Using Data Warehouse for Business Intelligence

2008 ◽  
pp. 411-440
Keyword(s):  
Data Warehouse ◽  
Business Intelligence ◽  
Using Data

scholarly journals Mapping Student Data Using Data Warehouse for Promotion at Vocational High School of Z

2020 ◽  
Vol 3 (2) ◽  
pp. 55
Author(s):  
Khoirudin Eko Nurcahyo ◽  
Sucipto Sucipto ◽  
Arie Nugroho
Keyword(s):  
High School ◽  
Data Warehouse ◽  
Junior High School ◽  
Business Intelligence ◽  
Junior High ◽  
Vocational High School ◽  
Dimensional Modeling ◽  
Education Organization ◽  
Using Data ◽  
Analyze Data

<em>The purpose of this study is provide data warehouse modeling which make executive of school can analyze data easily, the problem is executive of school are analysis list registrant list difficulty, what the most and least registrant junior high school come from and the major which most and least registrant. This study do is because how important data management on education organization and how the data can be managed better. The study use descriptive quantitative method research and use 4 step data warehouse dimensional modeling by Kimball. On building data warehouse used ETL, data be extracted and transformed into data warehouse as dimension and fact. For next data be imported and be showed by web base business intelligence app. The result of this study is an web base business intelligence app which can show sum of registrant on gender, majors, junior high school graduate come from, recommendation and register year. Data warehouse is good at data analyzing for decision making, because data warehouse can show information quickly and accurate.</em>

scholarly journals Rancang Bangun Real-Time Business Intelligence Untuk Subjek Kegiatan Akademik pada Universitas Menggunakan Change Data Capture

2014 ◽  
Vol 5 (2) ◽  
Author(s):  
Stephanie Pamela Adithama
Keyword(s):  
Real Time ◽  
Data Warehouse ◽  
Business Intelligence ◽  
Data Capture ◽  
Strategic Decision ◽  
Change Data Capture ◽  
Multiple Data ◽  
Academic Activities ◽  
Using Data ◽  
Unit Unit

Abstract. The running of academic activities in university continuously adds more data to the existing operational system. The data are not ready for the university strategic decision making, preparing reports for accreditation purposes and academic units. Real-time business intelligence application using data warehouse can become a solution for data analysis. The process of creating a data warehouse includes designing data warehouse, retrieving academic data from multiple data sources, extracting, transforming, loading (ETL) process, creating cube; and generating report. ETL processes are conducted by using a Pull Change Data Capture approach so that data changes during a certain period can be transferred in real-time. The higher the frequency of data change requests brings us closer to real-time and requires less time than loading all the data.Keywords: real-time, business intelligence, data warehouse, academic, change data capture Abstrak.  Kegiatan akademik di universitas berjalan terus menerus dan semakin menambah banyak data pada sistem operasional yang sudah ada. Data tersebut masih belum dapat dimanfaatkan oleh pihak universitas dalam pengambilan keputusan strategis, pembuatan laporan untuk keperluan akreditasi dan unit-unit akademik. Aplikasi real-time business intelligence menggunakan data warehouse menjadi solusi untuk analisa data. Proses pembuatan data warehouse meliputi perancangan data warehouse; pengambilan data akademik dari sumber data; proses extraction, transformation, loading (ETL); pembuatan cube; dan pembuatan laporan. Proses ETL dilakukan menggunakan pendekatan Change Data Capture Pull agar perubahan data selama periode tertentu dapat dipindahkan secara real-time. Semakin tinggi frekuensi permintaan perubahan data akan semakin mendekati real-time dan semakin membutuhkan waktu yang singkat dibandingkan dengan me-load semua data.Kata Kunci: real-time, business intelligence, data warehouse, akademik, change data capture

scholarly journals Konsolidasi Data Warehouse untuk Aplikasi Business Intelligence

2012 ◽  
Vol 3 (2) ◽  
pp. 841
Author(s):  
Rudy Rudy
Keyword(s):  
Data Warehouse ◽  
Business Intelligence ◽  
Information Needs ◽  
Information Access ◽  
Business Strategies ◽  
Corporate Leaders ◽  
Corporate Strategies ◽  
Single Presentation ◽  
Distribution Company ◽  
Using Data

As the business competition is getting strong, corporate leaders need complete data that as a basis for determining future business strategies. Similarly with management of company "A", a pharmaceutical company which has three distribution companies. Each distribution company already has a data warehouse to generate reports for each of them. For business operational and corporate strategies, chairman PT "A" requires an integrated report, so analysis of data owned by the three distribution companies can be done in a full report to answer the problems faced by the managemet. Thus, data warehouse consilidation can be used as a solution for company "A". Methodology starts with analysis of information needs to be displayed on the application of business intelligence, data warehouse consolidation, ETL (extract, transform and load), data warehousing, OLAP and Dashboard. Using data warehouse consolidation, information access by management of company "A" can be done in a single presentation, which can display data comparison between the three distribution companies.

