Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Globus® Toolkit 4, First Edition: Programming Java Services (The Morgan Kaufmann Series in Networking)
The Design and Evaluation of MPI-Style Web Services
IEEE Transactions on Services Computing
Weka4WS: a WSRF-enabled weka toolkit for distributed data mining on grids
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Parallel and distributed methods for incremental frequent itemset mining
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
| Hi-index | 0.00 |
The Web Services Resource Framework (WSRF) is the standard for the implementation of Grid applications which can be exploited for developing high-level services for distributed data mining applications. More performance can be achieved if there is support for tightly-coupled services where running services can exchange messages with each other as per MPI standards. This paper presents the design and development of an efficient frequent itemset mining framework for mining incremental and distributed data on Grid, integrated with MPI programming technologies of MPI-style Web Services (MPIWS). MPIWS takes advantage of the SOAP communication protocol, and allows direct MPI-style communication among loosely coupled services. The proposed framework generates local models as well as global model of frequent itemset mining. Both of these models are stored in WSRF stateful resource and used in subsequent mining over incremented dataset. The proposed framework is fully compliant with WSRF specifications. It has been evaluated for its performance analysis with various Grid configurations and dataset increment sizes. The obtained results validate the feasibility and efficiency of MPI style web services in Grid environment for tightly-coupled data mining applications.