Data-Centric Automated Data Mining

Campos, Marcos M.; Stengard, P.J.; Milenova, Boriana L.

doi:10.1109/icmla.2005.18

Cited by 26 publications

(19 citation statements)

References 3 publications

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“…The research community has been working on developing a generic system for data mining but all proposed solutions remain domain specific or require a list of very constraining assumptions about the data (Campos, Stengard, and Milenova 2005;Deshpande and Thakare 2011). There have been a number of discussions on whether the data mining process can ever be deterministic or if it will always remain a somewhat artistic discipline (Faloutsos and Megalooikonomou 2007;De Bie 2011).…”

Section: Decision Support In Plmmentioning

confidence: 99%

An autonomous system for PLM domain data exploitation

Milicic¹,

Kadiri²,

Clobes

et al. 2015

International Journal of Computer Integrated Manufacturing

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In this article, a system for automated data analysis and data modelling is presented. It is a well-known fact that a fully automated, data independent data mining system cannot be designed. It is proposed here that data from the product lifecycle management (PLM) domain can be successfully modelled mathematically by exploiting the advantages of having a semantic model of data, relaxed time constraints and by allowing sub-optimal accuracy. A data mining procedure is followed through in detail and, for each step, it is argued how the proposed assumptions tackle the challenges of automation. Finally, an industrial use case is presented to demonstrate the value gained from such a system.

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Section: Decision Support In Plmmentioning

confidence: 99%

An autonomous system for PLM domain data exploitation

Milicic¹,

Kadiri²,

Clobes

et al. 2015

International Journal of Computer Integrated Manufacturing

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“…The process of data creation, data updation and data maintenance is thoroughly checked by using different policies. The data categories are [7]:…”

Section: Data Model Considerationmentioning

confidence: 99%

Implementation of Data Mining and Data Warehousing In E-Governance

Agarwal¹,

Singh²,

Pandey³

2010

IJCA

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“…Data mining is the process of discovering useful and previously unknown information and relationships in large data sets (Campos, Stengard, & Milenova, 2005;Tan, Steinbach, & Kumar, 2006). Accordingly, data mining is the purposeful use of information technology to implement algorithms from machine learning, statistics, and artificial intelligence to analyze large data sets for the purpose of decision support.…”

Section: Introductionmentioning

confidence: 99%

“…Accordingly, a first course in programming, a database management course, and a statistical data analysis course suffice as prerequisites. For Information Systems programs, a data centric, algorithm understanding and process-automation approach to data mining similar to Jafar et al (2008a) and Campos et al (2005) is more appropriate. A data mining course in an Information Systems program has an (1) analytical component, (2) a toolsbased, hands-on component ,and (3) a rich collection of data sets.…”

Section: Introductionmentioning

confidence: 99%

A Tools-Based Approach to Teaching Data Mining Methods

Jafar

2010

JITE:IIP

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Executive SummaryData mining is an emerging field of study in Information Systems programs. Although the course content has been streamlined, the underlying technology is still in a state of flux. The purpose of this paper is to describe how we utilized Microsoft Excel's data mining add-ins as a front-end to Microsoft's Cloud Computing and SQL Server 2008 Business Intelligence platforms as backends to teach a senior level data mining methods class. The content presented and the hands on experience gained have broader applications in other areas, such as accounting, finance, general business, and marketing. Business students benefit from learning data mining methods and the usage of data mining tools and algorithms to analyze data for the purpose of decision support in their areas of specialization.Our intention is to highlight these newly introduced capabilities to faculty currently teaching a business intelligence course. Faculty interested in expanding their teaching portfolio to the data mining and the business intelligence areas may also benefit from this article.This set of integrated tools allowed us to focus on teaching the analytical aspects of data mining and the usage of algorithms through practical hands-on demonstrations, homework assignments, and projects. As a result, students gained a conceptual understanding of data mining and the application of data mining algorithms for the purpose of decision support. Without such a set of integrated tools, it would have been prohibitive for faculty to provide comprehensive coverage of the topic with practical hands-on experience.The availability of this set of tools transformed the role of a student from a programmer of data mining algorithms to a business intelligence analyst. Students now understand the algorithms and use tools to perform (1) elementary data analysis, (2) configure and use data mining computing engines to build, test, compare and evaluate various mining models, and (3) use the mining models to analyze data and predict outcomes for the purpose of decision support. If it was not for the underlying technologies that we used, it would have been impossible to cover such material in a one-semester course and provide students with much needed hands-on experience in data mining.Finally, what we presented is how to utilize the cloud as a computing platform that transformed the role of a student from "doing low-level IT" in a data mining course to a business intelligence analyst using tools to analyze data for the purpose of decision support.

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Data-Centric Automated Data Mining

Cited by 26 publications

References 3 publications

An autonomous system for PLM domain data exploitation

An autonomous system for PLM domain data exploitation

Implementation of Data Mining and Data Warehousing In E-Governance

A Tools-Based Approach to Teaching Data Mining Methods

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