Logo

Data Warehouse with Big Data Technology for Higher Education

Santoso, Leo Willyanto and Yulia, (2017) Data Warehouse with Big Data Technology for Higher Education. [UNSPECIFIED]

[img]
Preview
PDF (Cek Plagiat)
Download (2043Kb) | Preview
    [img]
    Preview
    PDF (Sertifikat)
    Download (379Kb) | Preview
      [img]
      Preview
      PDF (peerreview)
      Download (1353Kb) | Preview
        [img]
        Preview
        PDF
        Download (703Kb) | Preview
          [img]
          Preview
          PDF (korespondensi)
          Download (1024Kb) | Preview

            Abstract

            Nowadays, data warehouse tools and technologies cannot handle the load and analytic process of data into meaningful information for top management. Big data technology should be implemented to extend the existing data warehouse solutions. Universities already collect vast amounts of data so the academic data of university has been growing significantly and become a big academic data. These datasets are rich and growing. University’s top-level management needs tools to produce information from the records. The generated information is expected to support the decision-making process of top-level management. This paper explores how big data technology could be implemented with data warehouse to support decision making process. In this framework, we propose Hadoop as big data analytic tools to be implemented for data ingestion/staging. The paper concludes by outlining future directions relating to the development and implementation of an institutional project on Big Data.

            Item Type: UNSPECIFIED
            Uncontrolled Keywords: Data Warehouse, Big Data, Academic, Hadoop, Higher Education, Analysis
            Subjects: Q Science > QA Mathematics > QA76 Computer software
            Divisions: Faculty of Industrial Technology > Informatics Engineering Department
            Depositing User: Admin
            Date Deposited: 18 Jan 2018 22:19
            Last Modified: 30 Sep 2022 11:42
            URI: https://repository.petra.ac.id/id/eprint/17766

            Actions (login required)

            View Item