scholarly journals NASA aviation safety program: Aircraft Engine Health Management Data Mining Tools roadmap

2000 ◽  
Author(s):  
Jonathan S. Litt ◽  
Donald L. Simon ◽  
Claudia Meyer ◽  
Hans DePold ◽  
J. R. Curtiss ◽  
...  
Keyword(s):  
Data Mining ◽  
Health Management ◽  
Aircraft Engine ◽  
Aviation Safety ◽  
Safety Program ◽  
Mining Tools

Data Mining in Aviation Safety Data Analysis

2011 ◽  
pp. 178-198 ◽  
Author(s):  
Reima Suomi ◽  
Olli Sjöblom
Keyword(s):  
Data Mining ◽  
Data Analysis ◽  
Safety Management ◽  
Safety Data ◽  
Aviation Safety ◽  
Important Application ◽  
Civil Aviation ◽  
Basic Concepts ◽  
In The Beginning ◽  
Mining Tools

This chapter introduces aviation safety data analysis as an important application area for data mining. In the beginning of the chapter, the reader is introduced to the basic concepts of data mining. After that, the field of aviation safety management is discussed, and in that connection data mining is identified as a key technology to study through flight incidents reports. Afterwards the test runs for four data mining products, for possible use in the Finnish civil aviation authority, are described in detail. However, before the testing of tools the preparation of the test data for the tools is described in detail. The chapter ends with conclusions that tell that even sophisticated data mining tools are just tools: they do not provide any automatic tools, but skilled users can use them for searching clues in the data.

Hot-spot analysis of motorcyclist crashes involving fixed objects using multinomial logit and data mining tools

2021 ◽  
pp. 1-19
Author(s):  
Bahar Dadashova ◽  
Chiara Silvestri-Dobrovolny ◽  
Jayveersinh Chauhan ◽  
Marcie Perez ◽  
Roger Bligh
Keyword(s):  
Data Mining ◽  
Hot Spot ◽  
Multinomial Logit ◽  
Hot Spot Analysis ◽  
Mining Tools ◽  
Spot Analysis

DATA MINING FOR THE MANAGEMENT OF SOFTWARE DEVELOPMENT PROCESS

2004 ◽  
Vol 14 (06) ◽  
pp. 665-695 ◽  
Author(s):  
J. L. ÁLVAREZ-MACÍAS ◽  
J. MATA-VÁZQUEZ ◽  
J. C. RIQUELME-SANTOS
Keyword(s):  
Data Mining ◽  
Software Development ◽  
Unsupervised Learning ◽  
Supervised Learning ◽  
Development Process ◽  
A Priori ◽  
Post Mortem ◽  
Software Development Process ◽  
A Priori Analysis ◽  
Mining Tools

In this paper we present a new method for the application of data mining tools on the management phase of software development process. Specifically, we describe two tools, the first one based on supervised learning, and the second one on unsupervised learning. The goal of this method is to induce a set of management rules that make easy the development process to the managers. Depending on how and to what is this method applied, it will permit an a priori analysis, a monitoring of the project or a post-mortem analysis.

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