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Programming knowledge discovery workflows in service‐oriented distributed systems
Author(s) -
Cesario Eugenio,
Lackovic Marco,
Talia Domenico,
Trunfio Paolo
Publication year - 2013
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.2936
Subject(s) - computer science , workflow , exploit , knowledge extraction , distributed knowledge , data science , data discovery , process mining , grid , data stream mining , data mining , grid computing , software mining , business process discovery , distributed computing , business process , database , world wide web , business process management , metadata , business process modeling , software , work in process , knowledge management , software development , computer security , mathematics , business , software construction , geometry , marketing , programming language
SUMMARY In several scientific and business domains, very large data repositories are generated. To find interesting and useful information in those repositories, efficient data mining techniques and knowledge discovery processes must be used. The exploitation of data mining techniques in science helps scientists in hypothesis formation and gives them a support on their scientific practices, whereas in industrial processes, data mining can exploit existing data sources as a real value for companies that can take advantage from the knowledge that can be extracted from their large data sources. Data mining tasks are often composed by multiple stages that may be linked to each other to form various execution flows. Moreover, data mining tasks are often distributed because they involve data and tools located over geographically distributed environments. Therefore, it is fundamental to exploit effective paradigms, such as services and workflows, to model data mining tasks that are both multi‐staged and distributed. This paper discusses data mining services and workflows for analyzing scientific data in high‐performance distributed environments such as Grids and Clouds. We discuss how it is possible to define basic and complex services for supporting distributed data mining tasks in Grids. We also present a workflow formalism and a service‐oriented programming framework, named DIS3GNO, for designing and running distributed knowledge discovery processes in the Knowledge Grid system. DIS3GNO supports all the phases of a knowledge discovery process, including composition, execution, and results visualization. After introducing DIS3GNO, some relevant use cases implemented by it and a performance evaluation of the system are discussed.Copyright © 2012 John Wiley & Sons, Ltd.