Bayes Performance of Batch Data Mining Based on Functional Dependencies

2019 ◽  
Vol 33 (03) ◽  
pp. 1959011
Author(s):  
Haixu Xi ◽  
Feiyue Ye ◽  
Sheng He ◽  
Yijun Liu ◽  
Hongfen Jiang
Keyword(s):  
Data Mining ◽  
Data Processing ◽  
Video Processing ◽  
Mining Area ◽  
Batch Processing ◽  
Video Data ◽  
Batch Processes ◽  
Functional Dependencies ◽  
Workflow System ◽  
Traffic Video

Batch processes and phenomena in traffic video data processing, such as traffic video image processing and intelligent transportation, are commonly used. The application of batch processing can increase the efficiency of resource conservation. However, owing to limited research on traffic video data processing conditions, batch processing activities in this area remain minimally examined. By employing database functional dependency mining, we developed in this study a workflow system. Meanwhile, the Bayesian network is a focus area of data mining. It provides an intuitive means for users to comply with causality expression approaches. Moreover, graph theory is also used in data mining area. In this study, the proposed approach depends on relational database functions to remove redundant attributes, reduce interference, and select a property order. The restoration of selective hidden naive Bayesian (SHNB) affects this property order when it is used only once. With consideration of the hidden naive Bayes (HNB) influence, rather than using one pair of HNB, it is introduced twice. We additionally designed and implemented mining dependencies from a batch traffic video processing log for data execution algorithms.

Image Features from Morphological Scale-Spaces

2011 ◽  
pp. 32-79
Author(s):  
Sébastien Lefèvre
Keyword(s):  
Data Mining ◽  
Performance Evaluation ◽  
Video Processing ◽  
State Of The Art ◽  
Image Features ◽  
Video Data ◽  
Multimedia Data ◽  
Multimedia Data Mining ◽  
Shot Detection ◽  
Scale Spaces

Video processing and segmentation are important stages for multimedia data mining, especially with the advance and diversity of video data available. The aim of this chapter is to introduce researchers, especially new ones, to the “video representation, processing, and segmentation techniques”. This includes an easy and smooth introduction, followed by principles of video structure and representation, and then a state-of-the-art of the segmentation techniques focusing on the shot-detection. Performance evaluation and common issues are also discussed before concluding the chapter.

Video Representation and Processing for Multimedia Data Mining

2011 ◽  
pp. 1-31 ◽  
Author(s):  
Amr Ahmed
Keyword(s):  
Data Mining ◽  
Performance Evaluation ◽  
Video Processing ◽  
State Of The Art ◽  
Detection Performance ◽  
Video Data ◽  
Multimedia Data ◽  
Multimedia Data Mining ◽  
Video Representation ◽  
Shot Detection

Video processing and segmentation are important stages for multimedia data mining, especially with the advance and diversity of video data available. The aim of this chapter is to introduce researchers, especially new ones, to the “video representation, processing, and segmentation techniques”. This includes an easy and smooth introduction, followed by principles of video structure and representation, and then a state-of-the-art of the segmentation techniques focusing on the shot-detection. Performance evaluation and common issues are also discussed before concluding the chapter.

scholarly journals 3D-generalization of impulse noise removal method for video data processing

2020 ◽  
Vol 44 (1) ◽  
pp. 92-100
Author(s):  
N.I. Chervyakov ◽  
P.A. Lyakhov ◽  
A.R. Orazaev
Keyword(s):  
Data Processing ◽  
Video Processing ◽  
Impulse Noise ◽  
Structural Similarity ◽  
Noise Removal ◽  
Video Data ◽  
Practical Applications ◽  
Iterative Processing ◽  
And Control

The paper proposes a generalized method of adaptive median impulse noise filtering for video data processing. The method is based on the combined use of iterative processing and transformation of the result of median filtering based on the Lorentz distribution. Four different combinations of algorithmic blocks of the method are proposed. The experimental part of the paper presents the results of comparing the quality of the proposed method with known analogues. Video distorted by impulse noise with pixel distortion probabilities from 1% to 99% inclusive was used for the simulation. Numerical assessment of the quality of cleaning video data from noise based on the mean square error (MSE) and structural similarity (SSIM) showed that the proposed method shows the best result of processing in all the considered cases, compared with the known approaches. The results obtained in the paper can be used in practical applications of digital video processing, for example, in systems of video surveillance, identification systems and control of industrial processes.

