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- W2981107697 abstract "One of the important challenges in big data era is veracity. It means that the data sources are of widely differing qualities, with significant differences in the coverage, accuracy and timeliness of data provided. Therefore, different and conflicting descriptions for a single entity may be found. The aim of conflict resolution in the context of data integration is to identify the true values from among different and conflicting claims about a single entity, provided by different data sources. Most data fusion methods for resolving conflicts between objects are based on two estimated parameters: the truthfulness of data and the trustworthiness of sources. The relations between objects are however an additional source of information that can be used in conflict resolution. The main challenge in these methods is deciding about the existence of relation between entities. Previous methods focus only on apparent characteristics of entities and use a similarity function for drawing inference about relations. In this paper, we propose a method that the relationship between the entities can be estimated by mapping the data to an embedding space and creating enhanced and more informative features and then clustering it. Then the true values are determined by defining a confidence score based on the distance between data and center of clusters. We evaluated this approach on three categories of data, synthetic, simulated and real data. The evaluation results showed that our proposed approach outperforms existing conflict resolution techniques, especially where there are few reliable sources." @default.
- W2981107697 created "2019-10-25" @default.
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- W2981107697 date "2019-01-01" @default.
- W2981107697 modified "2023-10-14" @default.
- W2981107697 title "Unsupervised Deep Learning for Conflict Resolution in Big Data Analysis" @default.
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- W2981107697 doi "https://doi.org/10.1007/978-3-030-33495-6_4" @default.
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