Data-Centered Service Composition for Information Analysis

In e-Science, many scientific workflow management systems have been developed to integrate distributed computation resources, data sets, and mining algorithms. Users usually modify and rerun a workflow while repeating procedures: preprocess of data, selection of features, modification of data, selection of mining algorithms, generation of models, and evaluation of the models. These procedures are continued until the domain knowledge is acquired. However, as the size of the data increases, the execution time of the workflow becomes longer and longer, which drives up the cost of rerunning the modified workflow. As a result, it becomes hard to quickly obtain the analysis result. In this research, we avoided the rerun of the workflow by storing service invocation results on a platform and realized data-centered service composition by adding and deleting rules to be fired. To validate the effectiveness of our proposed platform, we created two rule-based services to analyze real-time data: stream message data and sensing data.