scholarly journals A model for data mining system in financial crisis management based on data warehouse concept

2005 ◽  
Vol 2 (1) ◽  
pp. 43-62
Author(s):  
Ljiljana Kascelan ◽  
Dragana Becejski-Vujaklija
Keyword(s):  
Data Mining ◽  
Financial Crisis ◽  
Crisis Management ◽  
Data Warehouse ◽  
Decision Processes ◽  
Data Mining Algorithm ◽  
Mining System ◽  
Business Decision ◽  
Data Mining System ◽  
Target Data

This paper deals with identification and analyses of business decision processes in financial crisis management and appropriate relational data warehouse design for this processes. Also, here a model for financial crises symptoms is proposed and a data mining algorithm for automatic detection of those symptoms is developed. Finally, paper presents the concept for realization of target data mining system, using Oracle tools.

scholarly journals A Feasible Data-Driven Mining System to Optimize Wastewater Treatment Process Design and Operation

Water ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 1342 ◽  
Author(s):  
Yong Qiu ◽  
Ji Li ◽  
Xia Huang ◽  
Hanchang Shi
Keyword(s):  
Data Mining ◽  
Wastewater Treatment ◽  
Data Warehouse ◽  
Process Design ◽  
High Efficiency ◽  
Data Driven ◽  
Process Data ◽  
Mining System ◽  
Wastewater Treatment Process ◽  
Data Mining System

Achieving low costs and high efficiency in wastewater treatment plants (WWTPs) is a common challenge in developing countries, although many optimizing tools on process design and operation have been well established. A data-driven optimal strategy without the prerequisite of expensive instruments and skilled engineers is thus attractive in practice. In this study, a data mining system was implemented to optimize the process design and operation in WWTPs in China, following an integral procedure including data collection and cleaning, data warehouse, data mining, and web user interface. A data warehouse was demonstrated and analyzed using one-year process data in 30 WWTPs in China. Six sludge removal loading rates on water quality indices, such as chemical oxygen demand (COD), total nitrogen (TN), and total phosphorous (TP), were calculated as derived parameters and organized into fact sheets. A searching algorithm was programmed to find out the five records most similar to the target scenario. A web interface was developed for users to input scenarios, view outputs, and update the database. Two case WWTPs were investigated to verify the data mining system. The results indicated that effluent quality of Case-1 WWTP was improved to meet the discharging criteria through optimal operations, and the process design of Case-2 WWTP could be refined in a feedback loop. A discussion on the gaps, potential, and challenges of data mining in practice was provided. The data mining system in this study is a good candidate for engineers to understand and control their processes in WWTPs.

scholarly journals Research of the Optimization of a Data Mining Algorithm Based on an Embedded Data Mining System

2013 ◽  
Vol 13 (Special-Issue) ◽  
pp. 5-17
Author(s):  
Xindi Wang ◽  
Mengfei Chen ◽  
Li Chen
Keyword(s):  
Data Mining ◽  
Theoretical Analysis ◽  
Strong Association ◽  
Database System ◽  
Data Mining Algorithm ◽  
Mining System ◽  
Mining Algorithm ◽  
Binary Coding ◽  
Data Mining System ◽  
Frequent Item Sets

Abstract At present most of the data mining systems are independent with respect to the database system, and data loading and conversion take much time. The running time of the algorithms in a data mining process is also long. Although some optimized algorithms have improved it in different aspects, they could not improve the efficiency to a large extent when many duplicate records are available in a database. Solving the problem of improving the efficiency of data mining in the presence of many coinciding records in a database, an Apriori optimized algorithm is proposed. Firstly, a new concept of duplication and use is suggested to remove and count the same records, in order to generate a new database of a small size. Secondly, the original database is compressed according to the users’ requirements. At last, finding the frequent item sets based on binary coding, strong association rules are obtained. The structure of the data mining system based on an embedded database has also been designed in this paper. The theoretical analysis and experimental verification prove that the optimized algorithm is appropriate and the algorithm application in an embedded data mining system can further improve the mining efficiency.

Implementation of data mining as a support of business application strategy

2018 ◽  
Vol 5 (1) ◽  
pp. 47-55
Author(s):  
Florensia Unggul Damayanti
Keyword(s):  
Data Mining ◽  
Random Forest ◽  
Business Strategy ◽  
Input Parameter ◽  
Data Mining Algorithm ◽  
Complex Data ◽  
Business Decision ◽  
Marketing Department ◽  
Business Application ◽  
Complex Data Sets

Data mining help industries create intelligent decision on complex problems. Data mining algorithm can be applied to the data in order to forecasting, identity pattern, make rules and recommendations, analyze the sequence in complex data sets and retrieve fresh insights. Yet, increasing of technology and various techniques among data mining availability data give opportunity to industries to explore and gain valuable information from their data and use the information to support business decision making. This paper implement classification data mining in order to retrieve knowledge in customer databases to support marketing department while planning strategy for predict plan premium. The dataset decompose into conceptual analytic to identify characteristic data that can be used as input parameter of data mining model. Business decision and application is characterized by processing step, processing characteristic and processing outcome (Seng, J.L., Chen T.C. 2010). This paper set up experimental of data mining based on J48 and Random Forest classifiers and put a light on performance evaluation between J48 and random forest in the context of dataset in insurance industries. The experiment result are about classification accuracy and efficiency of J48 and Random Forest , also find out the most attribute that can be used to predict plan premium in context of strategic planning to support business strategy.

An intelligent data mining system for drop test analysis of electronic products

2001 ◽  
Vol 24 (3) ◽  
pp. 222-231 ◽  
Author(s):  
Chi Zhou ◽  
P.C. Nelson ◽  
Weimin Xiao ◽  
T.M. Tirpak ◽  
S.A. Lane
Keyword(s):  
Data Mining ◽  
Drop Test ◽  
Mining System ◽  
Test Analysis ◽  
Electronic Products ◽  
Data Mining System

A framework for a World Wide Web-based Data Mining system

1998 ◽  
Vol 21 (3) ◽  
pp. 163-185 ◽  
Author(s):  
Johnny S.K. Wong ◽  
Rishi Nayar ◽  
Armin R. Mikler
Keyword(s):  
Data Mining ◽  
World Wide Web ◽  
World Wide ◽  
Mining System ◽  
Web Based ◽  
Data Mining System

General system theoretic approach to data mining system

2002 ◽  
Vol 31 (3) ◽  
pp. 245-264 ◽  
Author(s):  
Yasuhiko Takahara ◽  
Naoki Shiba ◽  
Yongmei Liu
Keyword(s):  
Data Mining ◽  
General System ◽  
Theoretic Approach ◽  
Mining System ◽  
Data Mining System
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