Evolutionary Intelligent Data Warehousing Approach to Knowledge Discovery Systems: Dynamic Cubing

2019 ◽  
Vol 13 ◽  
Author(s):  
Harkiran Kaur ◽  
Kawaljeet Singh ◽  
Tejinder Kaur
Keyword(s):  
Knowledge Discovery ◽  
Data Warehouse ◽  
Business Intelligence ◽  
End Users ◽  
Processing Stage ◽  
Information Update ◽  
On Line ◽  
Analytical Processing ◽  
Warehouse Operations ◽  
Made In

Background: Numerous E – Migrants databases assist the migrants to locate their peers in various countries; hence contributing largely in communication of migrants, staying overseas. Presently, these traditional E – Migrants databases face the issues of non – scalability, difficult search mechanisms and burdensome information update routines. Furthermore, analysis of migrants’ profiles in these databases has remained unhandled till date and hence do not generate any knowledge. Objective: To design and develop an efficient and multidimensional knowledge discovery framework for E - Migrants databases. Method: In the proposed technique, results of complex calculations related to most probable On-Line Analytical Processing operations required by end users, are stored in the form of Decision Trees, at the pre- processing stage of data analysis. While browsing the Cube, these pre-computed results are called; thus offering Dynamic Cubing feature to end users at runtime. This data-tuning step reduces the query processing time and increases efficiency of required data warehouse operations. Results: Experiments conducted with Data Warehouse of around 1000 migrants’ profiles confirm the knowledge discovery power of this proposal. Using the proposed methodology, authors have designed a framework efficient enough to incorporate the amendments made in the E – Migrants Data Warehouse systems on regular intervals, which was totally missing in the traditional E – Migrants databases. Conclusion: The proposed methodology facilitate migrants to generate dynamic knowledge and visualize it in the form of dynamic cubes. Applying Business Intelligence mechanisms, blending it with tuned OLAP operations, the authors have managed to transform traditional datasets into intelligent migrants Data Warehouse.

scholarly journals “P3”: an adaptive modeling tool for post-COVID-19 restart of surgical services

JAMIA Open ◽  
2021 ◽  
Vol 4 (2) ◽  
Author(s):  
Divya Joshi ◽  
Ali Jalali ◽  
Todd Whipple ◽  
Mohamed Rehman ◽  
Luis M Ahumada
Keyword(s):  
Data Warehouse ◽  
Predictive Analytics ◽  
Surgical Patient ◽  
Protective Equipment ◽  
Operating Room Personnel ◽  
Surgical Services ◽  
Multiple Variable ◽  
Multiple Variables ◽  
Using Data ◽  
User Friendly

Abstract Objective To develop a predictive analytics tool that would help evaluate different scenarios and multiple variables for clearance of surgical patient backlog during the COVID-19 pandemic. Materials and Methods Using data from 27 866 cases (May 1 2018–May 1 2020) stored in the Johns Hopkins All Children’s data warehouse and inputs from 30 operations-based variables, we built mathematical models for (1) time to clear the case backlog (2), utilization of personal protective equipment (PPE), and (3) assessment of overtime needs. Results The tool enabled us to predict desired variables, including number of days to clear the patient backlog, PPE needed, staff/overtime needed, and cost for different backlog reduction scenarios. Conclusions Predictive analytics, machine learning, and multiple variable inputs coupled with nimble scenario-creation and a user-friendly visualization helped us to determine the most effective deployment of operating room personnel. Operating rooms worldwide can use this tool to overcome patient backlog safely.

Using Data Warehouse to Discover the Relation between TW-DRG and Pharmacology

2011 ◽  
Vol 474-476 ◽  
pp. 938-942
Author(s):  
Chih Sheng Chen ◽  
Guan Yu Chen ◽  
Jing Wun Hong ◽  
Ji Rou Jhang ◽  
Jia Yi Liou ◽  
...  
Keyword(s):  
Policy Analysis ◽  
Data Warehouse ◽  
Medication Cost ◽  
Pharmacodynamic Effects ◽  
Patients Rights ◽  
Medical Institutions ◽  
Pharmaceutical Cost ◽  
Using Data

This research explores the relation between TW-DRG and pharmacological information by using the concept of data warehouse as a basis. It is hoped to assist doctors, under the condition that patients’ rights will not be affected, to replace the high-priced pharmaceuticals with the pharmaceuticals which are low-priced yet with the same pharmacological and pharmacodynamic effects, in order to reduce the medication cost in medical institutions and hospitals. From this result, we learn that the differences among doctors’ medication habits can be offered to hospitals and doctors for policy analysis on medication. Also, doctors can make appropriate adjustments in medication acts and find out the replaceable pharmaceuticals so that the pharmaceutical cost can be lowered.

Using of data science in healthcare

2021 ◽  
pp. 149-156
Author(s):  
Ihor Ponomarenko ◽  
Oleksandra Lubkovska
Keyword(s):  
Machine Learning ◽  
Health Care ◽  
Business Intelligence ◽  
Data Science ◽  
Large Data ◽  
Science Methods ◽  
Medical Field ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Using Data

The subject of the research is the approach to the possibility of using data science methods in the field of health care for integrated data processing and analysis in order to optimize economic and specialized processes The purpose of writing this article is to address issues related to the specifics of the use of Data Science methods in the field of health care on the basis of comprehensive information obtained from various sources. Methodology. The research methodology is system-structural and comparative analyzes (to study the application of BI-systems in the process of working with large data sets); monograph (the study of various software solutions in the market of business intelligence); economic analysis (when assessing the possibility of using business intelligence systems to strengthen the competitive position of companies). The scientific novelty the main sources of data on key processes in the medical field. Examples of innovative methods of collecting information in the field of health care, which are becoming widespread in the context of digitalization, are presented. The main sources of data in the field of health care used in Data Science are revealed. The specifics of the application of machine learning methods in the field of health care in the conditions of increasing competition between market participants and increasing demand for relevant products from the population are presented. Conclusions. The intensification of the integration of Data Science in the medical field is due to the increase of digitized data (statistics, textual informa- tion, visualizations, etc.). Through the use of machine learning methods, doctors and other health professionals have new opportunities to improve the efficiency of the health care system as a whole. Key words: Data science, efficiency, information, machine learning, medicine, Python, healthcare.

scholarly journals University Accreditation using Data Warehouse

2017 ◽  
Vol 801 ◽  
pp. 012030 ◽  
Author(s):  
A S Sinaga ◽  
A S Girsang
Keyword(s):  
Data Warehouse ◽  
Using Data
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