Research Interest

  • BigData storage, processing and mining
  • RDF stores & SPARQL
  • Graph data mining
  • Graph Theory
  • Queries on WSN
  • Health Informatics

Current Research Directions

Retired Research Projects

Research Statement

Data analysis over massive amounts of data have become a major challenge for scientific discovery as various scientific communities embrace computational techniques and, consequently, generate more data. Graphs have re-emerged as important data structures within this context as many of the data generated and used by these domains can be represented as graphs. Examples include computational biology, chemical data analysis, drug discovery, health informatics analysis, social networking, web link analysis and communication networks. As the amount of data has increased, the management and querying of graph data, along with their analysis, have become an important data management concern.

My research interest lies in mining very large information networks (graphs). Most graph algorithms do not scale as they generally assume that the entire data set can reside in the memory and are developed as centralized algorithms that execute on one machine. Therefore, my research focuses on distributed graph management and mining algorithms over cluster environments. In particular, I’m interested in developing distributed systems and algorithms for time-evolving graphs.

My Academic Genealogy

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