- author = "Moises Gomes {de Carvalho}",
- title = "Evolutionary Approaches to Data Integration Related Problems",
- school = "Computer Science of the Federal University of Minas Gerais",
- year = "2009",
- address = "Belo Horizonte, Brazil",
- month = "26 " # oct,
- keywords = "genetic algorithms, genetic programming, Data Integration, Record Deduplication, Schema Matching",
- keywords_pt = "Programacao genetica, Integracao de dados, Deduplicacao de registros",
-
URL = "
http://www.dcc.ufmg.br/pos/cursos/defesas/901D.PDF",
- size = "138 pages",
-
abstract = "Data integration aims to combine data from different
sources (data repositories such as databases, digital
libraries, etc.) by adopting a global data model and by
detecting and resolving schema and data conflicts so
that a homogeneous, unified view can be provided. Two
specific problems related to data integration - schema
matching and replica identification - present a large
solution space. This space is computationally expensive
and technically prohibitive to be intensively and
exhaustively explored by traditional approaches.
Moreover, the solutions for these problems usually
require that multiple, sometimes conflicting,
objectives must be simultaneously attended. This thesis
aims to show that evolutionary-based techniques can be
successfully applied to such problems, leading to novel
approaches and methods that address all aforementioned
requirements and, at the same time, provide efficient
and high accuracy solutions.
In this thesis, we first propose a genetic programming approach to record deduplication. This approach combines several different pieces of evidence extracted from the actual data present in the repositories to suggest a deduplication function that is able to identify whenever two entries in a repository are replicas or not. As shown by our experiments, our approach outperforms existing state-of-the-art methods found in the literature. Moreover, the suggested function is computationally less demanding since it uses fewer evidence. Finally, it is also important to notice that our approach is capable of automatically adapting to a given fixed replica identification boundary, freeing the user from the burden of having to choose and tune this parameter
Based on the previous approach, we also devised a novel evolutionary approach, that is able to automatically find complex schema matches. Our aim was to develop a method to find semantic relationships between schema elements, in a restricted scenario in which only the data instances are available. To the best of our knowledge, this is the first approach that is capable of discovering complex schema matches using only the data instances, which is performed by exploiting record deduplication and information retrieval techniques to find schema matches during the evolutionary process. To demonstrate the effectiveness of our approach, we conducted an experimental evaluation using real-world and synthetic datasets. Our results show that our approach is able to find complex matches with high accuracy, despite using only the data instances.",
- notes = "supervisor: Alberto Henrique Frade Laender",
