Secure Knowledge Discovery in Databases

2011 ◽  
pp. 3105-3115
Author(s):  
Rick L. Wilson ◽  
Peter A. Rosen ◽  
Mohammad Saad Al-Ahmadi
Keyword(s):  
Knowledge Management ◽  
Knowledge Discovery ◽  
Organizational Knowledge ◽  
Knowledge Discovery In Databases

Knowledge management (KM) systems are quite diverse, but all provide increased access to organizational knowledge, which helps the enterprise to be more connected, agile, and effective. The dilemma faced when using a KM system is to balance the goal of being knowledge-enabled while being knowledge-secure (Cohen, 2003; Lee & Rosenbaum, 2003).

Secure Knowledge Discovery in Databases

2009 ◽  
pp. 186-195
Author(s):  
Rick L. Wilson ◽  
Peter A. Rosen ◽  
Mohammad Saad Al-Ahmadi
Keyword(s):  
Knowledge Management ◽  
Knowledge Discovery ◽  
September 11 ◽  
Organizational Knowledge ◽  
Recent Survey ◽  
Knowledge Discovery In Databases ◽  
It Security

Knowledge management (KM) systems are quite diverse, but all provide increased access to organizational knowledge, which helps the enterprise to be more connected, agile, and effective. The dilemma faced when using a KM system is to balance the goal of being knowledge-enabled while being knowledge-secure (Cohen, 2003; Lee & Rosenbaum, 2003). A recent survey of IT security professions found that over 50% of respondents indicated an increase in the security budgets of their organizations since September 11, 2001, and projected that 2004 IT security budgets would be larger than ever (Briney & Prince, 2003).

Secure Knowledge Discovery in Databases

2011 ◽  
pp. 787-794 ◽  
Author(s):  
Rick L. Wilson ◽  
Peter A. Rosen ◽  
Mohammad Saad Al-Ahmadi
Keyword(s):  
Knowledge Management ◽  
Knowledge Discovery ◽  
Organizational Knowledge ◽  
Knowledge Discovery In Databases

Knowledge management (KM) systems are quite diverse, but all provide increased access to organizational knowledge, which helps the enterprise to be more connected, agile, and effective. The dilemma faced when using a KM system is to balance the goal of being knowledge-enabled while being knowledge-secure (Cohen, 2003; Lee & Rosenbaum, 2003).

Secure Knowledge Discovery in Databases

2010 ◽  
pp. 1419-1428
Author(s):  
Rick L. Wilson ◽  
Peter A. Rosen ◽  
Mohammad Saad Al-Ahmadi
Keyword(s):  
Knowledge Management ◽  
Knowledge Discovery ◽  
Organizational Knowledge ◽  
Knowledge Discovery In Databases

Knowledge management (KM) systems are quite diverse, but all provide increased access to organizational knowledge, which helps the enterprise to be more connected, agile, and effective. The dilemma faced when using a KM system is to balance the goal of being knowledge-enabled while being knowledge-secure (Cohen, 2003; Lee & Rosenbaum, 2003).

scholarly journals Particularities of data mining in medicine: lessons learned from patient medical time series data analysis

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Shadi Aljawarneh ◽  
Aurea Anguera ◽  
John William Atwood ◽  
Juan A. Lara ◽  
David Lizcano
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Knowledge Discovery ◽  
Time Series Data ◽  
Medical Patient ◽  
Lessons Learned ◽  
Physiological Signals ◽  
Knowledge Discovery In Databases ◽  
Series Data ◽  
Data Mining Techniques

AbstractNowadays, large amounts of data are generated in the medical domain. Various physiological signals generated from different organs can be recorded to extract interesting information about patients’ health. The analysis of physiological signals is a hard task that requires the use of specific approaches such as the Knowledge Discovery in Databases process. The application of such process in the domain of medicine has a series of implications and difficulties, especially regarding the application of data mining techniques to data, mainly time series, gathered from medical examinations of patients. The goal of this paper is to describe the lessons learned and the experience gathered by the authors applying data mining techniques to real medical patient data including time series. In this research, we carried out an exhaustive case study working on data from two medical fields: stabilometry (15 professional basketball players, 18 elite ice skaters) and electroencephalography (100 healthy patients, 100 epileptic patients). We applied a previously proposed knowledge discovery framework for classification purpose obtaining good results in terms of classification accuracy (greater than 99% in both fields). The good results obtained in our research are the groundwork for the lessons learned and recommendations made in this position paper that intends to be a guide for experts who have to face similar medical data mining projects.

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