scholarly journals Visualization Techniques: Which is the Most Appropriate in the Process of Knowledge Discovery in Data Base?

10.5772/50163 ◽  
2012 ◽  
Author(s):  
Maria Madalena ◽  
Juliana Keiko ◽  
Emerson Rabelo ◽  
Cllia Franco
Keyword(s):  
Knowledge Discovery ◽  
Data Base ◽  
Visualization Techniques

VISUALIZATION SUPPORT FOR USER-CENTERED MODEL SELECTION IN KNOWLEDGE DISCOVERY AND DATA MINING

2001 ◽  
Vol 10 (04) ◽  
pp. 691-713 ◽  
Author(s):  
TUBAO HO ◽  
TRONGDUNG NGUYEN ◽  
DUCDUNG NGUYEN ◽  
SAORI KAWASAKI
Keyword(s):  
Data Mining ◽  
Model Selection ◽  
Knowledge Discovery ◽  
Performance Metrics ◽  
Model Performance ◽  
Discovery Process ◽  
Whole Process ◽  
Discovery System ◽  
Competing Models ◽  
Visualization Techniques

The problem of model selection in knowledge discovery and data mining—the selection of appropriate discovered patterns/models or algorithms to achieve such patterns/models—is generally a difficult task for the user as it requires meta-knowledge on algorithms/models and model performance metrics. Viewing knowledge discovery as a human-centered process that requires an effective collaboration between the user and the discovery system, our work aims to make model selection in knowledge discovery easier and more effective. For such a collaboration, our solution is to give the user the ability to try easily various alternatives and to compare competing models quantitatively and qualitatively. The basic idea of our solution is to integrate data and knowledge visualization with the knowledge discovery process in order to the support the participation of the user. We introduce the knowledge discovery system D2MS in which several visualization techniques of data and knowledge are developed and integrated into the steps of the knowledge discovery process. The visualizers in D2MS greatly help the user gain better insight in each step of the knowledge discovery process as well the relationship between data and discovered knowledge in the whole process.

Air Pollution and Hospital Admissions: Looking for Spatial-Time Association by Technologies of Knowledge Discovery and Delivery (KDD) in Data Base.

Epidemiology ◽  
2006 ◽  
Vol 17 (Suppl) ◽  
pp. S507 ◽  
Author(s):  
A Uva ◽  
M A Vigotti ◽  
A M Romanelli ◽  
M Raciti ◽  
M A Protti ◽  
...  
Keyword(s):  
Air Pollution ◽  
Knowledge Discovery ◽  
Data Base ◽  
Hospital Admissions

scholarly journals Innovative information visualization of electronic health record data: a systematic review

2014 ◽  
Vol 22 (2) ◽  
pp. 330-339 ◽  
Author(s):  
Vivian L West ◽  
David Borland ◽  
W Ed Hammond
Keyword(s):  
Electronic Health Record ◽  
Knowledge Discovery ◽  
Information Visualization ◽  
Final Analysis ◽  
Reference List ◽  
Health Record ◽  
Complex Data ◽  
Electronic Health Record Data ◽  
Electronic Health ◽  
Visualization Techniques

Abstract Objective This study investigates the use of visualization techniques reported between 1996 and 2013 and evaluates innovative approaches to information visualization of electronic health record (EHR) data for knowledge discovery. Methods An electronic literature search was conducted May–July 2013 using MEDLINE and Web of Knowledge, supplemented by citation searching, gray literature searching, and reference list reviews. General search terms were used to assure a comprehensive document search. Results Beginning with 891 articles, the number of articles was reduced by eliminating 191 duplicates. A matrix was developed for categorizing all abstracts and to assist with determining those to be excluded for review. Eighteen articles were included in the final analysis. Discussion Several visualization techniques have been extensively researched. The most mature system is LifeLines and its applications as LifeLines2, EventFlow, and LifeFlow. Initially, research focused on records from a single patient and visualization of the complex data related to one patient. Since 2010, the techniques under investigation are for use with large numbers of patient records and events. Most are linear and allow interaction through scaling and zooming to resize. Color, density, and filter techniques are commonly used for visualization. Conclusions With the burgeoning increase in the amount of electronic healthcare data, the potential for knowledge discovery is significant if data are managed in innovative and effective ways. We identify challenges discovered by previous EHR visualization research, which will help researchers who seek to design and improve visualization techniques.

