P. Becuzzi, M. Coppola, and M. Vanneschi, "Mining of Association Rules in Very Large Databases: A Structured Parallel Approach," Proc. Europar-99, vol. 1685, pp. 1441-1450, Aug. 1999.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....Work We now compare our work with related research efforts. Significant amount of work has been done on parallelization of individual data mining techniques that can be parallelized through our approach. Most of the work has been on distributed memory machines, including association mining [4, 7, 22, 23], k Means clustering technique [6, 17, 38] and bayesian networks [20] Our work is significantly different, because we offer an interface and runtime support to parallelize each of these algorithms. Shared memory parallelization of association mining rules has also been an area of attention. ....

....parallelization techniques used in our middleware are significantly different, because we focus on techniques that can be used across a number of parallel data mining algorithms, or data intensive reduction operations in general. One effort somewhat similar to our work is from Becuzzi et al. [7]. They use a structured parallel programming environment PQE2000 SkIE for developing parallel implementation of data mining algorithms. Darlington et al. 16] have also used structured parallel programming for developing data mining algorithms. Our work is distinct at least two important ways. ....

P. Becuzzi, M. Coppola, and M. Vanneschi. Mining of association rules in very large databases: A structured parallel approach. In Proceedings of Europar-99, Lecture Notes in Computer Science (LNCS) Volume 1685, pages 1441 -- 1450. Springer Verlag, August 1999.

....different, because we offer an interface and runtime support to parallelize a number of data mining algorithms. Our shared memory parallelization techniques are also significantly different, because we focus on a common framework for parallelization of a number of algorithms. Becuzzi et al. [4] have used a structured parallel programming environment PQE2000 SkIE for developing parallel implementation of data mining algorithms. However, they only focus on distributed memory parallelization, and I O is handled explicitly by the programmers. The similarity among parallel versions of ....

P. Becuzzi, M. Coppola, and M. Vanneschi. Mining of association rules in very large databases: A structured parallel approach. In Proceedings of Europar-99, Lecture Notes in Computer Science (LNCS) Volume 1685, pages 1441 -- 1450. Springer Verlag, August 1999. 9

....on shared memory and hierarchical systems [10, 20, 21, 31, 29] Through the use of our middleware, we combine task and data parallelism, and exploit four new techniques for avoiding race conditions while updating candidate counts. One effort somewhat similar to our work is from Becuzzi et al. [3]. They use a structured parallel programming environment PQE2000 SkIE for developing parallel implementation of data mining algorithms. Darlington et al. 12] have also used structured parallel programming for developing data mining algorithms. Our work is distinct at least two important ways. ....

P. Becuzzi, M. Coppola, and M. Vanneschi. Mining of association rules in very large databases: A structured parallel approach. In Proceedings of Europar-99, Lecture Notes in Computer Science (LNCS) Volume 1685, pages 1441 -- 1450. Springer Verlag, August 1999.

....has been fostered and supported by several research and development projects, which resulted in the P3L language and the SkIE PPE [1, 2, 3] Here we present our analysis of a signi cant set of DM techniques, which we have ported from sequential to parallel with SkIE. We report our experiences [4, 5, 6, 7] about the problems of association rule extraction, classi cation and spatial clustering. We have developed three prototype applications by restructuring sequential code to structured parallel programs. The SPP approach of the SkIE coordination language is evaluated against the engineering and ....

....in SkIE is a very good example of the advantages of structured parallel programming. A sequential source code has been restructured in a modular parallel application, whose code is less than 25 larger and reuses 90 of the original. The development times were also quite short, as reported in [4]. The test results of gure 3 5 are consistent over a range of di erent architectures. We used the synthetic dataset generator from the Quest project, whose underlying model is also explained in [18] choosing average frequent sets of 9 O AB ABC D C B A w w farm pipe w w farm ....

P. Becuzzi, M. Coppola, M. Vanneschi, Mining of Association Rules in Very Large Databases: a Structured Parallel Approach, in: Euro-Par'99 Parallel Processing, Vol. 1685 of LNCS, Springer, 1999, pp. 1441-1450.

....the engineering of High Performance applications. To provide a test bed for the current environment, as well as to investigate the theoretical problems involved in the parallelisation of largely used algorithms, we are developing parallel versions of Data Mining (DM) applications. Work is ongoing [2,3] to develop DM computational kernels that exhibit both code and performance portability over various parallel architectures, where performance means parallel speed up and scalability to large databases. Here we present the current results about the C4.5 algorithm, focusing on two main issues: ....

P. Becuzzi, M. Coppola, and M. Vanneschi. Mining of Association Rules in Very Large Databases: a Structured Parallel Approach. In Euro-Par'99 Parallel Processing, volume 1685 of LNCS. Springer, 1999.

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P. Becuzzi, M. Coppola, and M. Vanneschi, "Mining of Association Rules in Very Large Databases: A Structured Parallel Approach," Proc. Europar-99, vol. 1685, pp. 1441-1450, Aug. 1999.

No context found.

P. Becuzzi, M. Coppola, and M. Vanneschi. Mining of association rules in very large databases: A structured parallel approach. In Proceedings of Europar-99, Lecture Notes in Computer Science (LNCS) Volume 1685, pages 1441 -- 1450. Springer Verlag, August 1999.

No context found.

P. Becuzzi, M. Coppola, and M. Vanneschi. Mining of association rules in very large databases: A structured parallel approach. In Proceedings of Europar-99, Lecture Notes in Computer Science (LNCS) Volume 1685, pages 1441 - 1450. Springer Verlag, August 1999.

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