An Empirical Study of Predictive Modeling Techniques of Software Quality

This study investigates the synergistic relationships among intellectual capital, process capability, and medical service performance. An empirical study was conducted by using a second-order research framework. Data were collected through a questionnaire survey, and structural equation modeling techniques were used to analyze the data. An empirical analysis revealed that intellectual capital is a major factor influencing final medical service performance. This major factor should be carefully improved to increase process capability in hospitals in the long term. Hospitals account for a substantial proportion of the intellectual capital in the health-care industry, and, thus, should improve their process capability to achieve high medical service performance.

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An Analysis of the Use of Predictive Modeling with Business Intelligence Systems for Exploration of Precious Metals Using Biogeochemical Data

International Journal of Business Intelligence Research ◽

10.4018/jbir.2013040103 ◽

2013 ◽

Vol 4 (2) ◽

pp. 39-53 ◽

Cited By ~ 2

Author(s):

Thomas A. Woolman ◽

John C. Yi

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Anomaly Detection ◽

Predictive Modeling ◽

Business Intelligence ◽

Precious Metals ◽

Geological Exploration ◽

Feed Forward ◽

Business Intelligence Systems ◽

Modeling Techniques

This study addresses the use of predictive modeling techniques; primarily feed-forward artificial neural networks as a tool for forecasting geological exploration targets for gold prospecting. It also provides evidence of effectiveness of using Business Intelligence systems to model pathfinder variables, anomaly detection, and forecasting to locate potential exploration sites for precious metals. The results indicate that the use of advanced Business Intelligence systems can be of extremely high value to the extractive minerals exploration industry.

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Aggregation for Predictive Modeling with Relational Data

Encyclopedia of Data Warehousing and Mining ◽

10.4018/978-1-59140-557-3.ch007 ◽

2011 ◽

pp. 33-38

Author(s):

Claudia Perlich ◽

Foster Provost

Keyword(s):

Data Mining ◽

Predictive Modeling ◽

Relational Databases ◽

Feature Vector ◽

Relational Data ◽

Government Organizations ◽

Modeling Techniques ◽

Aggregation Of Information ◽

New Challenges

Most data mining and modeling techniques have been developed for data represented as a single table, where every row is a feature vector that captures the characteristics of an observation. However, data in most domains are not of this form and consist of multiple tables with several types of entities. Such relational data are ubiquitous; both because of the large number of multi-table relational databases kept by businesses and government organizations, and because of the natural, linked nature of people, organizations, computers, and etc. Relational data pose new challenges for modeling and data mining, including the exploration of related entities and the aggregation of information from multi-sets (“bags”) of related entities.

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