Development of Knowledge Discovery System from Annotated Time Sereis Data

2012 ◽  
Vol 132 (4) ◽  
pp. 592-597
Author(s):  
Hiroshi Sugimura ◽  
Kazunori Matsumoto
Keyword(s):  
Knowledge Discovery ◽  
Discovery System

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.

Development of an Intelligent NLP-Based Audit Plan Knowledge Discovery System

2019 ◽  
Vol 17 (1) ◽  
pp. 89-97
Author(s):  
Qiao Li ◽  
Junming Liu
Keyword(s):  
Natural Language Processing ◽  
Decision Support ◽  
Natural Language ◽  
Knowledge Base ◽  
Knowledge Discovery ◽  
Language Processing ◽  
Discovery System

ABSTRACT Auditors' discussions in audit plan brainstorming sessions provide valuable knowledge on how audit engagement teams evaluate information, identify and assess risks, and make audit decisions. Collected expertise and experience from experienced auditors can be used as decision support for future audit plan engagements. With the help of Natural Language Processing (NLP) techniques, this paper proposes an intelligent NLP-based audit plan knowledge discovery system (APKDS) that can collect and extract important contents from audit brainstorming discussions and transfer the extracted contents into an audit knowledge base for future use.

Discovering Quality Knowledge from Relational Databases

2011 ◽  
pp. 95-111
Author(s):  
M. Mehdi Owrang O.
Keyword(s):  
Knowledge Discovery ◽  
Large Volume ◽  
Relational Databases ◽  
Discovery System ◽  
New Knowledge ◽  
Database Technology ◽  
Long Term Trends ◽  
Current Database

Current database technology involves processing a large volume of data in order to discover new knowledge. However, knowledge discovery on just the most detailed and recent data does not reveal the long-term trends. Relational databases create new types of problems for knowledge discovery since they are normalized to avoid redundancies and update anomalies, which make them unsuitable for knowledge discovery. A key issue in any discovery system is to ensure the consistency, accuracy, and completeness of the discovered knowledge. We describe the aforementioned problems associated with the quality of the discovered knowledge and provide some solutions to avoid them.

Discovering Quality Knowledge from Relational Databases

2009 ◽  
pp. 238-256 ◽  
Author(s):  
M. Mehdi Owrang O.
Keyword(s):  
Knowledge Discovery ◽  
Large Volume ◽  
Relational Databases ◽  
Discovery System ◽  
New Knowledge ◽  
Database Technology ◽  
Long Term Trends ◽  
Current Database

Current database technology involves processing a large volume of data in order to discover new knowledge. However, knowledge discovery on just the most detailed and recent data does not reveal the long-term trends. Relational databases create new types of problems for knowledge discovery since they are normalized to avoid redundancies and update anomalies, which make them unsuitable for knowledge discovery. A key issue in any discovery system is to ensure the consistency, accuracy, and completeness of the discovered knowledge. We describe the aforementioned problems associated with the quality of the discovered knowledge and provide some solutions to avoid them.

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