Besides volume, another important aspect of big data is velocity, in which data are generated continuously and often in high-speed. For this type of data, we need to process and analyze streaming data both efficiently and accurately. Research challenges in this topic include 1) how to handle challenges such as concept drifting that are unique for streaming data analytics, and 2) how to design software architecture/algorithms to work with both incoming streaming data and historical batch data.
We have worked on novel software designs and algorithms for various streaming data analytics applications including smart traffic/transportation, software-defined networking, cybersecurity. Our work in this research topic results in peer-reviewed publications at major journals (Sensors, IEEE Access, Neurocomputing, etc.) and conferences (IEEE Big Data, BigData Congress, etc.).
- 2020 – 2024: AI and Autonomy for Multi-Agent Systems, Army Research Lab (ARL)
- 2017 – 2018: Low-Code Workflow Software for Life Sciences, Maryland Industrial Partnerships (MIPS) Program