scholarly journals The Development of Data Warehouse from Payment Point Services of IT Business Solution Provider

2017 ◽  
Vol 8 (4) ◽  
pp. 191
Author(s):  
Kornelius Irfandhi
Keyword(s):  
Data Analysis ◽  
Data Warehouse ◽  
Design Methodology ◽  
Online Analytical Processing ◽  
Business Analytics ◽  
Chief Technology Officer ◽  
Analytical Processing

The goals of the research is to develop a data warehouse loaded from operational database of payment point service in IT business solution provider. The development scope is data analysis using Online Analytical Processing (OLAP) tools for identifying the trends of transaction and agent registration, and create reports and dashboards. The data warehouse was developed with Kimball method as known as the nine-step design methodology. The data and requirement were collected by observation and interview with Chief Technology Officer (CTO). The data warehouse was analyzed by OLAP tool provided by Pentaho Business Analytics software with additional plugin Pivot4J. The obtained results are the trends of transactionand agent registration between 2014 and 2015. It can be concluded that developed data warehouse can be used as ananalysis tool to know the trends information.

scholarly journals ARRANGEMENT AND MODULATION OF ETL PROCESS IN THE STORAGE

2020 ◽  
pp. 3-8
Author(s):  
Jala Aghazada
Keyword(s):  
Data Analysis ◽  
Data Processing ◽  
Data Warehouse ◽  
Analytical Data ◽  
Online Analytical Processing ◽  
Processing Data ◽  
Analytical Processing ◽  
Different Sources ◽  
Operational Data ◽  
Important Significance

Data warehouse (DW) is the basis of systems for operational data analysis (OLAP-Online Analytical Processing). Data extracted from different sources transforms and load in DW. Proper organization of this process, which is called ETL (Extract, Transform, Load) has important significance in creation of DW and analytical data processing. Forms of organization, methods of realization and modeling of ETL processes are considered in this paper.

scholarly journals Perancangan Data Warehouse Rumah Sakit Berbasis Online Analytical Processing (OLAP)

2021 ◽  
Vol 8 (5) ◽  
pp. 1077
Author(s):  
Joko Purwanto ◽  
Renny Renny
Keyword(s):  
Information Technology ◽  
Health Services ◽  
Data Warehouse ◽  
Data Extraction ◽  
Online Analytical Processing ◽  
Data Source ◽  
Analytical Processing ◽  
Quality Of Health Services ◽  
Warehouse Design

<p class="BodyCxSpFirst">Pemanfaatan teknologi informasi sangat penting bagi rumah sakit, karena berpengaruh pula terhadap kualitas pelayanan kesehatan yang secara manual diubah menjadi digital dengan menggunakan teknologi informasi.Dalam penelitian ini penulis menggunakan metodologi <em>Nine step</em> sebagai acuan dalam merancang suatu <em>data warehouse</em><em>,</em> untuk pemodelan menggunakan skema konstelasi fakta dengan 3 tabel fakta dan 11 tabel dimensi. Perbedaan penelitian ini dengan penelitian sebelumnya terletak pada sumber data yang diekstrak langsung dari <em>database</em> SIMRS yang digunakan rumah sakit, sehingga tidak ada ekstraksi data secara manual.Penelitian ini bertujuan untuk menghasilkan desain data warehouse berbasis Online Analytical Processing (OLAP) sebagai sarana penunjang kualitas pelayanan kesehatan rumah sakit. OLAP yang dihasilkan akan berupa desain data warehouse dengan berbagai dimensi yang akan menghasilkan tampilan informasi berupa Chart maupun Grafik sehingga informasinya mudah dibaca dan dipahami oleh berbagai pihak.</p><p class="BodyCxSpFirst"> </p><p class="BodyCxSpFirst"><em><strong>Abtract</strong></em></p><p class="BodyCxSpFirst"><em>The use of information technology is very important for hospitals, because it also affects the quality of health services, which manualy changed to digital using information technology. In this study, the authors used the Nine step methodology as a reference in designing a data warehouse for modeling using a fact constellation schema with 3 fact tables and 11 dimension tables. the different in this study from previous research is that the data source was taken directly from the SIMRS database used by the hospital, so there is no manual data extraction.</em><em>The aim of this research is to be able to produce a Data Warehouse design based on Online Analytical Processing (OLAP) as a means of supporting the quality of hospital health services. The resulting OLAP will be a data warehouse design with various dimensions will produce the displays information in the form of a graph or chart so that the information is easy to read and understand by various parties.</em></p><p class="BodyCxSpLast"><em> </em></p><p class="BodyCxSpFirst"><em><strong><br /></strong></em></p>

