GraphFind: enhancing graph searching by low support data mining techniques.
BACKGROUND
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, a key role is played by systems that search for all exact or approximate occurrences of a query graph. To deal efficiently with graph searching, advanced methods for indexing, representation and matching of graphs have been proposed.


RESULTS
This paper presents GraphFind. The system implements efficient graph searching algorithms together with advanced filtering techniques that allow approximate search. It allows users to select candidate subgraphs rather than entire graphs. It implements an effective data storage based also on low-support data mining.


CONCLUSIONS
GraphFind is compared with Frowns, GraphGrep and gIndex. Experiments show that GraphFind outperforms the compared systems on a very large collection of small graphs. The proposed low-support mining technique which applies to any searching system also allows a significant index space reduction.
Ferro
Alfredo
A
Dipartimento di Matematica e Informatica, Università di Catania, Catania, 95125, Italy. ferro@dmi.unict.it
Giugno
Rosalba
R
Mongiovì
Misael
M
Pulvirenti
Alfredo
A
Skripin
Dmitry
D
Shasha
Dennis
D
D003198:Computer Simulation; D003628:Database Management Systems; D016208:Databases, Factual; D016247:Information Storage and Retrieval / Q000379:methods*; D008956:Models, Chemical; D008958:Models, Molecular; D012984:Software