Approaches to Parallel Graph-Based Knowledge Discovery

Cook, Diane J.; Holder, Lawrence B.; Galal, Gehad; Maglothin, Ron

doi:10.1006/jpdc.2000.1696

Cited by 37 publications

(20 citation statements)

References 14 publications

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“…The edges cut at the partition boundaries pose a challenge to the quality of discovery. In earlier work, a static partitioning algorithm 14 was introduced to scale the Subdue graph-based data mining algorithm using distributed processing. This type of parallelism is appealing in terms of memory usage and speedup.…”

Section: Parallel Graph-based Knowledge Discoverymentioning

confidence: 99%

Iterative Structure Discovery in Graph-Based Data

Coble

Rathi

Cook

et al. 2005

Int. J. Artif. Intell. Tools

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Received Revised AcceptedMuch of current data mining research is focused on discovering sets of attributes that discriminate data entities into classes, such as shopping trends for a particular demographic group. In contrast, we are working to develop data mining techniques to discover patterns consisting of complex relationships between entities. Our research is particularly applicable to domains in which the data is event-driven or relationally structured. In this paper we present approaches to address two related challenges; the need to assimilate incremental data updates and the need to mine monolithic datasets. Many realistic problems are continuous in nature and therefore require a data mining approach that can evolve discovered knowledge over time. Similarly, many problems present data sets that are too large to fit into dynamic memory on conventional computer systems. We address incremental data mining by introducing a mechanism for summarizing discoveries from previous data increments so that the globally-best patterns can be computed by mining only the new data increment. To address monolithic datasets we introduce a technique by which these datasets can be partitioned and mined serially with minimal impact on the result quality. We present applications of our work in both the counter-terrorism and bioinformatics domains.

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Section: Parallel Graph-based Knowledge Discoverymentioning

confidence: 99%

Iterative Structure Discovery in Graph-Based Data

Coble

Rathi

Cook

et al. 2005

Int. J. Artif. Intell. Tools

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“…† For atomistic simulations of materials, a challenge is to extract topological defects such as dislocations from massive data with large thermal noises. Graph data structures have played an important role in analyzing atomistic data [2,7,14], where vertices and edges represent atoms and bonds, respectively. Recently, we have used a shortest-path ring analysis to study intermediate-range orders in amorphous materials [8] and an edge-based indexing to detect grain boundaries in semiconductors nanocrystals [17].…”

Section: Spacefilling-curve-based Adaptive Data Compression For Scalamentioning

confidence: 99%

Scalable atomistic simulation algorithms for materials research

Nakano

Kalia

Vashishta

et al. 2001

Proceedings of the 2001 ACM/IEEE Conference on Supercomputing

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A suite of scalable atomistic simulation programs has been developed for materials research based on space-time multiresolution algorithms. Design and analysis of parallel algorithms are presented for molecular dynamics (MD) simulations and quantum-mechanical (QM) calculations based on the density functional theory. Performance tests have been carried out on 1,088-processor Cray T3E and 1,280-processor IBM SP3 computers. The linear-scaling algorithms have enabled 6.44-billion-atom MD and 111,000-atom QM calculations on 1,024 SP3 processors with parallel efficiency well over 90%. The production-quality programs also feature wavelet-based computational-space decomposition for adaptive load balancing, spacefilling-curve-based adaptive data compression with user-defined error bound for scalable I/O, and octree-based fast visibility culling for immersive and interactive visualization of massive simulation data.

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“…[12], [20]. For graph mining only parallelizations on PC clusters with distributed memory have been developed for Subdue [7] and MoFa [9].…”

Section: Introductionmentioning

confidence: 99%

Mining Molecular Datasets on Symmetric Multiprocessor Systems

Meinl

Wörlein

Fischer

et al. 2006

2006 IEEE International Conference on Systems, Man and Cybernetics

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Abstract-Although in the last years about a dozen sophisticated algorithms for mining frequent subgraphs have been proposed, it still takes too long to search big databases with 100,000 graphs and more. Even the currently fastest algorithms like gSpan, FFSM, Gaston, or MoFa need hours to complete their tasks. This paper presents thread-based parallel versions of MoFa [5] and gSpan [26] that achieve speedups up to 11 on a sharedmemory SMP system using 12 processors. We discuss the design space of the parallelization, the results, and the obstacles, that are caused by the irregular search space and by the current state of Java technology.

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Approaches to Parallel Graph-Based Knowledge Discovery

Cited by 37 publications

References 14 publications

Iterative Structure Discovery in Graph-Based Data

Iterative Structure Discovery in Graph-Based Data

Scalable atomistic simulation algorithms for materials research

Mining Molecular Datasets on Symmetric Multiprocessor Systems

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