1. Research at Open Systems Lab IIIT Bangalore http://osl.iiitb.ac.in/

2. Broad Areas Co-occurrence analyses
Multi-agent approaches for (database related) optimization

3. Co-occurrence Analyses
Using models of semantic memory from cognitive psychology to extract latent semantics in document collections
Graphs
depicting
higher-order
inferences
Co-occurrence (labeled) graph
Document corpus

4. Co-occurrence graph
Captures pair-wise co-occurrences across different typed entities
Entity types
Nouns (Person, Institution, Place, Country, etc.)
Tags
URLs
Phrases

5. Higher-order inferences
Topic anchors
Topic markers
Synonymy
Semantic siblings
Topic induction

6. Higher-order inferences
Co-citations as URL co-occurrences
Contrasting co-citation patterns between Web pages and Wikipedia
Co-citation as hyperlink endorsements
Co-citation as knowledge aggregation
Co-citation as conditional probability of topical relevance

7. Dataset Co-occurrence graph built from a complete Wikipedia dump
Co-citation graph built from a crawl of over 10 million pages and over 85 million hyperlinks

8. Some results Topical anchor experiments

9. Some results: web co-citation graph

10. Some results: endorsed hyperlink graph

11. Some questions Innate macro characteristics of co-occurrence graphs
Concept formation from instances of co-occurrences
Multipartite clustering

12. Multi-agent optimization
Query optimization in stream grids
Distributed index design under arbitrary constraints (churn, load, symmetry, etc.)

13. Thank you