Cui Qin

Learn More
Big data applications with their high-volume and dynamically changing data streams impose new challenges to application performance management. Efficient and effective solutions must balance performance versus result precision and cope with dramatic changes in real-time load and needs without over-provisioning resources. Moreover, a developer should not be(More)
Stream processing is a popular paradigm to process huge amounts of data. During processing, the actual characteristics of the analyzed data streams may vary, e.g., in terms of volume or velocity. To provide a steady quality of the analysis results, runtime adaptation of the data processing is desirable. While several techniques for changing data stream(More)
Creating product lines of Big Data stream processing applications introduces a number of novel challenges to variability modeling. In this paper, we discuss these challenges and demonstrate how advanced variability modeling capabilities can be used to directly model the topology of processing pipelines as well as their variability. We also show how such(More)
  • 1