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Instance-based attribute identification in database integration   总被引:3,自引:0,他引:3  
Most research on attribute identification in database integration has focused on integrating attributes using schema and summary information derived from the attribute values. No research has attempted to fully explore the use of attribute values to perform attribute identification. We propose an attribute identification method that employs schema and summary instance information as well as properties of attributes derived from their instances. Unlike other attribute identification methods that match only single attributes, our method matches attribute groups for integration. Because our attribute identification method fully explores data instances, it can identify corresponding attributes to be integrated even when schema information is misleading. Three experiments were performed to validate our attribute identification method. In the first experiment, the heuristic rules derived for attribute classification were evaluated on 119 attributes from nine public domain data sets. The second was a controlled experiment validating the robustness of the proposed attribute identification method by introducing erroneous data. The third experiment evaluated the proposed attribute identification method on five data sets extracted from online music stores. The results demonstrated the viability of the proposed method.Received: 30 August 2001, Accepted: 31 August 2002, Published online: 31 July 2003Edited by L. Raschid  相似文献
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Very little research in knowledge discovery has studied how to incorporate statistical methods to automate linear correlation discovery (LCD). We present an automatic LCD methodology that adopts statistical measurement functions to discover correlations from databases’ attributes. Our methodology automatically pairs attribute groups having potential linear correlations, measures the linear correlation of each pair of attribute groups, and confirms the discovered correlation. The methodology is evaluated in two sets of experiments. The results demonstrate the methodology’s ability to facilitate linear correlation discovery for databases with a large amount of data.  相似文献
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A knowledge-based system, called the Knowledge Extraction System (KES), is presented which performs the process of reverse engineering of relational databases. KES generates an extended entity-relationship (EER) model from a relational database. Within its extraction procedure, domain semantics are obtained by analyzing the data schema and data instances of an existing database, by using heuristics, or asking the user. Relations and attributes are classified into several categories and then converted into the corresponding modelling structures of the EER model. KES demonstrates how knowledge-based system technology can be applied to ease the work of database reverse engineering. It also illustrates that the reverse engineering process can be implemented at a high level of automation. To do so, KES is integrated with the target database management system so that data can be analyzed directly through dynamic SQL queries.  相似文献
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