During the past fifteen years, data exchange has been explored in depth and in a variety of different settings. Even though temporal databases constitute a mature area of research studied over several decades, the investigation of temporal data exchange was initiated only very recently. We analyze the properties of universal solutions in temporal data exchange with emphasis on the relationship between universal solutions in the context of concrete time and universal solutions in the context of abstract time. We show that challenges arise even in the setting in which the data exchange specifications involve a single temporal variable. After this, we identify settings, including data exchange settings that involve multiple temporal variables, in which these challenges can be overcome.

This article proposes an approach for identifying and recommending scientific workflows for reuse and repurposing. Specifically, a scientific workflow is represented as a layer hierarchy, which specifies hierarchical relations between this workflow, its sub-workflows, and activities. Semantic similarity is calculated between layer hierarchies of workflows. A graph-skeleton based clustering technique is adopted for grouping layer hierarchies into clusters. Barycenters in each cluster are identified, which refer to core workflows in this cluster, for facilitating cluster identification and workflow ranking and recommendation. Experimental evaluation shows that our technique is efficient and accurate on ranking and recommending appropriate clusters and scientific workflows with respect to specific requirements of scientific experiments.

This article proposes to identify and recommend scientific workflows to promote their reuse and repurposing. Specifically, a scientific workflow is converted into a layer hierarchy, which specifies hierarchical relations between this workflow, its sub-workflows, and activities. Semantic similarity is calculated between layer hierarchies of workflows in order to construct a scientific workflow network model. A graph-skeleton based clustering method is adopted for grouping layer hierarchies into clusters. Barycenters in clusters are identified for facilitating cluster identification and workflow ranking and recommendation. Experimental result shows that this technique is efficient and accurate on ranking and recommending appropriate clusters and scientific workflows.

Recently, workflow fragments gains increasing momentum for reuse and re-purpose in Cyber-Physical Systems. To the end, this article proposes to detect and recommend workflow fragments gratifying e-Scientist requirement. Specifically, most common workflow fragments in the form of layer hierarchy are extracted from scientific workflows, which are collected in the myExperiment repository. Annotations for those workflow fragments are developed to support the discovery of workflow fragments. Consequently, an approach for workflow fragments rank and recommendation is presented considering the semantics and structure of workflow fragments.