Business Intelligence Tools In Data Mining

Business Intelligence Tools In Data Mining – From NEDC to WLTP: The Effect of Energy Consumption, NEV Credits, and PHEV Subsidy Policies on the Chinese Market.

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Business Intelligence Tools In Data Mining

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What Are Business Intelligence Tools And The Types Of Business Intelligence Software In 2022

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Solution: Knowledge Management Class Notes_22547305 Business Intelligence And Knowledge Management

Received: 28 May 2020 / Revised: 12 July 2020 / Accepted: 13 July 2020 / Published: 17 July 2020

Now, universities are forced to change educational paradigms, where knowledge is based mainly on the teacher’s experience. These changes include the development of quality education that focuses on student learning. These factors have forced universities to look for a solution that allows them to extract data from different information systems and translate it into information necessary to make decisions that improve learning outcomes. Information systems managed by universities store large volumes of data on socio-economic variables and student studies. In the university field, these data are not widely used to generate information about their students, unlike in the business field, where the data is mainly analyzed for business intelligence to gain a competitive advantage. These business success stories can be replicated in universities through educational data analysis. This document presents an approach that integrates modeling and data mining techniques within a business intelligence framework to make decisions about variables that may affect learning development. In order to test the proposed method, a case study is presented, where students are identified and classified according to the data they generate in different university information systems.

Currently, the use of information and communication technologies (ICTs) is included in all public activities. Universities are not far behind, and are incorporating ICTs into many of their programs. These systems combine the administrative control on which the existence of universities depends or use as support for academic management [1]. The most widespread use of ICT in course management is the learning management system (LMS) [2] which supports online communication between teachers and students. However, there are situations where specific support of ICTs is needed to solve common learning-based problems. These conditions allow ICTs to use new models and teaching methods in student learning. The guide to this can be the personalization that companies succeed in their customers by using data analysis models that allow managers, executives and analysts to find trends and improve the services and products they offer to their customers.

Personal service can be introduced in educational contexts where the process is similar to that used at the business level, but the purpose of education is to improve methods or activities that enable learning for students [3]. Learning environments are primarily based on a range of interactive services and their delivery. Personalized advising systems can provide learning recommendations to students based on their needs [4, 5]. Companies use data analytics architecture whose results help them make decisions about their business. These structures are called business intelligence (BI); their ability to extract data from different sources, process it and turn it into information is a solution that can be included in the management of university education [6].

Figure 1.6 From Business Intelligence: Data Mining And Optimization For Decision Making

As an example, it is important to consider that many universities use a BI platform that focuses on management or operations, which helps them make decisions in the management of the institution’s finances [7]. In the same way, previous works [8, 9] have made an analysis of dropout rates considering the model and statistical tools and the use of economic and educational variables, distinguishing the analysis if the students are enrolled or not in the next semester. This formula works perfectly; however, it ignores the factors that determine why students drop out. In contrast, our proposal is distinguished by the ability to analyze the data of students’ academic activities and focus on the learning problems they present. This analysis helps in decision-making in educational management and the improvement of learning methods established by teachers [10].

In this work, three research questions are proposed that help align the concepts and processes in their design; in addition, they want to establish the current state of the environment in which this work is done:

To answer all these questions, this work includes a description of the BI framework that supports its design through a thorough review of previous works, a Unified Modeling Language (UML) diagram and a complete method of using educational data mining. This function extracts data from various educational sources, processes it and allows us to identify, through data mining algorithms, the strengths and weaknesses of each student. Once the results are obtained, information is generated about each student’s learning process, which allows appropriate decisions to be made to improve student learning.

This article is organized as follows: Section 2 reviews existing work related to the purpose of this study; Section 3 describes the components and processes of the proposed framework; Section 4 applies the method to a case study, to assess the suitability of the method; and Section 5 presents the conclusions.

Essential Business Intelligence Statistics: 2021 Analysis Of Trends, Data And Market Share

The literature review presented follows the published guidelines for the systematic literature review method proposed by Kitchenham et al. [11] and Petersen et al. [12]. Kitchenham et al. explain how the results of a literature review in software engineering should be planned, implemented and presented; Petersen et al. provide guidance on how to conduct a rigorous literature review and follow a systematic process. In our literature review, the works were grouped according to the type of tool, model, paradigm or discussion they used in their analysis of educational data. In this type of classification, it was necessary to know the nature of scientific work in learning areas that involve the use of BI techniques that improve education. The purpose of this literature review is to try to learn how they do it, and what methods and techniques they use. The search string “business intelligence AND education” was selected, and only documents published in the last 5 years were considered.

The search was conducted based on the information provided in the title, abstract and keywords of the works. From the selected works, a detailed reading of the introduction and conclusions is made, in order to filter out the publications that are not relevant.

Figure 1 shows a flowchart of the bibliography selection process; the first phase collects articles from online databases. The string terms used to search for articles in online databases, such as Springer Link, Web of Science, ACM Digital Library, IEEE Digital Library (Xplore) and Scopus, can be found in Table 1. In the selection process, each of these points was present. analyzed according to the guidelines that must be met in BI design. In the next step, we examined the tasks involved in data mining applications. This filter is used because the BI platform integrates data mining algorithms that generate insights from the analyzed data. These articles go through the editing stage and, finally, they are compiled as the working literature of the subject. Jobs that do not meet the criteria specified in the selection are automatically removed from the system.

Works are classified according to type, contribution and scope of research. The articles are classified according to the type of research based on the procedures proposed in [11] and [13], prioritizing articles where the proposed solution to the problem is new or a significant extension of existing techniques. Obtaining the results of the review began with the location of the main subjects, then moved on to the data extraction and, finally, the classification and the resulting scheme.

Top 5 Business Intelligence (bi) Tools In The Market

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