Analysis on Ship Equipment Consumption Data Based on Data Mining

Consumption Data ◽

Ship Maintenance ◽

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.

Online Analytical Processing (OLAP): A Fast and Effective Data Mining Tool for Gene Expression Databases

Journal of Biomedicine and Biotechnology ◽

10.1155/jbb.2005.181 ◽

2005 ◽

Vol 2005 (2) ◽

pp. 181-188 ◽

Cited By ~ 19

Author(s):

Nadim W. Alkharouf ◽

D. Curtis Jamison ◽

Benjamin F. Matthews

Keyword(s):

Gene Expression ◽

Data Mining ◽

Cluster Analysis ◽

Soybean Cyst Nematode ◽

Current Data ◽

Database Management System ◽

Biological Knowledge ◽

Microarray Database ◽

Gene expression databases contain a wealth of information, but current data mining tools are limited in their speed and effectiveness in extracting meaningful biological knowledge from them. Online analytical processing (OLAP) can be used as a supplement to cluster analysis for fast and effective data mining of gene expression databases. We used Analysis Services 2000, a product that ships with SQLServer2000, to construct an OLAP cube that was used to mine a time series experiment designed to identify genes associated with resistance of soybean to the soybean cyst nematode, a devastating pest of soybean. The data for these experiments is stored in the soybean genomics and microarray database (SGMD). A number of candidate resistance genes and pathways were found. Compared to traditional cluster analysis of gene expression data, OLAP was more effective and faster in finding biologically meaningful information. OLAP is available from a number of vendors and can work with any relational database management system through OLE DB.

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 ◽

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.

Data Mining in Franchise Organizations

Data Warehousing and Mining ◽

10.4018/978-1-59904-951-9.ch169 ◽

2008 ◽

pp. 2722-2733 ◽

Cited By ~ 18

Author(s):

Ye-Sho Chen ◽

Robert Justis ◽

P. Pete Chong

Keyword(s):

Data Mining ◽

Growth Strategy ◽

Pertinent Data ◽

Analytical Processing ◽

Customer Services ◽

Internal Operations ◽

Comprehensive Framework ◽

Franchise Organizations

Franchising has been used by businesses as a growth strategy. Based on the authors’ cumulative research and experience in the industry, this paper describes a comprehensive framework that describes both the franchise environment — from customer services to internal operations — and the pertinent data items in the system. The authors identify the most important aspects of a franchising business, the role of online analytical processing (OLAP) and data mining play and the data items that data mining should focus on to ensure its success.

Online Data Mining

Data Warehousing and Mining ◽

10.4018/978-1-59904-951-9.ch007 ◽

2008 ◽

pp. 75-83

Author(s):

He´ctor Oscar Nigro ◽

Sandra Elizabeth González Císaro

Keyword(s):

Data Mining ◽

Data Analysis ◽

Intelligent Data Analysis ◽

Online Data ◽

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).

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 ◽

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.

Online Data Mining

Encyclopedia of Database Technologies and Applications ◽

10.4018/978-1-59140-560-3.ch072 ◽

2005 ◽

pp. 427-432

Author(s):

Héctor Oscar Nigro ◽

Sandra Elizabeth González Císaro

Keyword(s):

Data Mining ◽

Data Analysis ◽

Intelligent Data Analysis ◽

Online Data ◽

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 ◽

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 ◽

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.

An Intelligent Support System Integrating Data Mining and Online Analytical Processing

Organizational Data Mining ◽

10.4018/978-1-59140-134-6.ch010 ◽

2011 ◽

pp. 141-156

Author(s):

Rahul Singh ◽

Richard T. Redmond ◽

Victoria Yoon

Keyword(s):

Data Mining ◽

Decision Making ◽

Decision Support ◽

Decision Makers ◽

Intelligent Decision Making ◽

Course Of Action ◽

Intelligent Decision ◽

Analytical Processing ◽

Intelligent Decision Support

Intelligent decision support requires flexible, knowledge-driven analysis of data to solve complex decision problems faced by contemporary decision makers. Recently, online analytical processing (OLAP) and data mining have received much attention from researchers and practitioner alike, as components of an intelligent decision support environment. Little that has been done in developing models to integrate the capabilities of data mining and online analytical processing to provide a systematic model for intelligent decision making that allows users to examine multiple views of the data that are generated using knowledge about the environment and the decision problem domain. This paper presents an integrated model in which data mining and online analytical processing complement each other to support intelligent decision making for data rich environments. The integrated approach models system behaviors that are of interest to decision makers; predicts the occurrence of such behaviors; provides support to explain the occurrence of such behaviors and supports decision making to identify a course of action to manage these behaviors.

Strategic Utilization of Data Mining

Encyclopedia of Information Science and Technology, First Edition ◽

10.4018/978-1-59140-553-5.ch468 ◽

2005 ◽

pp. 2638-2642

Author(s):

Chandra S. Amaravadi

Keyword(s):

Artificial Intelligence ◽

Data Mining ◽

Decision Support ◽

Information Systems ◽

Relational Databases ◽

Artificial Intelligence Techniques ◽

The Past ◽

In the past decade, a new and exciting technology has unfolded on the shores of the information systems area. Based on a combination of statistical and artificial intelligence techniques, data mining has emerged from relational databases and Online Analytical Processing as a powerful tool for organizational decision support (Shim et al., 2002).