A Preliminary Estimate of The Apennine Brown Bear Population Size Based on Hair-Snag Sampling and Multiple Data Source Mark–Recapture Huggins Models

Ursus ◽  
2008 ◽  
Vol 19 (2) ◽  
pp. 105-121 ◽  
Author(s):  
Vincenzo Gervasi ◽  
Paolo Ciucci ◽  
John Boulanger ◽  
Mario Posillico ◽  
Cinzia Sulli ◽  
...  
Keyword(s):  
Population Size ◽  
Brown Bear ◽  
Preliminary Estimate ◽  
Mark Recapture ◽  
Multiple Data Source ◽  
Multiple Data ◽  
Data Source ◽  
Apennine Brown Bear

A multiple data source approach to improve abundance estimates of small populations: The brown bear in the Apennines, Italy

2012 ◽  
Vol 152 ◽  
pp. 10-20 ◽  
Author(s):  
Vincenzo Gervasi ◽  
Paolo Ciucci ◽  
John Boulanger ◽  
Ettore Randi ◽  
Luigi Boitani
Keyword(s):  
Brown Bear ◽  
Small Populations ◽  
Multiple Data Source ◽  
Multiple Data ◽  
Abundance Estimates ◽  
Data Source

Dependency Injection mechanism in Intelligence Information System of Power Quality

2012 ◽  
Vol 241-244 ◽  
pp. 3085-3091
Author(s):  
Jian Gong ◽  
Cui Hong Lv ◽  
Lin Hai Qi ◽  
Su Xia Ma
Keyword(s):  
Information System ◽  
Power Quality ◽  
Data Sources ◽  
Monitoring Data ◽  
Multiple Data Source ◽  
Data Provider ◽  
Multiple Data ◽  
Injection Mechanism ◽  
Data Source ◽  
Intelligent Information

The calculation subsystems of the power quality intelligent information system will face many types of monitoring data source, and when different data sources provide data for calculation subsystem, it does not need to change algorithm but need to change the way how to get the data needed; then how to make the calculation subsystem does not alter with the change of data provider becomes a necessary demand;Aiming at the problem this paper put forward a set of solutions, which are dependent on dependency-injection, to help the calculation subsystem in multiple data source supporting.

scholarly journals Multiple Data Source Discovery with Group Interaction Approach

2013 ◽  
Vol 760-762 ◽  
pp. 2141-2145
Author(s):  
Wu Hao
Keyword(s):  
Data Mining ◽  
Group Interaction ◽  
Attribute Level ◽  
Group Interactions ◽  
Multiple Data Source ◽  
Multiple Data ◽  
Medical Researchers ◽  
Data Source ◽  
Interaction Approach ◽  
Attribute Value

Medical researchers seek to identify and predict profit (or effectiveness) potential in a new medicine B against a specified disease by comparing it to an existing medicine A, which has been used to treat the disease for many years, called medicine assessment. Applying traditional data mining techniques to the medicine assessment, one can discover patterns, such as A.X=a à B.Y=b, which are identified at the attribute-value level. These patterns are useful in predicting associated behaviors at the attribute-value level. However, to evaluate B against A, we have to obtain globally useful relations between B and A at an attribute level. Therefore, this paper proposes a group interaction approach for multiple data source discovery. Group interactions include, such as rules, differences, and links between datasets. These group interactions are discovered at the attribute level. For example, R(A.X, B.Y), where R is a relationship, or a predication. Some examples are presented for illustrating the use of the group interaction approach.

Group Pattern Discovery Systems for Multiple Data Sources

2011 ◽  
pp. 546-549
Author(s):  
Shichao Zhang ◽  
Chengqi Zhang
Keyword(s):  
Pattern Analysis ◽  
Pattern Discovery ◽  
Data Sources ◽  
Multiple Data Source ◽  
Local Pattern ◽  
Multiple Data ◽  
Analysis Strategy ◽  
Data Source ◽  
Analysis System ◽  
Group Pattern

Multiple data source mining is the process of identifying potentially useful patterns from different data sources, or datasets (Zhang et al., 2003). Group pattern discovery systems for mining different data sources are based on local pattern-analysis strategy, mainly including logical systems for information enhancing, a pattern discovery system, and a post-pattern-analysis system.

Sign in / Sign up

Export Citation Format

Share Document