The Research of WebGIS-Based Data Mining

2011 ◽  
Vol 267 ◽  
pp. 774-777
Author(s):  
Chun Chang Fu
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Decision Support ◽  
Network Analysis ◽  
Gis Analysis ◽  
Mining Technology ◽  
Mass Data ◽  
Support Level ◽  
Decision Making Support

Data mining provide us a technical found potential knowledge technical from mass data , the data mining technology is applied to GIS can promote GIS analysis of the data, so as to improve the ability of decision support level. WebGIS is traditional GIS space outspread, current network analysis and decision making support capability are low, the network-based integration of data mining and WebGIS can strengthen the analytical capacity.

THE RESEARCH TREND OF INFORMATION TECHNOLOGY AND DECISION MAKING IN 2009

2010 ◽  
Vol 09 (01) ◽  
pp. 1-8 ◽  
Author(s):  
YONG SHI
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Information Technology ◽  
Decision Support ◽  
Risk Analysis ◽  
Multiple Criteria Decision Making ◽  
Advisory Board ◽  
Research Trend ◽  
Research Topics

The research topics of the 39 papers published in the International Journal of Information Technology and Decision Making (IT&DM) in 2009 can be classified into three major directions: decision support, multiple criteria decision making, and data mining and risk analysis. The Editor-in-Chief, on behalf of the editorial board and advisory board, highlights the key ideas of these contributions. The seven papers in first issue of 2010 IT&DM are also introduced.

The Safety Monitoring of Colliery Equipments Based on Data Mining Technology

2011 ◽  
Vol 128-129 ◽  
pp. 731-734
Author(s):  
Shu Fang Zhao ◽  
Li Chao Chen
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Essential Role ◽  
Scientific Information ◽  
Safety Monitoring ◽  
Monitoring Data ◽  
Mining Technology ◽  
Use Of Data ◽  
Stochastic Data

Data mining is the process of abstracting unaware, potential and useful information and knowledge from plentiful, incomplete, noisy, fuzzy and stochastic data. The reliability of colliery equipments takes an essential role in the safety of production. Not only since their continuance of operation, had the accumulation of historical error data of colliery equipments resulted in a mass of surplus data, but also because their lacks of helpful information, which as a result makes colliery managers as well as equipment operators hard to make decisions. Seeing that, we introduced ways here that makes use of data mining technology by processing and analyzing historical monitoring data, recognizing and extracting meaningful patterns so as to provide scientific information for decision-making on the safety of colliery operations, which would help for the forecasting of potential threatens of colliery equipments’ operation, thus, make great contributions to prevent disasters from happening.

scholarly journals Analysis of Characteristics of Tennis Singles Matches Based on 5G and Data Mining Technology

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Ming Li ◽  
Qinsheng Li ◽  
Yuening Li ◽  
Yunkun Cui ◽  
Xiufeng Zhao ◽  
...  
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Statistical Methods ◽  
Analysis Data ◽  
Scientific Basis ◽  
Experimental Simulation ◽  
Mining Technology ◽  
Offensive Tactics ◽  
Analysis System ◽  
Scientific Decision

The level of technical and tactical decision-making in a tennis game has a very important impact on the outcome of the game. How to discover the characteristics and rules of the game from a large amount of technical and tactical data, how to overcome the shortcomings of traditional statistical methods, and how to provide a scientific basis for correct decision-making are a top priority. Based on 5G and association analysis data mining theory, we established a data mining model for tennis technical offensive tactics and association rules and conducted specific case studies. It can calculate the maximization and distribution rate of certain technologies, also distinguish between the athlete’s gain and loss rate and the spatial position on the track, and use artificial statistical methods to cause errors and subjective participation. This solution provides objective and scientific decision support for this problem and is used in the decision-making of the landing point in tennis match technology and tactics. Experimental simulation shows that the data mining technology analysis system used for regional tennis matches is more concise, efficient, and accurate than traditional movie analysis methods.

