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| Author(s) |
Di Fatta, G., Berthold, M. |
| Title |
Efficient mining of discriminative molecular fragments |
| Abstract |
Frequent pattern discovery in structured data is receiving
an increasing attention in many application areas of sciences.
However, the computational complexity and the large
amount of data to be explored often make the sequential algorithms
unsuitable. In this context high performance distributed
computing becomes a very interesting and promising
approach. In this paper we present a parallel formulation
of the frequent subgraph mining problem to discover
interesting patterns in molecular compounds. The application
is characterized by a highly irregular tree-structured
computation. No estimation is available for task workloads,
which show a power-law distribution in a wide range. The
proposed approach allows dynamic resource aggregation
and provides fault and latency tolerance. These features
make the distributed application suitable for multi-domain
heterogeneous environments, such as computational Grids.
The distributed application has been evaluated on the wellknown
National Cancer Institute's HIV-screening dataset. |
| Download |
DiBe05b.pdf |
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