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Data-centric and service-oriented workflows are commonly used in scientific research to enable the composition and execution of complex analysis on distributed resources. Although there are a plethora of orchestration frameworks to implement workflows, most of them are not suitable to execute data-centric workflows. The main issue is transferring output of service invocations through a centralized orchestration engine to the next service in the workflow, which can be a bottleneck for the performance of a data-centric workflow. In this paper, we propose a flexible and lightweight workflow framework based on the Object Modeling Systems (OMS). Moreover, we take advantage of the OMS architecture to deploy and execute data-centric workflows in a decentralized manner to avoid passing through the centralized engine. The proposed framework is implemented in context of the Australian Urban Research Infrastructure Network (AURIN) project which is an initiative aiming to develop an e-Infrastructure supporting research in the urban and built environment research disciplines. Performance evaluation results using spatial data-centric workflows show that we can reduce 20% of the workflows execution time while using Cloud resources in the same network domain.