Online Data Mining

2008 ◽  
pp. 75-83
Author(s):  
He´ctor Oscar Nigro ◽  
Sandra Elizabeth González Císaro
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Online Analytical Processing ◽  
Intelligent Data Analysis ◽  
Online Data ◽  
Analytical Processing

Several approaches for intelligent data analysis are not only available but also tried and tested. Online analytical processing (OLAP) and data mining represent two of the most important approaches. They mainly emphasize different aspects of the data and allow deriving of different kinds of information. So far, these approaches have mainly been used in isolation (Schwarz, 2002).

Schema Evolution Models and Languages for Multidimensional Data Warehouses

2009 ◽  
pp. 119-128
Author(s):  
Edgard Benítez-Guerrero ◽  
Ericka-Janet Rechy-Ramírez
Keyword(s):  
Data Analysis ◽  
Data Warehouse ◽  
Historical Data ◽  
Multidimensional Systems ◽  
Multidimensional Data ◽  
Schema Evolution ◽  
Time Dimension ◽  
On Line ◽  
Analytical Processing ◽  
Factual Data

A Data Warehouse (DW) is a collection of historical data, built by gathering and integrating data from several sources, which supports decisionmaking processes (Inmon, 1992). On-Line Analytical Processing (OLAP) applications provide users with a multidimensional view of the DW and the tools to manipulate it (Codd, 1993). In this view, a DW is seen as a set of dimensions and cubes (Torlone, 2003). A dimension represents a business perspective under which data analysis is performed and organized in a hierarchy of levels that correspond to different ways to group its elements (e.g., the Time dimension is organized as a hierarchy involving days at the lower level and months and years at higher levels). A cube represents factual data on which the analysis is focused and associates measures (e.g., in a store chain, a measure is the quantity of products sold) with coordinates defined over a set of dimension levels (e.g., product, store, and day of sale). Interrogation is then aimed at aggregating measures at various levels. DWs are often implemented using multidimensional or relational DBMSs. Multidimensional systems directly support the multidimensional data model, while a relational implementation typically employs star schemas(or variations thereof), where a fact table containing the measures references a set of dimension tables.

Online Data Mining

2005 ◽  
pp. 427-432
Author(s):  
Héctor Oscar Nigro ◽  
Sandra Elizabeth González Císaro
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Online Analytical Processing ◽  
Intelligent Data Analysis ◽  
Online Data ◽  
Analytical Processing

Several approaches for intelligent data analysis are not only available but also tried and tested. Online analytical processing (OLAP) and data mining represent two of the most important approaches. They mainly emphasize different aspects of the data and allow deriving of different kinds of information. So far, these approaches have mainly been used in isolation (Schwarz, 2002).

scholarly journals An online analytical processing multi-dimensional data warehouse for malaria data

Database ◽  
2017 ◽  
Vol 2017 ◽  
Author(s):  
S M Niaz Arifin ◽  
Gregory R Madey ◽  
Alexander Vyushkov ◽  
Benoit Raybaud ◽  
Thomas R Burkot ◽  
...  
Keyword(s):  
Data Warehouse ◽  
Online Analytical Processing ◽  
Analytical Processing

Analysis on Ship Equipment Consumption Data Based on Data Mining

2013 ◽  
Vol 846-847 ◽  
pp. 1141-1144
Author(s):  
Dan Dan Chen ◽  
Zhi Gang Yao
Keyword(s):  
Data Mining ◽  
Cluster Analysis ◽  
Regression Analysis ◽  
Data Warehouse ◽  
Comprehensive Analysis ◽  
Online Analytical Processing ◽  
Consumption Data ◽  
Ship Maintenance ◽  
Analytical Processing

A comprehensive analysis on a large amount of ship equipment consumption data accumulated over the years is achieved through the establishment of data warehouse, online analytical processing, regression analysis, cluster analysis, etc. by means of data mining. The analysis results present important references for equipment guarantee department in terms of equipment preparation and carrying, etc. and provide the comprehensive analysis and utilization on massive ship maintenance support data with technical means.

