Projects

Ontology Learning and Reasoning

• Create an ontology from structured and semi-structured data sources

• Identify mapping from the data ontology to the standard ontology

• Extract the relevant sub-graph from the standard ontology to enhance the data ontology

• Use the enhanced data ontology to bootstrap conversational space for ontology enhanced query answering

Natural Language Querying over Knowledge Bases 1 | 2

• Focused on natural language querying over knowledge bases, as well as platforms and infrastructure for large-scale data analysis, storage and querying of knowledge bases.

• Built cognitive querying systems that utilize ontologies, integrate both semi-structured and unstructured content sources, and expose natural language interfaces.

Intelligent Video Analytics 1 | 2

• 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.

Event Trend Analytics 1 | 2

• 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.