In this article, we focus on distributed Apriori-based frequent itemsets mining. We present a new distributed approach which takes into account inherent characteristics of this algorithm. We study the distribution aspect of this algorithm and give a comparison of the proposed approach with a classical Apriori-like distributed algorithm, using both analytical and experimental studies. We find that under a wide range of conditions and datasets, the performance of a distributed Apriori-like algorithm is not related to global strategies of pruning since the performance of the local Apriori generation is usually characterized by relatively high success rates of candidate sets frequency at low levels which switch to very low rates at some stage, and often drops to zero. This means that the intermediate communication steps and remote support counts computation and collection in classical distributed schemes are computationally inefficient locally, and then constrains the global performance. Our performance evaluation is done on a large cluster of workstations using the Condor system and its workflow manager DAGMan. The results show that the presented approach greatly enhances the performance and achieves good scalability compared to a typical distributed Apriori founded algorithm.

This paper presents a case study concerning the challenges and requirements posed by next generation language resources, realized as an overall model of open, distributed and collaborative language infrastructure. If a sort of "new paradigm" is required, we think that the emerging and still evolving technology connected to Grid computing is a very interesting and suitable one for a concrete realization of this vision. Given the current limitations of Grid computing, it is very important to test the new environment on basic language analysis tools, in order to get the feeling of what are the potentialities and possible limitations connected to its use in NLP. For this reason, we have done some experiments on a module of Linguistic Miner, i.e. the extraction of linguistic patterns from restricted domain corpora.