Enhancing Graph Database Indexing by Suffix Tree Structure
Authors: Vincenzo Bonnici, Alfredo Ferro, Rosalba Giugno, Alfredo Pulvirenti, Dennis Shasha Keywords: subgraph isomorphism, graph database search, indexing, suffix tree, molecular database
Biomedical and chemical databases are large and rapidly growing in size. Graphs naturally model such kinds of data. To fully exploit the wealth of information in these graph databases, scientists require systems that search for all occurrences of a query graph. To deal efficiently with graph searching, advanced methods for indexing, representation and matching of graphs have been proposed.
This paper presents GraphGrepSX. The system implements efficient graph searching algorithms together with an advanced filtering technique. GraphGrepSX is compared with SING, GraphFind, CTree and GCoding. Experiments show that GraphGrepSX outperforms the compared systems on a very large collection of molecular data. In particular, it reduces the size and the time for the construction of large database index and outperforms the most popular systems. (hyperlinks added.)
Be aware that bioinformatics is at the cutting edge of search/retrieval technology. Pick up any proceedings volume for the last year to see what I mean.
A credible topic map is going to incorporate one or more of the techniques you will find there, plus semantic mapping based on those techniques.
Saying Topic-Association-Occurrence is only going to get you past the first two minutes of your presentation. You will need something familiar (to your audience) and domain specific to fill the rest of your time.
BTW, see the audience posting earlier today. Don’t guess what will interest your audience. Ask someone in that community what interests them.