Focus on natural language querying over knowledge bases, as well as platforms and infrastructure for large-scale data analysis, storage and querying of knowledge bases.
Build cognitive querying systems that utilize ontologies, integrate both semi-structured and unstructured content sources, and expose natural language interfaces.
Designed an intelligence video analytics system that blends, in real-time, the intelligence in high-velocity streaming data sources with contextual information from many other data sources to generate complex, data-driven insights.
Built a complex situation detection solution based on spatio-temporal relationships among objects extracted from video streams.
Designed event trend optimization techniques that trade off between CPU and memory costs to execute event queries with Kleene patterns over high-rate event streams.
Proposed an online aggregation approach to dynamically compute event trend aggregation without ever constructing the actual trends.
Scalable Complex Event Analytics
Designed a share-aware optimizer that identifies opportunities for effective shared processing among CEP queries by leveraging time-based event correlations.
Developed a scalable framework that offers stream transactions to assure concurrent shared maintenance and reuse of sub-patterns across queries.
Recurring Query Processing on Big Data
Built a scalable data infrastructure that treats recurring query over big evolving data as first class citizens during query processing.
Tackled issues from massive query workloads to approximate processing to achivew low-latency execution with limited resources.
Robust Distributed Stream Processing
- Designed a new query optimization paradigm capable of coping with data fluctuations in distributed data streams arriving in large volumes, and with near-real time response requirement.