Data Mining Architecture System Expansion

2014 ◽  
Vol 926-930 ◽  
pp. 1898-1901
Author(s):  
Jing Zhu Li ◽  
Hui Ling Wu ◽  
Tai Yu Liu
Keyword(s):  
Data Mining ◽  
Data Warehouse ◽  
Traditional Method ◽  
New Technologies ◽  
System Expansion ◽  
New Development ◽  
Design Selection ◽  
Commercial Applications ◽  
Technical Features ◽  
Enterprise Data Warehouse

How to build a data warehouse and data mining is constantly worth exploring and optimization, not only technically, in commercial applications as well. This article discusses the introduction of new technologies and concepts, the traditional method of data warehouse technology has changed dramatically, based on data warehouse applications are a new development. Each enterprise data warehouse based on the characteristics of different companies, you can use a very flexible method of selection and design selection, implementation. According to some relatively new technical features, talk about data warehousing and data mining architecture.

Semantic Data Mining

2008 ◽  
pp. 3524-3530
Author(s):  
Protima Banerjee ◽  
Xiaohua Hu ◽  
Illhio Yoo
Keyword(s):  
Data Mining ◽  
Data Warehouse ◽  
Transaction Processing ◽  
Data Sets ◽  
Ordered Trees ◽  
Semantic Data ◽  
Symbolic Sequences ◽  
Semantic Data Mining ◽  
Enterprise Data Warehouse ◽  
Mining Algorithms

Over the past few decades, data mining has emerged as a field of research critical to understanding and assimilating the large stores of data accumulated by corporations, government agencies, and laboratories. Early on, mining algorithms and techniques were limited to relational data sets coming directly from Online Transaction Processing (OLTP) systems, or from a consolidated enterprise data warehouse. However, recent work has begun to extend the limits of data mining strategies to include “semi-structured data such as HTML and XML texts, symbolic sequences, ordered trees and relations represented by advanced logics” (Washio & Motoda, 2003).

Semantic Data Mining

2011 ◽  
pp. 1010-1014
Author(s):  
Protima Banerjee ◽  
Xiaohua Hu ◽  
Illhoi Yoo
Keyword(s):  
Data Mining ◽  
Data Warehouse ◽  
Transaction Processing ◽  
Data Sets ◽  
Ordered Trees ◽  
Semantic Data ◽  
Symbolic Sequences ◽  
Semantic Data Mining ◽  
Enterprise Data Warehouse ◽  
Mining Algorithms

Over the past few decades, data mining has emerged as a field of research critical to understanding and assimilating the large stores of data accumulated by corporations, government agencies, and laboratories. Early on, mining algorithms and techniques were limited to relational data sets coming directly from Online Transaction Processing (OLTP) systems, or from a consolidated enterprise data warehouse. However, recent work has begun to extend the limits of data mining strategies to include “semi-structured data such as HTML and XML texts, symbolic sequences, ordered trees and relations represented by advanced logics” (Washio & Motoda, 2003).

DATA MINING AS A MEANS OF QUALITY CONTROL IN PRODUCTION AND ORGANIZATION MANAGEMENT

2020 ◽  
Vol 1 (5) ◽  
pp. 130-138
Author(s):  
L. S. ZVYAGIN ◽  
Keyword(s):  
Data Mining ◽  
Quality Control ◽  
New Technologies ◽  
Data Interpretation ◽  
Organization Management

The article deals with data mining (IAD), which is widely used both in business and in various studies. IAD methods are used to create new ways to solve problems of forecasting, segmentation, data interpretation, etc. The problems to be solved by creating new technologies and methods of IAD are analyzed.

The Japanese National Large Telescope

1991 ◽  
Vol 9 (1) ◽  
pp. 52-54 ◽  
Author(s):  
Tomakazu Kogure ◽  
Keiichi Kodaira
Keyword(s):  
International Collaboration ◽  
New Technologies ◽  
Study Phase ◽  
Large Telescope ◽  
High Quality ◽  
Quality Performance ◽  
Final Study ◽  
Technical Features ◽  
Active Mirror

AbstractThe Japanese National Large Telescope is an 8-metre class optical-infrared reflector with a monolithic thin meniscus mirror, to be constructed at the Mauna Kea summit, Hawaii. The JNLT will be characterised by high quality performance in the optical and infrared regions, achieved by adopting new technologies such as active mirror support, fast optics and a thermally controlled dome. In particular, high infrared qualities are regarded as the most important characteristics among various design goals.The JNLT project is now close to the final study phase before construction. This paper reviews the scientific motivations and the special technical features of the JNLT. Finally, the promotion of international collaboration around the JNLT is emphasised.

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 Efficient Transfer of data from RDBMS to HDFS and conversion to JSON format

2021 ◽  
Vol 9 (10) ◽  
pp. 1869-1871
Author(s):  
Dr. C. K. Gomathy
Keyword(s):  
Data Warehouse ◽  
Relational Databases ◽  
Command Line ◽  
Command Line Interface ◽  
Apache Hadoop ◽  
Enterprise Data Warehouse ◽  
Efficient Transfer ◽  
Efficient Execution

Abstract: Apache Sqoop is mainly used to efficiently transfer large volumes of data between Apache Hadoop and relational databases. It helps to certain tasks, such as ETL (Extract transform load) processing, from an enterprise data warehouse to Hadoop, for efficient execution at a much less cost. Here first we import the table which presents in MYSQL Database with the help of command-line interface application called Sqoop and there is a chance of addition of new rows and updating new rows then we have to execute the query again. So, with the help of our project there is no need of executing queries again for that we are using Sqoop job, which consists of total commands for import and next after import we retrieve the data from hive using Java JDBC and we convert the data to JSON Format, which consists of data in an organized way and easy to access manner by using GSON Library. Keywords: Sqoop, Json, Gson, Maven and JDBC

scholarly journals Predicting Academic Course Preference using Hadoop

2019 ◽  
Vol 8 (4) ◽  
pp. 2527-2530
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
New Technologies ◽  
Parallel Applications ◽  
Distributed File System ◽  
Industrial Training ◽  
Effective Learning ◽  
Meaningful Information ◽  
Learning Techniques ◽  
Mining Tools

These days new technologies have been introduced by this new academic trends also have been came into existence into the education system. And this leads to huge amounts of data which makes a big challenge for the students to store the preferred course. For this many data mining tools have been invented to convert the unregulated data into structured format to understand the meaningful information. As we know that Hadoop is a distributed file system which is used to hold huge amounts of data this stores the files in a redundant fashion across multiple machines. Due to this it leads to failure and parallel applications do not work. To avoid this problem we are using Mapreduce for decision making of students in order to choose their preferred course for industrial training purpose for their effective learning techniques to increase their knowledge and capability.

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).

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