PaDDMAS: parallel and distributed data mining application suite

Rana, Omer F.; Walker, Donald F.; Li, Maozhen; Lynden, Steven; Ward, M.

doi:10.1109/ipdps.2000.846010

Cited by 20 publications

(13 citation statements)

References 4 publications

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“…Another interesting distributed data mining suite based on Java is Parallel and distributed data mining application suite (PaDDMAS), a component-based tool set that integrates pre-developed or custom packages (that can be sequential or parallel) using a dataflow approach [11].…”

Section: Related Workmentioning

confidence: 99%

Grid implementation of the Apriori algorithm

Aflori

Craus

2007

Advances in Engineering Software

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Section: Related Workmentioning

confidence: 99%

Grid implementation of the Apriori algorithm

Aflori

Craus

2007

Advances in Engineering Software

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“…That system uses PMML for predictive models and an XML language called Data Space Markup Language for data and information contained in clusters. Another distributed data mining suite based on Java is PaDDMAS [29], a componentbased tool set that integrates pre-developed or custom packages by using a dataflow approach. Each system component is wrapped as a Java or CORBA object whose interface is specified in XML.…”

Section: Related Workmentioning

confidence: 99%

Metadata for Managing Grid Resources in Data Mining Applications

2004

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The Grid is an infrastructure for resource sharing and coordinated use of those resources in dynamic heterogeneous distributed environments. The effective use of a Grid requires the definition of metadata for managing the heterogeneity of involved resources that include computers, data, network facilities, and software tools provided by different organizations. Metadata management becomes a key issue when complex applications, such as dataintensive simulations and data mining applications, are executed on a Grid. This paper discusses metadata models for heterogeneous resource management in Grid-based data mining applications. In particular, it discusses how resources are represented and managed in the KNOWLEDGE GRID, a framework for Grid-enabled distributed data mining. The paper illustrates how XML-based metadata is used to describe data mining tools, data sources, mining models, and execution plans, and how metadata is used for the design and execution of distributed knowledge discovery applications on Grids.

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“…Then, we propose a system of services aiming to cover the fundamental features of such a support system. In the literature, solutions adopting similar paradigms exist, [9,11,15,2], but they are mainly concerned with largescale and high-performance issues, and they focus mainly on the Data Mining phase (see also [13] for an extensive survey), without considering the KDD process as a whole. As a consequence, they share with the present proposal some basic feature, but they lack to include higher-level supports to the user.…”

Section: Introductionmentioning

confidence: 99%

Developing an open knowledge discovery support system for a network environment

Diamantini

Potena

Panti

2005

Proceedings of the 2005 International Symposium on Collaborative Technologies and Systems, 2005.

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Knowledge Discovery in Databases (KDD) is a highly complex process where a lot of data manipulation tools with different characteristics can, and in fact have to, be used together in an interactive and iterative fashion, to reach the goal of previously unknown, potentially useful information extraction. In this paper we analyze the major sources of complexity in the framework of network organizations, pointing out the necessity to give support to the user in many different ways and at very different levels of granularity, from the use of a single tool, to the management of whole, distributed, KDD projects. Unfortunately, currently available systems lack to support the users in at least some of these features. We then propose a solution based on the Service Oriented Computing paradigm, arguing that the advantages ofthis paradigm, namely openness, modularity, reusability and transparency, as well as ubiquity, can help in the design of an effective support system for Knowledge Discovery in Databases in network environments.

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PaDDMAS: parallel and distributed data mining application suite

Abstract: Discovering complex associations, anomalies and patterns in distributed data

Cited by 20 publications

References 4 publications

Grid implementation of the Apriori algorithm

Grid implementation of the Apriori algorithm

Metadata for Managing Grid Resources in Data Mining Applications

Developing an open knowledge discovery support system for a network environment

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