DIB—a distributed implementation of backtracking
ACM Transactions on Programming Languages and Systems (TOPLAS)
Compile-time minimisation of load imbalance in loop nests
ICS '97 Proceedings of the 11th international conference on Supercomputing
Molecular feature mining in HIV data
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Parallel Mining of Association Rules
IEEE Transactions on Knowledge and Data Engineering
Scalable Parallel Data Mining for Association Rules
IEEE Transactions on Knowledge and Data Engineering
Mining Molecular Fragments: Finding Relevant Substructures of Molecules
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Frequent Sub-Structure-Based Approaches for Classifying Chemical Compounds
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
State of the art of graph-based data mining
ACM SIGKDD Explorations Newsletter
Parallel algorithms for mining frequent structural motifs in scientific data
Proceedings of the 18th annual international conference on Supercomputing
High performance subgraph mining in molecular compounds
HPCC'05 Proceedings of the First international conference on High Performance Computing and Communications
A customizable multi-agent system for distributed data mining
Proceedings of the 2007 ACM symposium on Applied computing
Application of load balancing based on symmetric balanced incomplete block design to random networks
Information Sciences: an International Journal
An efficient distributed algorithm for canonical labeling on directed split-stars
Discrete Applied Mathematics
Performance-based data distribution for data mining applications on grid computing environments
The Journal of Supercomputing
An iterative MapReduce approach to frequent subgraph mining in biological datasets
Proceedings of the ACM Conference on Bioinformatics, Computational Biology and Biomedicine
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In molecular biology, it is often desirable to find common properties in large numbers of drug candidates. One family of methods stems from the data mining community, where algorithms to find frequent graphs have received increasing attention over the past years. However, the computational complexity of the underlying problem and the large amount of data to be explored essentially render sequential algorithms useless. In this paper, we present a distributed approach to the frequent subgraph mining problem to discover interesting patterns in molecular compounds. This problem is characterized by a highly irregular search tree, whereby no reliable workload prediction is available. We describe the three main aspects of the proposed distributed algorithm, namely, a dynamic partitioning of the search space, a distribution process based on a peer-to-peer communication framework, and a novel receiver-initiated load balancing algorithm. The effectiveness of the distributed method has been evaluated on the well-known National Cancer Institute's HIV-screening data set, where we were able to show close-to linear speedup in a network of workstations. The proposed approach also allows for dynamic resource aggregation in a nondedicated computational environment. These features make it suitable for large-scale, multidomain, heterogeneous environments, such as computational grids.