Sql 2012 Business Intelligence Advancement Workshop

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Sql 2012 Business Intelligence Advancement Workshop – Training PT Expertindo as one of the best training and consulting companies in Indonesia on 30 and 31 August 2023 conducted training in Malang, East Java. The training entitled Querying SQL Server 2012 was conducted by participants from the Department of Reporting and Compliance (DPKL) Bank Indonesia LLD Report Management and Monitoring Division. A two-day training session now.

SQL Server is a relational database management system (RDBMS) designed for applications with a client/server environment. The terms client, server, and client/server can be used to refer to general concepts or specific items of hardware or software. At a general level, a client is any system component that requests services or resources from other system components. Meanwhile, a server is any system component that provides services or resources to other system components. This course is the foundation for all disciplines related to DQL Server, namely Database Administration, Database Development and Business Intelligence. In this training participants will learn the technology how to write basic Transact-SQL queries for Microsoft SQL Server 2012. This training also helps participants prepare to take the 70-461 exam.

Sql 2012 Business Intelligence Advancement Workshop

For other training schedules, apart from the SQL Server 2012 Query Training above, Bank Indonesia is also holding In Home Training, the news of which can be found in the following link => In Home Training.

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Measuring Intelligent Service Quality and Its Impact on Golf Course Performance According to Types of Applications and User Profile

Open Access Policy Access to the Open Institution System Important Issues Guidelines Research Review and Ethical Publications Section Making Fees Awards Certificates

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Pdf) Business Intelligence In Education: An Application Of Pentaho Software

These papers represent the most advanced research with significant potential for high impact in the field. The Feature Paper should be a significant original Article that involves multiple methods or approaches, provides a perspective for future research directions and describes possible research applications.

Feature papers are submitted upon individual invitation or recommendation by scientific editors and must receive positive feedback from reviewers.

Editor’s Choice articles are based on recommendations by science editors of journals from around the world. The editors select a small number of recently published articles in the journal that they believe will be of particular interest to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the journal’s various research areas.

By William Villegas-Ch William Villegas-Ch Scilit Preprints.org Google Scholar 1, * , Xavier Palacios-Pacheco Xavier Palacios-Pacheco Scilit Preprints.org Google Scholar 2 and Sergio Luján-Mora Sergio Luján-Mora Scilit Preprints.org Google Scholar

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

Currently, universities are forced to change the principles of education, where knowledge is based on the experience of the teacher. This change includes the development of quality education focused on student learning. These factors have forced universities to find a solution that allows them to extract data from different information systems and transform them into necessary knowledge to make decisions that improve learning outcomes. Information systems managed by universities store a large amount of data on the economic and educational variables of students. In the academic field, this data is generally not used to generate knowledge about their students, unlike in the business field, where data is analyzed intensively in business intelligence to gain competitive advantage. These success stories in the business field can be replicated by universities through the analysis of educational data. This paper presents an approach that integrates models and techniques of data mining within the architecture of business intelligence to make decisions about variables that can influence the development of learning. In order to test the proposed method, we developed a case study, in which students were identified and distributed according to the data they produced in different information systems of a university.

Currently, the use of information and communication technologies (ICTs) is present in all social activities. Universities are not far behind, and include ICTs in most of their processes. These principles integrate the management on which the existence of universities depends or use them as a support for educational management [1]. The most widespread use of ICTs for learning management is the learning management system (LMS) [2] that supports online interaction between teachers and students. However, there are scenarios in which specific support through ICTs is needed to solve common problems based on learning. These scenarios allow ICTs to use new models and educational methods in student learning. A guide to this can be the personalization of companies that have succeeded with their customers through data analysis models that allow managers, executives and analysts to discover trends and improve their services and products. for their customers.

Personal work can be introduced to educational environments where the process is similar to that used at the business level, but the goal in education is to improve methods or activities that generate learning in the student [3]. Learning environments are primarily based on many interactive and delivery services. Personalized learning recommendation programs can provide learning recommendations to students based on their needs [4, 5]. Companies use data analysis graphs whose results help them make decisions about their business. These towers are called business intelligence (BI); their ability to extract data from different sources, process them and turn them into knowledge is a solution that can also be found in the educational management of a university [6].

Business Intelligence, Analytics, And Data Science A Managerial Perspective

As a preliminary, it is important to consider that many universities use the BI platform with a management or operational focus, which helps them make decisions in the financial management of the institution [7]. In the same way, previous works [8, 9] have analyzed desertion rates models and statistical tools with the use of economic and educational variables, dividing the analysis into whether students are enrolled or not in the next semester. This formula is very useful; however, it gives reasons that determine why students drop out. In contrast, our proposal is distinguished by its ability to analyze the data of the students’ courses and focus on the academic problems they present. This analysis helps to make decisions in educational management and the improvement of training methods established by teachers [10].

In this work, three research questions are proposed that help to justify the concepts and processes in their design; in addition, they seek to establish the current status of the area where this work is done:

To answer each of these questions, this work includes a description of a BI process that bases its design on a detailed review of previous works, a Unified Modeling Diagram (UML) and a complete method for using educational data mining. This service extracts data from different educational sources, processes them and allows us to identify, through data mining algorithms, the strengths and weaknesses of each student. Once the results are obtained, knowledge is generated about the learning process of each student, allowing appropriate decisions to improve the way the student learns.

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

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The literature review presented follows the guidelines published in the process for systematic literature review proposed by Kitchenham et al. [11] and by Petersen et al. [12]. Kitchenham et al. describe how the results of a literature review in software engineering should be planned, conducted and presented; Petersen et al. provide guidance on how to conduct a rigorous review of the literature and follow a process. For our literature review, we grouped the works according to the type of material, model, design or discussion they adopted in their own analysis of educational data. For this type of classification, it is necessary to know the state of scientific work in learning areas that include the use of BI techniques that enhance education. The purpose of this literature review is to try to learn how they do it, and the methods and techniques they use. Search string “ business intelligence AND

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