Rancang Bangun Data Warehouse Untuk Pembuatan Laporan dan Analisis pada Data Kunjungan Pasien Rawat Jalan Rumah Sakit Universitas Airlangga Berbasis Online Analytical Processing (OLAP)

Abstrak— Rumah Sakit Universitas Airlangga (RSUA) merupakan sarana pelayanan kesehatan yang dikelola di bawah naungan Universitas Airlangga. Seiring berjalannya proses bisnis, jumlah pasien RSUA yang semakin bertambah menyebabkan data kunjungan pasien rawat jalan yang harus dikelola oleh bagian rekam medis semakin banyak. Data tersebut dikelola untuk digunakan dalam pembuatan laporan. Informasi dalam laporan dihasilkan melalui perhitungan secara manual atau menggunakan formula Microsoft Excel menjadi kendala dalam pembuatan laporan selain adanya kebutuhan laporan dengan format beragam dan analisis multidimensional. Data warehouse berbasis Online Analytical Processing (OLAP) dapat diterapkan untuk menangani masalah tersebut. Tujuan penelitian ini adalah merancang dan membangun data warehouse berbasis OLAP agar dapat digunakan oleh bagian rekam medis RSUA dalam pembuatan laporan. Data warehouse dibangun melalui tujuh tahap yaitu analisis, desain, proses ETL (Extraction, Transformation, and Loading), penerapan OLAP, uji coba, eksplorasi untuk hasil laporan dan analisis, serta evalusi. Perancangan data warehouse menggunakan Nine Step Methodology dengan pemodelan berupa fact constellation schema. Hasil implementasi data warehouse adalah aplikasi OLAP yang dapat digunakan untuk membantu kinerja bagian rekam medis RSUA dalam pembuatan laporan, baik berupa tabel pivot maupun grafik. Penilaian pengguna terhadap sistem data warehouse menunjukkan kategori baik dengan hasil penilaian sebesar 73.61 persen. Kata Kunci— Data Warehouse, Rawat Jalan, ETL, Nine Step Methodology, OLAPAbstract— Airlangga University Hospital is a health care facilities managed by the auspices of Airlangga University. Increasing number of patients in RSUA caused more outpatients’ visits data must be managed by the medical record unit. The data was used to report making. The information in the reports generated through manual calculation or used function of Microsoft Excel became a problem of report making in addition to their reporting needs with diverse formats and multidimensional analysis. Data warehouse based on Online Analytical Processing (OLAP) could implemented to solved the problem. The goal of this research were to designing and implementing the data warehouse based on OLAP so it could be used by medical record unit to making report. Data warehouse was implemented in seven process : analysis, design, ETL (Extraction, Transformation, and Loading), implementing OLAP, trial, explore the report and analysis, and evaluation. Design of data warehouse were using Nine Step Methodology and fact constellation schema model.The outcome of this research was an OLAP application that can used to help the task of RSUA medical record unit to making report using pivot table or chart. User ratings against the data warehouse system showed good category with the results of 73.61 percent in assessment. Keywords— Data Warehouse, Outpatient, ETL, Nine Step Methodology, OLAP

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

Jurnal Teknologi Informasi dan Ilmu Komputer ◽

10.25126/jtiik.2021854232 ◽

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

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 Nine step sebagai acuan dalam merancang suatu data warehouse, 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 database 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. AbtractThe 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.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.

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Data Warehouse and Business Intelligence Systems in the Context of E-HRM

Encyclopedia of Human Resources Information Systems ◽

10.4018/978-1-59904-883-3.ch034 ◽

2009 ◽

pp. 223-229

Author(s):

Martin Burgard ◽

Franca Piazza

Keyword(s):

Data Mining ◽

Information Technology ◽

Resource Management ◽

Human Resource Management ◽

Data Warehouse ◽

Human Resource ◽

Business Intelligence ◽

Multidimensional Data ◽

Business Intelligence Systems ◽

Analytical Processing

The increased use of information technology leads to the generation of huge amounts of data which have to be stored and analyzed by appropriate systems. Data warehouse systems allow the storage of these data in a special multidimensional data base. Based on a data warehouse, business intelligence systems provide different analysis methods such as online analytical processing (OLAP) and data mining to analyze these data. Although these systems are already widely used and the usage is still growing, their application in the area of electronic human resource management (e-HRM) is rather scarce. Therefore, the objective of this article is to depict the components and functionality of these systems and to illustrate the application possibilities and benefits of these systems by selected application examples in the context of e-HRM.

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Schema Evolution Models and Languages for Multidimensional Data Warehouses

Handbook of Research on Innovations in Database Technologies and Applications ◽

10.4018/978-1-60566-242-8.ch014 ◽

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.

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Data Warehouse Schemas using Multidimensional Data Model for Retail

International Journal of Engineering and Emerging Technology ◽

10.24843/ijeet.2017.v02.i01.p17 ◽

2017 ◽

Vol 2 (1) ◽

pp. 84

Author(s):

Kheri Arionadi Shobirin ◽

Adi Panca Saputra Iskandar ◽

Ida Bagus Alit Swamardika

Keyword(s):

Decision Making ◽

Real Time ◽

Data Warehouse ◽

Data Model ◽

Transaction Processing ◽

Multidimensional Data ◽

Complex Query ◽

On Line ◽

Analytical Processing ◽

Operational Data

A data warehouse are central repositories of integrated data from one or more disparate sources from operational data in On-Line Transaction Processing (OLTP) system to use in decision making strategy and business intelligent using On-Line Analytical Processing (OLAP) techniques. Data warehouses support OLAP applications by storing and maintaining data in multidimensional format. Multidimensional data models as an integral part of OLAP designed to solve complex query analysis in real time.

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

Database ◽

10.1093/database/bax073 ◽

2017 ◽

Vol 2017 ◽

Cited By ~ 1

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

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Analysis on Ship Equipment Consumption Data Based on Data Mining

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.846-847.1141 ◽

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.

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

Jurnal Sistem Informasi, Teknologi Informasi, dan Edukasi Sistem Informasi ◽

10.25126/justsi.v1i1.2 ◽

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.

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Data Warehouse Development

Encyclopedia of Information Science and Technology, First Edition ◽

10.4018/978-1-59140-553-5.ch127 ◽

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.

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PARALLEL PROCESSING OF OLAP QUERIES USING A CLUSTER OF WORKSTATIONS

International Journal of Information Technology & Decision Making ◽

10.1142/s0219622007002484 ◽

2007 ◽

Vol 06 (02) ◽

pp. 279-299 ◽

Cited By ~ 4

Author(s):

S. DEHURI ◽

R. MALL

Keyword(s):

Parallel Processing ◽

Parallel Algorithms ◽

Data Warehouse ◽

Performance Studies ◽

Response Times ◽

Online Analytical Processing ◽

Practical Solution ◽

Data Intensive ◽

Cluster Of Workstations ◽

Analytical Processing

Online analytical processing (OLAP) queries normally incur enormous processing overheads due to the huge size of data warehouses. This results in unacceptable response times. Parallel processing using a cluster of workstations has of late emerged as a practical solution to many compute and data intensive problems. In this article, we present parallel algorithms for some of the OLAP operators. We have implemented these parallel solutions for a data warehouse implemented on Oracle hosted in a cluster of workstations. Our performance studies show that encouraging speedups are achieved.

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