Real-time intelligent decision making with data mining

2004 ◽  
Author(s):  
Deepak P. Gupta ◽  
Bhaskaran Gopalakrishnan
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Real Time ◽  
Intelligent Decision Making ◽  
Intelligent Decision

scholarly journals IoT in Online Banking

2021 ◽  
Vol 2 (4) ◽  
pp. 219-222
Author(s):  
Malti Bansal ◽  
Naman Oberoi ◽  
Mohd. Sameer
Keyword(s):  
Decision Making ◽  
Real Time ◽  
Market Share ◽  
Online Banking ◽  
Huge Amount ◽  
Intelligent Decision Making ◽  
Intelligent Decision ◽  
Banking Institutions ◽  
Physical Objects

As we know, there are so many changes arriving right now ion the banking industries which are really complex industries. Every day, huge amount of data is processed and gathered. With this increase in size, it is becoming more difficult for banking institutions to manage this data and handle other segments of their business. This paper presents the scope of IoT in the banking domain and how various transformations could potentially bring game changing reforms in the traditional methodology. Banking institutions need to integrate IoT in their systems to increase their market share by providing services catered to a clients need based on the data that’s being processed in real time. In future, IoT will be able to create such technologies which will be able to connect physical objects so that objects can do their own intelligent decision making.

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.

EBDMSS

2012 ◽  
pp. 265-284
Author(s):  
Fen Wang ◽  
Natalie Lupton ◽  
David Rawlinson ◽  
Xingguo Zhang
Keyword(s):  
Decision Making ◽  
Real Time ◽  
Support System ◽  
Balanced Scorecard ◽  
Business Strategy ◽  
Business Managers ◽  
Web Based ◽  
Intelligent Decision Making ◽  
Intelligent Decision ◽  
Wide Range

This paper describes a Web-based intelligent decision making support system (DMSS) to deliver balanced scorecard (BSC) based modelling and analysis in support of strategic E-business management. This framework supports E-business managers during the strategy making process in a comprehensive, integrated, and continuous manner. The paper demonstrates how practitioners can use this system to deliver a wide range of embodied E-business strategy expertise in support of real-time decision making.

Intelligent decision-making with bird-strike risk assessment for airport bird repellent

2018 ◽  
Vol 122 (1252) ◽  
pp. 988-1002 ◽  
Author(s):  
Weishi Chen ◽  
Jie Zhang ◽  
Jing Li
Keyword(s):  
Risk Assessment ◽  
Decision Making ◽  
Real Time ◽  
Expert Knowledge ◽  
Assessment Model ◽  
Classification Model ◽  
Support Vector ◽  
Bird Strike ◽  
Intelligent Decision Making ◽  
Intelligent Decision

ABSTRACTAn intelligent decision-making method was proposed for airport bird-repelling based on a Support Vector Machine (SVM) and bird-strike risk assessment. The bird-strike risk assessment model is established with two exponential functions to separate the risk levels, while the SVM method includes two steps of training and testing. After the risk assessment, the Bird-Repelling Strategy Classification Model (BRSCM) was trained based on the expert knowledge and large amount of historical bird information collected by the airport linkage system for bird detection, surveillance and repelling. Then, in the testing step, the BRSCM was continuously optimised according to the real-time intelligent bird-repelling strategy results. Through several bird-repelling examples of a certain airport, it is demonstrated that the decision accuracy of BRSCM is relatively high, and it could solve new problems by self-correction. The proposed method achieved the optimised operation of multiple bird-repelling devices against real-time bird information with great improvement of bird-repelling effects, overcoming the tolerance of birds to the bird-repelling devices due to their long-term repeated operation.

EBDMSS

2011 ◽  
pp. 549-568
Author(s):  
Fen Wang ◽  
Natalie Lupton ◽  
David Rawlinson ◽  
Xingguo Zhang
Keyword(s):  
Decision Making ◽  
Real Time ◽  
Support System ◽  
Balanced Scorecard ◽  
Business Strategy ◽  
Business Managers ◽  
Web Based ◽  
Intelligent Decision Making ◽  
Intelligent Decision ◽  
Wide Range

This paper describes a Web-based intelligent decision making support system (DMSS) to deliver balanced scorecard (BSC) based modelling and analysis in support of strategic E-business management. This framework supports E-business managers during the strategy making process in a comprehensive, integrated, and continuous manner. The paper demonstrates how practitioners can use this system to deliver a wide range of embodied E-business strategy expertise in support of real-time decision making.

An Intelligent Support System Integrating Data Mining and Online Analytical Processing

2008 ◽  
pp. 2964-2977
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.

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