Keywords- Cloud computing, Data-as-a-Service (DaaS), data marketplace, service and data description, discovery.
Context, Quality and Relevance: Dependencies and Impacts on RESTful Web Services Design ⋆
"... Abstract. While several techniques have been introduced for specifying and acquiring context and quality information associated with Web services, they consider such information representing the whole Web services. However, accessing to context and quality of data resources provided by Web services ..."
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Abstract. While several techniques have been introduced for specifying and acquiring context and quality information associated with Web services, they consider such information representing the whole Web services. However, accessing to context and quality of data resources provided by Web services is crucial. This is particularly relevant for data-intensive Web services of which the context and quality of data resources will strongly impact on the service development and composition. In this paper we contribute an analysis of relationships among context, quality, and relevance, as well as their impact on the design and composition of Web services, in particular at the data resource level. Then, we propose several techniques to incorporate context and quality descriptions into REST APIs and RESTful services publishing. By implementing these features, RESTful Web services could allow the consumer to specify and query context and quality information associated with services and data resources, thus fostering the provision of high relevant data resources. 1
Furthering the Growth of Cloud Computing by Providing Privacy as a Service
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All in-text references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately.