Closing the Gap between Data Mining and Business Users of Business Intelligence Systems

2012 ◽  
Vol 3 (4) ◽  
pp. 14-53 ◽  
Author(s):  
Ana Azevedo ◽  
Manuel Filipe Santos
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Decision Support ◽  
Information Systems ◽  
Decision Support Systems ◽  
Business Intelligence ◽  
Easy Access ◽  
Complete Knowledge ◽  
Business Intelligence Systems ◽  
Closing The Gap

Since Lunh first used the term Business Intelligence (BI) in 1958, major transformations happened in the field of information systems and technologies, especially in the area of decision support systems. BI systems are widely used in organizations and their importance is recognized. These systems present themselves as essential parts of a complete knowledge of business and an irreplaceable tool in the support to decision making. The dissemination of data mining (DM) tools is increasing in the BI field, as well as the acknowledgment of the relevance of its usage in enterprise BI systems. BI tools are friendly, iterative, and interactive, allowing business users an easy access. The user can manipulate directly data, having the ability to extract all the value contained into that business data. Problems noted in the use of DM in the field of BI is related to the fact that DM models are complex in order to be directly manipulated by business users, not including BI tools. The nonexistence of BI tools allowing business users the direct manipulation of DM models was identified as the problem. More of these issues, possible solutions and conclusions are presented in this article.

Non-Traditional Data Mining Applications in Taiwan National Health Insurance (NHI) Databases

2017 ◽  
Vol 12 (4) ◽  
pp. 31-51
Author(s):  
Joseph Tan ◽  
Fuchung Wang
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Health Insurance ◽  
Decision Support ◽  
National Health Insurance ◽  
National Health ◽  
Domain Knowledge ◽  
Lessons Learned ◽  
Application Systems ◽  
Taiwan National Health Insurance

This study examines time-sensitive applications of data mining methods to facilitate claims review processing and provide policy information for insurance decision-making vis-à-vis the Taiwan National Health Insurance (NHI) databases. In order to obtain the best payment management, a hybrid mining (HM) approach, which has been grounded on the extant knowledge of data mining projects and health insurance domain knowledge, is proposed. Through the integration of data warehousing, online analytic processing, data mining techniques and traditional data analysis in the healthcare field, an easy-to-use decision support platform, which will assist in directing the health insurance decision-making process, is built. Drawing from lessons learned within a case study setting, results showed that not only is HM approach a reliable, powerful, and user-friendly platform for diversified payment decision support, but that it also has great relevance for the practice and acceptance of evidence-based medicine. Essentially, HM approach can provide a critical boost to health insurance decision support; hence, future researchers should develop and improve the approach combined with their own application systems.

Non-Traditional Data Mining Applications in Taiwan National Health Insurance (NHI) Databases

2017 ◽  
pp. 666-688
Author(s):  
Joseph Tan ◽  
Fuchung Wang
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Health Insurance ◽  
Decision Support ◽  
National Health Insurance ◽  
National Health ◽  
Lessons Learned ◽  
Policy Information ◽  
Taiwan National Health Insurance ◽  
Mining Projects

This study examines time-sensitive applications of data mining methods to facilitate claims review processing and provide policy information for insurance decision-making vis-à-vis the Taiwan National Health Insurance (NHI) databases. In order to obtain the best payment management, a hybrid mining (HM) approach, which has been grounded on the extant knowledge of data mining projects and health insurance domain knowledge, is proposed. Through the integration of data warehousing, online analytic processing, data mining techniques and traditional data analysis in the healthcare field, an easy-to-use decision support platform, which will assist in directing the health insurance decision-making process, is built. Drawing from lessons learned within a case study setting, results showed that not only is HM approach a reliable, powerful, and user-friendly platform for diversified payment decision support, but that it also has great relevance for the practice and acceptance of evidence-based medicine. Essentially, HM approach can provide a critical boost to health insurance decision support; hence, future researchers should develop and improve the approach combined with their own application systems.

An Intelligent Support System Integrating Data Mining and Online Analytical Processing

2011 ◽  
pp. 141-156
Author(s):  
Rahul Singh ◽  
Richard T. Redmond ◽  
Victoria Yoon
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Decision Support ◽  
Decision Makers ◽  
Online Analytical Processing ◽  
Intelligent Decision Making ◽  
Course Of Action ◽  
Intelligent Decision ◽  
Analytical Processing ◽  
Intelligent Decision Support

Intelligent decision support requires flexible, knowledge-driven analysis of data to solve complex decision problems faced by contemporary decision makers. Recently, online analytical processing (OLAP) and data mining have received much attention from researchers and practitioner alike, as components of an intelligent decision support environment. Little that has been done in developing models to integrate the capabilities of data mining and online analytical processing to provide a systematic model for intelligent decision making that allows users to examine multiple views of the data that are generated using knowledge about the environment and the decision problem domain. This paper presents an integrated model in which data mining and online analytical processing complement each other to support intelligent decision making for data rich environments. The integrated approach models system behaviors that are of interest to decision makers; predicts the occurrence of such behaviors; provides support to explain the occurrence of such behaviors and supports decision making to identify a course of action to manage these behaviors.

Sign in / Sign up

Export Citation Format

Share Document