A Meta-Mining Ontology Framework for Data Processing

2021 ◽  
Vol 12 (2) ◽  
pp. 37-56
Author(s):  
Man Tianxing ◽  
Nataly Zhukova ◽  
Alexander Vodyaho ◽  
Tin Tun Aung
Keyword(s):  
Data Mining ◽  
Data Processing ◽  
Data Streams ◽  
Specific Data ◽  
Ontology Merging ◽  
Knowledge Models ◽  
Task Requirements ◽  
Ontology Extraction ◽  
Merging Method ◽  
And Task

Extracting knowledge from data streams received from observed objects through data mining is required in various domains. However, there is a lack of any kind of guidance on which techniques can or should be used in which contexts. Meta mining technology can help build processes of data processing based on knowledge models taking into account the specific features of the objects. This paper proposes a meta mining ontology framework that allows selecting algorithms for solving specific data mining tasks and build suitable processes. The proposed ontology is constructed using existing ontologies and is extended with an ontology of data characteristics and task requirements. Different from the existing ontologies, the proposed ontology describes the overall data mining process, used to build data processing processes in various domains, and has low computational complexity compared to others. The authors developed an ontology merging method and a sub-ontology extraction method, which are implemented based on OWL API via extracting and integrating the relevant axioms.

A Data Mining Approach for Risk Assessment in Car Insurance

2014 ◽  
Vol 5 (3) ◽  
pp. 11-28
Author(s):  
Ljiljana Kašćelan ◽  
Vladimir Kašćelan ◽  
Milijana Novović-Burić
Keyword(s):  
Data Mining ◽  
Risk Assessment ◽  
Risk Premium ◽  
Study Data ◽  
Functional Dependencies ◽  
Standard Methods ◽  
Data Mining Techniques ◽  
Data Mining Approach ◽  
The One ◽  
Car Insurance

This paper has proposed a data mining approach for risk assessment in car insurance. Standard methods imply classification of policies to great number of tariff classes and assessment of risk on basis of them. With application of data mining techniques, it is possible to get functional dependencies between the level of risk and risk factors as well as better results in predictions. On the case study data it has been proved that data mining techniques can, with better accuracy than the standard methods, predict claim sizes and occurrence of claims, and this represents the basis for calculation of net risk premium and risk classification. This paper, also, discusses advantages of data mining methods compared to standard methods for risk assessment in car insurance, as well as the specificities of the obtained results due to small insurance market, such is the one in Montenegro.

scholarly journals Novel IT Technologies on the Digital Battlefield: The Application of Big Data and Data Mining Technologies

Hadmérnök ◽  
2020 ◽  
Vol 15 (4) ◽  
pp. 141-158
Author(s):  
Eszter Katalin Bognár
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Data Processing ◽  
Relevant Information ◽  
Military Operations ◽  
Textual Data ◽  
Modern Warfare ◽  
Tools And Techniques

In modern warfare, the most important innovation to date has been the utilisation of information as a  weapon. The basis of successful military operations is  the ability to correctly assess a situation based on  credible collected information. In today’s military, the primary challenge is not the actual collection of data.  It has become more important to extract relevant  information from that data. This requirement cannot  be successfully completed without necessary  improvements in tools and techniques to support the acquisition and analysis of data. This study defines  Big Data and its concept as applied to military  reconnaissance, focusing on the processing of  imagery and textual data, bringing to light modern  data processing and analytics methods that enable  effective processing.

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