DIB—a distributed implementation of backtracking
ACM Transactions on Programming Languages and Systems (TOPLAS)
A randomized parallel branch-and-bound procedure
STOC '88 Proceedings of the twentieth annual ACM symposium on Theory of computing
Implementing discrete mathematics: combinatorics and graph theory with Mathematica
Implementing discrete mathematics: combinatorics and graph theory with Mathematica
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Scalable load balancing techniques for parallel computers
Journal of Parallel and Distributed Computing
Compile-time minimisation of load imbalance in loop nests
ICS '97 Proceedings of the 11th international conference on Supercomputing
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Parallel Algorithms for Discovery of Association Rules
Data Mining and Knowledge Discovery
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
Context-Aware Visual Exploration of Molecular Datab
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
High performance subgraph mining in molecular compounds
HPCC'05 Proceedings of the First international conference on High Performance Computing and Communications
Decentralized proactive resource allocation for maximizing throughput of P2P Grid
Journal of Parallel and Distributed Computing
PMBC: Pattern mining from biological sequences with wildcard constraints
Computers in Biology and Medicine
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In this paper, we present a distributed computing framework for problems characterized by a highly irregular search tree, whereby no reliable workload prediction is available. The framework is based on a peer-to-peer computing environment and dynamic load balancing. The system allows for dynamic resource aggregation, does not depend on any specific meta-computing middleware and is suitable for large-scale, multi-domain, heterogeneous environments, such as computational Grids. Dynamic load balancing policies based on global statistics are known to provide optimal load balancing performance, while randomized techniques provide high scalability. The proposed method combines both advantages and adopts distributed job-pools and a randomized polling technique. The framework has been successfully adopted in a parallel search algorithm for subgraph mining and evaluated on a molecular compounds dataset. The parallel application has shown good scalability and close-to linear speedup in a distributed network of workstations.