Review of The data warehouse toolkit

Anisimov, Alexander

doi:10.1145/945721.945741

Cited by 33 publications

(2 citation statements)

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“…To help the development of the WIS, Kimball's lifecycle methodology was used, to simplify and coordinate the work. This methodology consists on the creation of a set of activities, required to design, develop and deploy a data warehouse (DW) successfully [15]. For Kimball and Ross [15], and for the development of this project, this methodology could be divide in 8 activities: Planning; Define the business requirements; Design the multidimensional model; Design the technological architecture; Development and conception of the WIS; Implementation of the system; Test the system; Results.…”

Section: B Methodologymentioning

confidence: 99%

Web Intelligence in Higher Education: A Study on the Usage of Business Intelligence Techniques in Education

Maia

Portela

Santos

2018

2018 6th International Conference on Future Internet of Things and Cloud Workshops (FiCloudW)

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The usage of web intelligence systems in Education has been growing in the last years. The large amount of papers, dissertations and articles about this topic, created in the last decade, is a proof of the investment in the research of this theme. This paper has the main purpose of obtaining a better comprehension of the benefits and the disadvantages of implementing this type of systems to improve the way that we learn and teach. With this main goal in mind and considering that a simple analysis of a normalized database is not sufficient, a Web Intelligent System (WIS) was designed. This type of implementation allows the evaluation of large amounts of data (in this case related to Education), with a lot of incoherences, most of the time, which could be more easily analyzed with the help of the tools, supplied by these systems. This study was conducted with a sample of more than 133 students, that attended the course of Web Programming, in MiEGSI (master's degree in Information Systems) in University of Minho. With the WIS, it was possible to understand the main reasons that lead to success or retention in education, if this type of implementation was well accepted and used by the students, along with other important aspects, which can be verified by the results obtained. The application was accessed 21349 times, which means, in average, every student accessed it 143 times. The rate of retention is 10.5 % which corresponds to a total of 14 students that did not achieve success within the course. In average 25 students did not attend the classes, which links to the principal cause of retention.

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Section: B Methodologymentioning

confidence: 99%

Web Intelligence in Higher Education: A Study on the Usage of Business Intelligence Techniques in Education

Maia

Portela

Santos

2018

2018 6th International Conference on Future Internet of Things and Cloud Workshops (FiCloudW)

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“…The tuple represents that "user U (optionally, using device D) at time T employs service S on Resource R with result X." All these tuples are stored to a single entity that is related to others that stores data about users (U), resources (R), and results (X), following a star schema [59].…”

Section: Data Source: the Universitat Oberta De Catalunya Data Martmentioning

confidence: 99%

A Predictive Analytics Infrastructure to Support a Trustworthy Early Warning System

et al. 2021

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Learning analytics is quickly evolving. Old fashioned dashboards with descriptive information and trends about what happened in the past are slightly substituted by new dashboards with forecasting information and predicting relevant outcomes about learning. Artificial intelligence is aiding this revolution. The accessibility to computational resources has increased, and specific tools and packages for integrating artificial intelligence techniques leverage such new analytical tools. However, it is crucial to develop trustworthy systems, especially in education where skepticism about their application is due to the risk of teachers’ replacement. However, artificial intelligence systems should be seen as companions to empower teachers during the teaching and learning process. During the past years, the Universitat Oberta de Catalunya has advanced developing a data mart where all data about learners and campus utilization are stored for research purposes. The extensive collection of these educational data has been used to build a trustworthy early warning system whose infrastructure is introduced in this paper. The infrastructure supports such a trustworthy system built with artificial intelligence procedures to detect at-risk learners early on in order to help them to pass the course. To assess the system’s trustworthiness, we carried out an evaluation on the basis of the seven requirements of the European Assessment List for trustworthy artificial intelligence (ALTAI) guidelines that recognize an artificial intelligence system as a trustworthy one. Results show that it is feasible to build a trustworthy system wherein all seven ALTAI requirements are considered at once from the very beginning during the design phase.

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