Study and application of data mining and data warehouse in CIMS

2003 ◽  
Author(s):  
Lijuan Zhou ◽  
Chi Liu ◽  
Daxin Liu
Keyword(s):  
Data Mining ◽  
Data Warehouse

scholarly journals A Pattern Storage System using Pattern Warehouse along with Sources of Pattern Generation and Applications

2019 ◽  
Vol 8 (10S) ◽  
pp. 357-362
Keyword(s):  
Data Mining ◽  
Data Warehouse ◽  
Storage System ◽  
Critical Evaluation ◽  
Large Data ◽  
Vital Role ◽  
Pattern Generation ◽  
Data Repository ◽  
Storage Unit ◽  
Data Mining Algorithms

Now a day different data mining algorithms are ready to create the specific set of data known as Pattern from a huge data repository, but there is no infrastructure or system to save it as persistent storage for the generated patterns. Pattern warehouse presents a foundation to make these patterns safe in the specific environment for long term use. Most organizations are excited to know the information or patterns rather than raw data or group of unprocessed data. Because extracted knowledge play a vital role to take right decision for the growth of an organization. We have examined the sources of patterns generated from large data sets. In this paper, we have presented little importance on the application area of pattern and idea of patter warehouse, the architecture of pattern warehouse then correlation between data warehouse and data mining, association between data mining and pattern warehouse, critical evaluation between existing approaches which theoretically published and more stress on association rule related review elements. In this paper, we analyze the patterns warehouse, data warehouse concerning various factors like storage space, type of storage unit, characteristics, and provide several research domains.

scholarly journals PEMANFAATAN DATA WAREHOUSE DI PERGURUAN TINGGI INDONESIA

2012 ◽  
Vol 4 (1) ◽  
pp. 47
Author(s):  
Iik Wilarso
Keyword(s):  
Data Mining ◽  
Data Warehouse

Paradigma Baru Pendidikan Tinggi [1,2] yang dicanangkan oleh Direktorat Jenderal Pendidikan Tinggi pada tahun 1995, mengubah pola manajemen institusi pendidikan tinggi di Indonesia, dimana manajemen institusi pendidikan tinggi harus senantiasa melakukan evaluasi diri secara berkesinambungan guna peningkatan kualitas institusi tersebut dalam melaksanakan misi Tridharma Pendidikan Tinggi. Untuk dapat melakukan evaluasi diri diperlukan berbagai data dan informasi, baik data internal maupun eksternal institusi. Dengan melihat kebutuhan berbagai macam data dan informasi yang diperlukan untuk penyusunan laporan evaluasi diri, seharusnya dapat mendorong pemanfaatan teknologi data warehouse maupun data mining pada institusi tersebut. Pada kenyataannya, belum ada satupun institusi pendidikan tinggi di Indonesia yang memanfaatkan teknologi data warehouse maupun data mining untuk penyusunan laporan evaluasi diri. Hal ini disebabkan karena hampir semua institusi pendidikan tinggi di Indonesia belum mempunyai Sistem Informasi yang tertata dengan baik dan digunakan untuk pengelolaan manajemen institusi pendidikan tinggi pada semua jenjang atau tingkatan manajemen (institusi, fakultas, jurusan maupun program studi).

scholarly journals Perancangan Analytical CRM untuk Mendukung Segmentasi Pelanggan di Institusi Pendidikan

2017 ◽  
Vol 11 (1) ◽  
pp. 79
Author(s):  
Valentinus Roby Hananto ◽  
Agus Dwi Churniawan ◽  
Ayouvi Poerna Wardhanie
Keyword(s):  
Data Mining ◽  
Data Warehouse ◽  
Customer Relationship

Persaingan untuk meraih pelanggan merupakan salah satu tantangan yang harus dihadapi oleh institusi pendidikan. Berbagai metode diterapkan sebagai upaya untuk meningkatkan daya saing, salah satunya yaitu menggunakan Customer Relationship Mangement (CRM) berupa database pelanggan yang berisi mengenai informasi pelanggan yang terperinci. Perkembangan teknologi manjemen data seperti data warehouse dan data mining menjadi pendukung penerapan CRM yang lebih sistematis dibandingkan sebelumnya. Dalam penelitian ini dijelaskan bagaimana rancangan penerapan analytical CRM untuk mendukung segmentasi pelanggan pada institusi pendidikan. Studi literatur dari berbagai penelitian dilakukan untuk mengetahui teknologi apa saja yang harus dipersiapkan oleh institusi pendidikan dalam penerapan analytical CRM. Selain itu, kami juga melakukan analisis kebutuhan untuk menyesuaikan sistem analytical CRM pada institusi pendidikan. Dengan rancangan terebut, penerapan analytical CRM dapat semakin banyak digunakan dalam mendukung segmentasi pelanggan pada institusi pendidikan.

Design and Research on Data Warehouse of Insurance Industry

2014 ◽  
Vol 496-500 ◽  
pp. 2108-2111
Author(s):  
Jian Hu Zhang ◽  
Lei Lei ◽  
Xin You Cui ◽  
Yong Wu ◽  
Lin Tao Li
Keyword(s):  
Data Mining ◽  
Data Warehouse ◽  
Domain Knowledge ◽  
Insurance Industry ◽  
Basic Flow ◽  
Logical Model ◽  
Mining Technology ◽  
Concept Model ◽  
Design And Implementation

Through in-depth understanding of the domain knowledge of insurance and the study of the technology of data warehouse, the paper illustrate the application of data mining technology and data warehouse technology in the insurance clients analysis, and from the basic flow of, discusse the application of data warehouse technology in the field of insurance industry. Then, from the concept of data warehouse, describe the design and implementation of data warehouse concept model and logical model.

Data mining of WHO data warehouse with PASW modeler

2011 ◽  
Author(s):  
Shraddha Masih ◽  
Shruti Dubey ◽  
Dharmendra Pathak ◽  
Neha Rahatekar
Keyword(s):  
Data Mining ◽  
Data Warehouse

The Integral of Spatial Data Mining in the Era of Big Data

2017 ◽  
pp. 90-126
Author(s):  
Gebeyehu Belay Gebremeskel ◽  
Chai Yi ◽  
Zhongshi He
Keyword(s):  
Data Mining ◽  
Data Warehouse ◽  
Spatial Data ◽  
High Volume ◽  
Spatial Data Mining ◽  
Research Field ◽  
Data Sets ◽  
Data Types ◽  
Basic Principles ◽  
Gis Data

Data Mining (DM) is a rapidly expanding field in many disciplines, and it is greatly inspiring to analyze massive data types, which includes geospatial, image and other forms of data sets. Such the fast growths of data characterized as high volume, velocity, variety, variability, value and others that collected and generated from various sources that are too complex and big to capturing, storing, and analyzing and challenging to traditional tools. The SDM is, therefore, the process of searching and discovering valuable information and knowledge in large volumes of spatial data, which draws basic principles from concepts in databases, machine learning, statistics, pattern recognition and 'soft' computing. Using DM techniques enables a more efficient use of the data warehouse. It is thus becoming an emerging research field in Geosciences because of the increasing amount of data, which lead to new promising applications. The integral SDM in which we focused in this chapter is the inference to geospatial and GIS data.

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

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.

Data-Mining als Komponente eines Data-Warehouse

1996 ◽  
pp. 337-368 ◽  
Author(s):  
Nicolas Bissantz ◽  
Jürgen Hagedorn ◽  
Peter Mertens
Keyword(s):  
Data Mining ◽  
Data Warehouse
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