Please use this identifier to cite or link to this item:
http://hdl.handle.net/11375/25386
Title: | Restoring Consistency in Ontological Multidimensional Data Models via Weighted Repairs |
Authors: | Haque, Enamul |
Advisor: | Chiang, Fei |
Department: | Computing and Software |
Keywords: | Logic;Data Science;Data Cleaning;MCDM;CRITIC;OMD;Genetic Algorithms;Database;Datalog |
Publication Date: | 2020 |
Abstract: | High data quality is a prerequisite for accurate data analysis. However, data inconsistencies often arise in real data, leading to untrusted decision making downstream in the data analysis pipeline. In this research, we study the problem of inconsistency detection and repair of the Ontology Multi-dimensional Data Model (OMD). We propose a framework of data quality assessment, and repair for the OMD. We formally define a weight-based repair-by-deletion semantics, and present an automatic weight generation mechanism that considers multiple input criteria. Our methods are rooted in multi-criteria decision making that consider the correlation, contrast, and conflict that may exist among multiple criteria, and is often needed in the data cleaning domain. After weight generation we present a dynamic programming based Min-Sum algorithm to identify minimal weight solution. We then apply evolutionary optimization techniques and demonstrate improved performance using medical datasets, making it realizable in practice. |
Description: | This can be considered as a multidisciplinary research where ideas from Operations Research, Data Science and Logic came together to solve an inconsistency handling problem in a special type of ontology. |
URI: | http://hdl.handle.net/11375/25386 |
Appears in Collections: | Open Access Dissertations and Theses |
Files in This Item:
File | Description | Size | Format | |
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Haque_Enamul_2020April_MSc.pdf | Thesis Data Science | 2.93 MB | Adobe PDF | View/Open |
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