scholarly journals Pengembangan Data Warehouse dan Online Analytical Processing (OLAP) untuk Data Artikel pada Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK)

2020 ◽  
Vol 1 (1) ◽  
Author(s):  
Bella Krisanda Easterita ◽  
Issa Arwani ◽  
Dian Eka Ratnawati
Keyword(s):  
Data Warehouse ◽  
Online Analytical Processing ◽  
Analytical Processing

Saat ini, Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK) telah terakreditasi dengan peringkat 2. Dengan JTIIK yang telah terakreditasi, maka peminat peneliti untuk mengirimkan artikel ke JTIIK semakin tinggi. Namun, proses untuk menerbitkan suatu artikel memerlukan waktu lebih dari satu tahun. Agar proses penerbitan tidak memerlukan waktu yang terlalu lama, mulai pada tahun 2018 JTIIK menerbitkan jurnal sebanyak enam kali dimana penerbitan mengalami peningkatan dari tahun sebelumnya. Dari peningkatan penerbitan ini, data yang disimpan juga akan semakin bertambah. Sebuah sistem diperlukan untuk mengelola data yang besar dari beberapa sumber dan melakukan analisis yang dapat menjadi bahan pertimbangan dalam pengambilan keputusan. Pada penelitian ini dilakukan pengembangan data warehouse dan Online Analytical Processing (OLAP) sebagai langkah penyelesaian untuk masalah dalam mengelola dan menganalisis banyaknya data dari beberapa sumber. Dalam penelitian ini dilakukan analisis sebagai tahapan pertama. Berdasarkan hasil analisis didapatkan 2 information package yaitu information package data artikel dan information package data penulis. Penelitian dilanjutkan dengan perancangan data warehouse dengan menggunakan snowflake schema yang menghasilkan 2 tabel fakta dan 4 tabel dimensi. Selanjutnya dilakukan implementasi ETL dan OLAP. Kemudian dilakukan 2 jenis pengujian. Hasil pengujian pertama yaitu validasi kebutuhan menunjukkan bahwa data warehouse yang dibangun telah sesuai dengan kebutuhan yang dirancang. Hasil pengujian kedua yaitu performansi proses ETL dari segi waktu menunjukkan bahwa hasil rata-rata waktu yang dibutuhkan untuk eksekusi proses ETL adalah 14,5 detik.

scholarly journals ANALISIS DATA KEJADIAN BENCANA ANGIN PUTING BELIUNG DENGAN METODE ONLINE ANALYTICAL PROCESSING (OLAP)

2019 ◽  
Vol 2 (1) ◽  
pp. 18-23 ◽  
Author(s):  
Ridho Darman
Keyword(s):  
Decision Making ◽  
Data Analysis ◽  
Natural Disaster ◽  
Historical Data ◽  
Study Data ◽  
Online Analytical Processing ◽  
Graphical Form ◽  
High Incidence ◽  
Analytical Processing

A whirlwind is a natural disaster with a relatively high incidence. In improving whirlwinddisaster mitigation preparedness, analysis of historical  data of events is needed to minimize the possibility of losses. In this study, data analysis was carried out using the Online Analytical Processing (OLAP) method with the Zoho Reports application so that it can be known to the region prone to whirlwind and the time of occurrence to help those who have an importance in decision making. The results of the analysis are in the form of information displayed in graphical form from data on the occurrence of whirlwind in Indonesia in 2011-2014.

Data Warehouse Development

2005 ◽  
pp. 729-733
Author(s):  
José María Cavero Barca ◽  
Esperanza Marcos Martinez ◽  
Mario G. Piattini ◽  
Adolfo Sánchez de Miguel
Keyword(s):  
Decision Support ◽  
Data Warehouse ◽  
Processing Technology ◽  
Online Analytical Processing ◽  
Data Synthesis ◽  
Analytical Processing

The concept of data warehouse first appeared in Inmon (1993) to describe a “subject oriented, integrated, non-volatile, and time variant collection of data in support of management’s decisions” (31). It is a concept related to the OLAP (online analytical processing) technology, first introduced by Codd et al. (1993) to characterize the requirements of aggregation, consolidation, view production, formulae application, and data synthesis in many dimensions. A data warehouse is a repository of information that mainly comes from online transactional processing (OLTP) systems that provide data for analytical processing and decision support.

Sign in / Sign up

Export Citation Format

Share Document