Datenanalyse: On-Line Analytical Processing und Data Mining

2007 ◽  
pp. 143-204
Keyword(s):  
Data Mining ◽  
On Line ◽  
Analytical Processing ◽  
On Line Analytical Processing

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.

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.

Objective and Subjective Aspects of Decision-Making Support at the Mesoeconomic Level

2016 ◽  
pp. 183-215
Author(s):  
Anastasia Y. Nikitaeva
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Cognitive Mapping ◽  
Objective Data ◽  
Management Effectiveness ◽  
Cognitive Approach ◽  
Multi Attribute Utility Theory ◽  
On Line ◽  
Analytical Processing

This chapter substantiates the importance of improving management effectiveness of mesoeconomic systems in current economic conditions and the features of mesoeconomy as a management object which defines the high complexity of decision making at the meso level. There are approaches, methods, and technologies which provide support of the decision making process via the integration of formal methods for objective data analysis and methods of accounting to solve semi-structured complex problems of mesoeconomy. A cognitive approach, and an approach involving the integration of the On-Line Analytical Processing and Data mining technologies with methods of a multi-criteria assessment of alternative, in particular methods of Multi-Attribute Utility Theory are considered in the chapter. Cognitive mapping of interaction between state and business in a mesoeconomic system are included as a case-study.

On-Line Analytical Processing at Washtenaw Mortgage

2011 ◽  
pp. 196-216
Author(s):  
John H. Heinrichs ◽  
William J. Doll
Keyword(s):  
Information Systems ◽  
Market Analysis ◽  
Ease Of Use ◽  
Executive Information Systems ◽  
Analysis Application ◽  
On Line ◽  
Stage Process ◽  
Analytical Processing ◽  
On Line Analytical Processing ◽  
Strategic Market

In an ever-changing, competitive marketplace, executive information systems (EIS) promise the ability to simultaneously assess factors in both the internal and external environment, enabling a timely competitive response. EIS are enjoying a renaissance due to the recent emergence of on-line analytical processing (OLAP) capabilities. OLAPs power, flexibility and ease of use supports mental model (knowledge) creation better than traditional executive information systems. This case study allows you to examine the usefulness and ease of use of OLAP technology for strategic market analysis at Washtenaw Mortgage Company, a firm in the mortgage wholesale industry. The key to improving competitive performance is not the technology, but rather, how the technology is utilized to focus managements analysis. Gaining strategic insights requires three ingredients people, process, and technology. A three-stage process used for implementing an OLAP strategic market analysis application is presented. OLAP technology marks an evolutionary improvement in EIS software. The potential of this technology, however, is not likely to be realized without a better understanding of the process for achieving management focus.

Sign in / Sign up

Export Citation Format

Share Document