A service oriented architecture to provide data mining services for non-expert data miners

Zorrilla, Marta E.; García‐Saiz, Diego

doi:10.1016/j.dss.2012.05.045

Cited by 63 publications

(35 citation statements)

References 37 publications

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“…Researchers have shown, in particular, that BI can be used to improve a firm's operational efficiencies by minimising the mis-targeting customers [42], transforming business processes [42,45,59,62,82,138], enriching organizational intelligence [59,79,81,85,89], and developing new or improving products or services [11,68,82,86,88]. However, the BI literature has been silent on how these BI Impacts complement other internal and external factors to create business value.…”

Section: Bi Impactsmentioning

confidence: 99%

Getting value from Business Intelligence systems: A review and research agenda

Trieu

2017

Decision Support Systems

193

154

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Much of the research on Business Intelligence (BI) has examined the ability of BI systems to help organizations address challenges and opportunities. However, the literature is fragmented and lacks an overarching framework to integrate findings and systematically guide research. Moreover, researchers and practitioners continue to question the value of BI systems. This study reviews and synthesizes empirical Information System (IS) studies to learn what we know, how well we know, and what we need to know about the processes of organizations obtaining business value from BI systems. The study aims to identify which parts of the BI business value process have been studied and are still most in need of research, and to propose specific research questions for the future. The findings show that organizations appear to obtain value from BI systems according to the process suggested by Soh and Markus (1995), as a chain of necessary conditions from BI investments to BI assets to BI impacts to organizational performance; however, researchers have not sufficiently studied the probabilistic processes that link the necessary conditions together. Moreover, the research has not sufficiently covered all relevant levels of analysis, nor examined how the levels link up. Overall, the paper identified many opportunities for researchers to provide a more complete picture of how organizations can and do obtain value from BI.

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Section: Bi Impactsmentioning

confidence: 99%

Getting value from Business Intelligence systems: A review and research agenda

Trieu

2017

Decision Support Systems

193

154

View full text Add to dashboard Cite

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“…Also, the BI could not be implemented on a traditional system, because the current volume of data for analysis is massive. BI-as-a-Service [36] is other example that shows how the BI could migrate to the cloud computing systems as a software application in the SaaS layer.…”

Section: The Role Of Business Intelligencementioning

confidence: 99%

The Role of Cloud Computing Architecture in Big Data

Bahrami

Singhal

2014

Studies in Big Data

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In this data-driven society, we are collecting a massive amount of data from people, actions, sensors, algorithms and the web; handling ''Big Data'' has become a major challenge. A question still exists regarding when data may be called big data. How large is big data? What is the correlation between big data and business intelligence? What is the optimal solution for storing, editing, retrieving, analyzing, maintaining, and recovering big data? How can cloud computing help in handling big data issues? What is the role of a cloud architecture in handling big data? How important is big data in business intelligence? This chapter attempts to answer these questions. First, we review a definition of big data. Second, we describe the important challenges of storing, analyzing, maintaining, recovering and retrieving a big data. Third, we address the role of Cloud Computing Architecture as a solution for these important issues that deal with big data. We also discuss the definition and major features of cloud computing systems. Then we explain how cloud computing can provide a solution for big data with cloud services and open-source cloud software tools for handling big data issues. Finally, we explain the role of cloud architecture in big data, the role of major cloud service layers in big data, and the role of cloud computing systems in handling big data in business intelligence models.

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“…There are applications dealing with the assessment of students' learning performance, course adaptation and learning recommendations based on the student's learning behavior, evaluation of learning material and AAT To analyze student's behavior in learning systems [35] E-learning web miner To discover student's behavior profile in virtual course [36] SNAPP To display the relation evolution between participants in discussion forums [37] EDM visualization tool To visualize the process in which students solve procedural problems in logic [38] DRAL To discover relevant e-activities for learners [39] IDLS To analyze learner's browsing behavior and learning styles [40] eLAT To explore and correlate learning object usage, user properties, user behavior, as well as assessment results based on graphical indicators [41] Meerkat-ED To analyze interactions of students in asynchronous discussion forums of online courses [28] PSL-datashop MULCE To store and analyze click-stream data, fine-grained longitudinal data generated by educational systems [42] [ 43] web-based courses, providing feedback to both teacher and students in e-learning courses, and detection of students' learning behaviors [21]. A review of these studies can be found in [9,10,12,21,44,45].…”

Section: Examples Of Edm Applications In Computer-based Learning Envimentioning

confidence: 99%

Which Contribution Does EDM Provide to Computer-Based Learning Environments?

Bousbia

Belamri

2013

Educational Data Mining

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Educational Data Mining is a new growing research area that can be defined as the application of data mining techniques on raw data from educational systems in order to respond to the educational questions and problems, and also to discover the information hidden after this data. Over the last few years, the popularity of this field enhanced a large number of research studies that is difficult to surround and to identify the contribution of data mining techniques in educational systems. In fact, exploit and understand the raw data collected from educational systems can be ''a gold mine'' to help the designers and the users of these systems improving their performance and extracting useful information on the behaviors of students in the learning process. The use of data mining techniques in e-learning systems could be very interesting to resolve learning problems. Researchers' ambition is to respond to questions like: What can predict learners' success? Which scenario sequence is more efficient for a specific student? What are the student actions that indicate the learning progress? What are the characteristics of a learning environment allowing a better learning? etc. The current feedback allows detecting the usefulness of applying EDM on visualizing and describing the learning raw data. The predictions take also an interest, particularly the prediction of performance and learners' behaviors. The aim of this chapter is to establish a bibliographic review of the various studies made in the field of educational data mining (EDM) to identify the different aspects studied: the analyzed data, the objectives of these studies, the used techniques and the contribution of the application of these techniques in the field of computer based learning. The goal is not only to list the existing work but also to facilitate the use and the understanding of data mining techniques to help the educational field specialists to give their

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A service oriented architecture to provide data mining services for non-expert data miners

Cited by 63 publications

References 37 publications

Getting value from Business Intelligence systems: A review and research agenda

Getting value from Business Intelligence systems: A review and research agenda

The Role of Cloud Computing Architecture in Big Data

Which Contribution Does EDM Provide to Computer-Based Learning Environments?

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