This Computing Research Infrastructure project aims to enhance Worcester Polytechnic Institute's (WPI) high-performance infrastructure to support its ongoing research and education centered around big data analytics over scalable infrastructure. The enhanced compute clustering will provide a scalable infrastructure to support large-scale data analytics research along a number of important directions, including (1) a full-fledged software platform for seamlessly supporting large-scale continuous and recurring workloads with stream-window-centric query processing, (2) scalable annotation and provenance management for natively supporting annotations as first-class citizen over big data and exploiting their semantics in query processing and quality inference, (3) complex event analytics that combines complex event processing with OLAP techniques for efficient multi-dimensional event pattern detection in high-speed large-volume data, and (4) multi-model-driven analytics system on high-velocity data streams with visual exploration of the derived models and patterns. The projects collectively address key research challenges related to the analysis of large volume, high velocity, and wide variety of data.
The infrastructure is expected to significantly enhance the integration of research and education at both the undergraduate and graduate levels at WPI, including in particular, WPI's project-oriented undergraduate curriculum, which requires each undergraduate to complete a year-long research project. The infrastructure will also benefit a diverse group of researchers at WPI in different departments -- as is the case with the current compute cluster -- to conduct their data-intensive research. Broad dissemination of results of research enabled by the infrastructure including research publications, open-source software, and educational materials are expected to benefit the scientific community at large.