Semantic Visualization to Support Knowledge Discovery in Multi-Relational Service Communities

2011 ◽  
pp. 281-303
Author(s):  
Nadeem Bhatti ◽  
Stefan Hagen Weber
Keyword(s):  
Web Services ◽  
Knowledge Discovery ◽  
Service Oriented Architecture ◽  
Service Providers ◽  
Business Value ◽  
Value Networks ◽  
Service Oriented ◽  
Hybrid Recommender ◽  
Visualization Techniques ◽  
Semantic Visualization

Services provided through the Internet serve a dual purpose. They are used by consumers and by technical systems to access business functionality, which is provided remotely by business partners. The semantics of services, multi-relational networked data and knowledge discovery in multi-relational service communities (e.g., service providers, service consumers, and service brokers, etc.) become an area of increasing interest. The complex multi-dimensional semantic relationship between services demands innovative and intuitive visualization techniques to present knowledge in a personalized manner, where community members can interact with knowledge assets and navigate through the network of Semantic Web services. In this chapter, the authors introduce Semantic Visualization approach (SemaVis) to support knowledge discovery by using hybrid recommender system (HYRES). It makes use of the semantic descriptions of the Web services, and also exploits the dynamic evolving relationships between services, service providers and service consumers. The authors introduce a sample scenario from a research project TEXO, within the THESEUS research program initiated by the German Federal Ministry of Economy and Technology (BMWi). It aims to supply a service-oriented architecture for the integration of Web-based services in the next generation of Business Value Networks. The authors present as well the application of their approaches SemaVis and HYRES to support knowledge discovery in multi-relational service communities of future Business Value Networks.

Towards the use of Data Engineering, Advanced Visualization techniques and Association Rules to support knowledge discovery for public policies

2020 ◽  
pp. 114509
Author(s):  
Jose María Conejero ◽  
Juan Carlos Preciado ◽  
Antonio Jesús Fernández-García ◽  
Alvaro E. Prieto ◽  
Roberto Rodríguez-Echeverría
Keyword(s):  
Knowledge Discovery ◽  
Association Rules ◽  
Public Policies ◽  
Use Of Data ◽  
Data Engineering ◽  
Visualization Techniques ◽  
Advanced Visualization

Predicting spatial activity patterns in a neural map from a probabilistic atlas of 3-D reconstructed neurons

1995 ◽  
Vol 53 ◽  
pp. 812-813
Author(s):  
G. Jacobs ◽  
F. Theunissen
Keyword(s):  
Activity Patterns ◽  
Three Dimensional ◽  
Sensory System ◽  
Neural System ◽  
Probabilistic Atlas ◽  
Uniform Sample ◽  
Dimensional Reconstruction ◽  
The Time Domain ◽  
And Function ◽  
Visualization Techniques

In order to understand how the algorithms underlying neural computation are implemented within any neural system, it is necessary to understand details of the anatomy, physiology and global organization of the neurons from which the system is constructed. Information is represented in neural systems by patterns of activity that vary in both their spatial extent and in the time domain. One of the great challenges to microscopists is to devise methods for imaging these patterns of activity and to correlate them with the underlying neuroanatomy and physiology. We have addressed this problem by using a combination of three dimensional reconstruction techniques, quantitative analysis and computer visualization techniques to build a probabilistic atlas of a neural map in an insect sensory system. The principal goal of this study was to derive a quantitative representation of the map, based on a uniform sample of afferents that was of sufficient size to allow statistically meaningful analyses of the relationships between structure and function.

Data base or data bias?

1990 ◽  
Vol 45 (5) ◽  
pp. 676-676 ◽  
Author(s):  
Douglas E. Mould
Keyword(s):  
Data Base
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