Publikation: Efficient Mining of Discriminative Molecular Fragments
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2005
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Di Fatta, Giuseppe
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Proceedings, International Conference on Parallel and Distributed Computing and Systems 2005. 2005, pp. 619-625
Zusammenfassung
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.
Zusammenfassung in einer weiteren Sprache
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004 Informatik
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Distributed computing, frequent subgraph mining, dynamic load balancing, biochemical databases
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DI FATTA, Giuseppe, Michael R. BERTHOLD, 2005. Efficient Mining of Discriminative Molecular Fragments. In: Proceedings, International Conference on Parallel and Distributed Computing and Systems 2005. 2005, pp. 619-625BibTex
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title={Efficient Mining of Discriminative Molecular Fragments},
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<dcterms:abstract xml:lang="eng">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.</dcterms:abstract>
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