Digging Deep into the Data Mine with DataMiningGrid

Stankovski, Vlado; Swain, Martin; Kravtsov, Valentin; Nießen, T.; Wegener, Dennis; Rohm, M.; Trnkoczy, Jernej; May, Michael; Franke, Juergen; Schuster, Assaf; Dubitzky, Werner

doi:10.1109/mic.2008.122

Cited by 20 publications

(6 citation statements)

References 11 publications

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“…Fig. 6 System architecture of execution engine [37] 3.3 DDM systems based on Grid DM in grid [13,30] computing environment represents a specific incarnation of DDM motivated by resource sharing via local and wide area networks [48]. Increased performance, scalability, access, and resource exploitation are the key drivers behind such endeavors.…”

Section: Ddm Systems Based On Meta-learningmentioning

confidence: 99%

“…For highly domain-oriented end users, user transparency and ease-of-use is paramount. Technologyaware specialistsneed to control certain detailed aspects of DM and grid technology [48]. Today, new DDM projects aim to mine data in a geographically distributed environment which is based on grid standards and platforms, in order to hide the complexity of heterogeneous data and lower level details.…”

Section: Ddm Systems Based On Meta-learningmentioning

confidence: 99%

“…Based on the principle of SOA [17,24,31,32,54,60], standardization and open source, Stankovski et al [48] proposed a DDM system based on a Data Mining Grid (DMG). Figure 8 depicts the DMG system architecture in four layers.…”

Section: Ddm Systems Based On Meta-learningmentioning

confidence: 99%

“…The DMG system architecture[48] layer model for AGrIP platform from an implementation point of view, as illustrated inFig. 10:…”

mentioning

confidence: 99%

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Distributed data mining for e-business

Liu

Cao

2011

Inf Technol Manag

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In the internet-based e-business environment, most business data are distributed, heterogeneous and private. To achieve true business intelligence, mining large amounts of distributed data is necessary. Through a thorough literature review, this paper identifies four main issues in distributed data mining (DDM) systems for e-business and classifies modern DDM systems into three classes with representative samples. To address these identified issues, this paper proposes a novel DDM model named DRHPDM (Data source Relevance-based Hierarchical Parallel Distributed data mining Model). In addition, to improve the quality of the final result, the data sources are divided into a centralized mining layer and a distributed mining layer, according to their relevance. To improve the openness, cross-platform ability, and intelligence of the DDM system, web service and multi-agent technologies are adopted. The feasibility of DRHPDM was verified by building a prototype system and applying it to a web usage mining scenario.

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Section: Ddm Systems Based On Meta-learningmentioning

confidence: 99%

Section: Ddm Systems Based On Meta-learningmentioning

confidence: 99%

Section: Ddm Systems Based On Meta-learningmentioning

confidence: 99%

“…The DMG system architecture[48] layer model for AGrIP platform from an implementation point of view, as illustrated inFig. 10:…”

mentioning

confidence: 99%

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Distributed data mining for e-business

Liu

Cao

2011

Inf Technol Manag

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“…Also ensemble learners are naturally suited for learning from distributed data sources. Also the DataMiningGrid.org project is concerned with all sorts of data mining applications on geographically distributed data sources (Stankovski et al, 2008). Another framework that is concerned with learning from distributed heterogeneous data sources is described by Caragea et al (2003) and has successfully been demonstrated for classification rule induction in the form of decision trees.…”

Section: Introductionmentioning

confidence: 99%

Scaling up classification rule induction through parallel processing

Stahl

Bramer

2012

The Knowledge Engineering Review

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The fast increase in the size and number of databases demands data mining approaches that are scalable to large amounts of data. This has led to the exploration of parallel computing technologies in order to perform data mining tasks concurrently using several processors. Parallelization seems to be a natural and cost-effective way to scale up data mining technologies. One of the most important of these data mining technologies is the classification of newly recorded data. This paper surveys advances in parallelization in the field of classification rule induction.

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Data mining and life sciences applications on the grid

Cannataro

Guzzi

Sarica

2013

WIREs Data Min & Knowl

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Data mining (DM) is increasingly used in the analysis of data generated in life sciences, including biological data produced in several disciplines such as genomics and proteomics, medical data produced in clinical practice, and administrative data produced in health care. The difficulty in mining such data is twofold. First of all, data in life sciences are inherently heterogeneous, spanning from molecular level data to clinical and administrative data. Second, data in life sciences are produced at an increasing rate and data repositories are becoming very large. Thus, the management and analysis of such data is becoming a main bottleneck in biomedical research. The main goal of this paper is to review the main methodologies to mine life sciences data and the ways they are coupled to high‐performance infrastructures and systems that result in an efficient analysis. This paper recalls basic concepts of DM, grids, and distributed DM on grids, and reviews main approaches to mine biomedical data on high‐performance infrastructures with special focus on the analysis of genomics, proteomics, and interactomics data, and the exploration of magnetic resonance images in neurosciences. The paper can be of interest both to bioinformaticians, who can learn how to exploit high performance infrastructures to mine life sciences data, and to computer scientists, who can address the heterogeneity and the high volumes of life sciences data at the data management, algorithm, and user interface layers. © 2013 Wiley Periodicals, Inc.This article is categorized under: Algorithmic Development > Biological Data Mining Application Areas > Data Mining Software Tools Application Areas > Health Care Technologies > Computer Architectures for Data Mining

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Digging Deep into the Data Mine with DataMiningGrid

Cited by 20 publications

References 11 publications

Distributed data mining for e-business

Distributed data mining for e-business

Scaling up classification rule induction through parallel processing

Data mining and life sciences applications on the grid

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