scholarly journals DATA WAREHOUSING APPLICATIONS: AN ANALYTICAL TOOL FOR DECISION SUPPORT SYSTEM

2014 ◽  
pp. 294-299
Author(s):  
MOHAMMED SHAFEEQ AHMED
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Data Storage ◽  
Data Warehousing ◽  
Transaction Processing ◽  
Multidimensional Data ◽  
Data Warehouses ◽  
On Line ◽  
Analytical Processing ◽  
On Line Analytical Processing

Data-driven decision support systems, such as data warehouses can serve the requirement of extraction of information from more than one subject area. Data warehouses standardize the data across the organization so as to have a single view of information. Data warehouses (DW) can provide the information required by the decision makers. The data warehouse supports an on-line analytical processing (OLAP), the functional and performance requirements of which are quite different from those of the on-line transaction processing (OLTP) applications traditionally supported by the operational databases. Data warehouses provide on-line analytical processing (OLAP) tools for the interactive analysis of multidimensional data of varied granularities, which facilitates effective data mining. Data warehousing and OLAP have emerged as leading technologies that facilitate data storage, organization and then, significant retrieval. Both are essential elements of decision support, which has increasingly become a focus of the database industry. This paper provides a detailed picture of Data warehousing (DW), exploring the features of it, applications and the architecture of DW over Data Mining, Online Analytical Processing (OLAP), On-line Transaction Processing (OLTP) technologies.

scholarly journals Data Warehouse Benchmarking with DWEB

2011 ◽  
pp. 302-323
Author(s):  
Jérôme Darmont
Keyword(s):  
Performance Evaluation ◽  
Decision Support ◽  
Data Warehouse ◽  
Database Management ◽  
Ad Hoc ◽  
Synthetic Data ◽  
Transaction Processing ◽  
Data Warehouses ◽  
On Line ◽  
Analytical Processing

Performance evaluation is a key issue for designers and users of Database Management Systems (DBMSs). Performance is generally assessed with software benchmarks that help, for example test architectural choices, compare different technologies, or tune a system. In the particular context of data warehousing and On-Line Analytical Processing (OLAP), although the Transaction Processing Performance Council (TPC) aims at issuing standard decision-support benchmarks, few benchmarks do actually exist. We present in this chapter the Data Warehouse Engineering Benchmark (DWEB), which allows generating various ad-hoc synthetic data warehouses and workloads. DWEB is fully parameterized to fulfill various data warehouse design needs. However, two levels of parameterization keep it relatively easy to tune. We also expand on our previous work on DWEB by presenting its new Extract, Transform, and Load (ETL) feature, as well as its new execution protocol. A Java implementation of DWEB is freely available online, which can be interfaced with most existing relational DMBSs. To the best of our knowledge, DWEB is the only easily available, up-to-date benchmark for data warehouses.

scholarly journals Data Warehouse Schemas using Multidimensional Data Model for Retail

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.

Abstracting Data With A Decision Support System Based on Data Warehouses for the Evaluation of Ship Form Alternatives

2006 ◽  
Vol 22 (04) ◽  
pp. 248-252
Author(s):  
Song Sang ◽  
Hua-jun Li
Keyword(s):  
Data Mining ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Data Abstraction ◽  
Data Warehouses ◽  
Development Environment ◽  
On Line ◽  
Line Analysis ◽  
Theory Framework

A new aided decision support system (DSS) based on data warehouses is discussed. It is composed of data warehouses, on-line analysis processing, and data mining, which is new in the field of DSS. The essential principle, the setting up of the model, the development environment, the main system interface, and a sketch of the theory framework of the new DSS architecture are also described. The decision support system was applied to data abstraction for evaluating ship form scenarios. Tests have shown it to be practical and dependable in complex systems, such as in the demonstration of ship forms.

scholarly journals INTEGRASI DATA MINING DAN ONLINE ANALYTICAL PROCESSING (OLAP) PADA DATA PERFORMA SISWA

2020 ◽  
Vol 9 (3) ◽  
pp. 400-406
Author(s):  
Lydia Liliana ◽  
Henny Hartono ◽  
Devi Yurisca Bernanda
Keyword(s):  
Data Mining ◽  
Online Analytical Processing ◽  
On Line ◽  
Analytical Processing ◽  
On Line Analytical Processing

Pertumbuhan teknologi membawa dampak terhadap peningkatan data untuk digunakan bagi setiap orang. Akumulasi data tersebut telah menciptakan pola data yang semakin banyak, namun perolehan informasi dari data tersebut masih minim. Oleh karena itu, saat ini diperlukan suatu teknik analisa data dalam mencari pola dari kumpulan data tersebut, salah satunya adalah data mining. Data mining merupakan proses pencarian informasi baru dari kumpulan data yang besar untuk menemukan informasi baru sebagai bahan pertimbangan dalam pengambilan keputusan di berbagai bidang, seperti bidang pendidikan. Dalam bidang pendidikan, banyak menghasilkan berbagai macam data, seperti data performa siswa dalam persiapan mengikuti ujian. Data tersebut dapat dianalisis dengan menggunakan metode On-Line Analytical Processing (OLAP) untuk menemukan pola dari data performa siswa tersebut. Penelitian ini berfokus pada proses integrasi data mining yang terdiri dari association, clustering, classification dan forecasting dengan kombinasi metode On-Line Analytical Processing (OLAP) pada data performa siswa. Penulis juga menggunakan bantuan tools Power OLAP untuk membantu analisa metode data mining. Hasil dari penelitian ini adalah penemuan pola baru dalam proses identifikasi kelompok data tersebut, seperti informasi mengenai rata-rata hasil ujian siswa berdasarkan persiapan ujian yang dilakukan dalam bentuk grafik sebagai alat pemodelan dari data, sehingga pengetahuan baru tersebut dapat membantu pihak universitas/sekolah untuk melalukan klasifikasi mengenai tingkat kelulusan dan dapat menetukan strategi dalam meningkatkan kelulusan siswa pada tahun - tahun berikutnya.

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