The proposed techniques are used

The proposed techniques are used for generating dependable rules from these datasets, and they GSK126 also are employed for fixing errors found automatically to yield data into a consistent state. The cleaned data become on demand data access for decision making and management purposes, enabling accurate decisions based on their exact quality of data. The proposed techniques are evaluated using several measures including:
Conclusions and future works
Introduction The design of modern cloud computing technologies does not consider interoperability [1]. To facilitate cloud-computing interoperability, a previous work [2] proposed that every cloud system be described by its components, including resources, services, and Application Program Interfaces (APIs). Moreover, widely accepted standardized APIs should be used to regulate communication and resource and services management. However, such a model is not realistic and would be difficult to achieve because companies consider differentiation a competitive advantage. Consequently, another approach using a cloud broker was introduced [2]. A cloud broker is used as a mediation technique to enable interaction between different cloud systems that were not equipped with the ability to interact with each other. This technique may reduce the need for modifying current cloud systems. Finding solutions to the issues related to interoperability could advance cloud computing because this will increase customer trust in the cloud and solve vendor lock-in problems. According to “Cloud Computing Use Case Discussion Group, Cloud Computing Use Cases V.4,” there are two aspects of cloud computing interoperability, namely, technical (syntactic) and semantic. Technical interoperability is the ability of a code to simultaneously work with multiple cloud vendors. It includes aspects such as interface specifications, services for interaction, and integration, presentation, and exchange. “European Interoperability Framework (EIF) for European Public Services” describes semantic interoperability as the ability of an organization to understand information coming from external sources with the same meaning as the originator intended. In this paper, we introduce a Cloud Interoperability Broker (CIB) at the Software-as-a-Service (SaaS) level in an attempt to bridge the semantic interoperability gap between different SaaS providers that serve the same business need but still lack interoperability prospects as a result of not following the same data model. Although the idea of filling the interoperability gap is not new, most efforts have addressed its challenges in the Infrastructure-as-a-Service (IaaS) layer. For example, the RESERVOIR model is aimed at creating a service model for providing and managing resources and services transparently on an on-demand basis. The RESERVOIR model aims to enable IaaS vendors to dynamically interoperate to create what could be considered as a pool of infinite IT resources [1]. In Ref. [3] the authors use the concept of brokering to optimize virtual infrastructure deployment via multiple cloud systems, regardless of the deployment and management of physical infrastructures. The novel idea introduced in this paper is to apply the brokering concept in the SaaS layer. The remainder of the paper is organized as follows. Section 2 provides a survey of existing cloud-computing interoperability efforts. Section 3 introduces the proposed cloud broker methodology. Section 4 presents the cloud broker\'s implementation. Section 5 summarizes the paper and discusses future work.
Related work
SaaS CIB methodology This study aims to create a CIB in the SaaS layer that would act as a mediator for filling in the interoperability gap between any SaaS providers that serves the same business need or domain. Many research efforts have addressed the idea of brokerage but the implementation and testing of a mediation mechanism in the SaaS layer is a novel idea. In addition, this research focuses on developing a generic cloud interoperability brokerage methodology that can be implemented for the different data migration and movement processes in the SaaS layer. Fig. 1 shows the location of the CIB in the SaaS layer, and more generally, in relation to the cloud computing deployment models IaaS, SaaS, and Platform as a Service (PaaS).