SHRDIS: A Semantic-Based Heterogeneous Relational Data Integration System

Integrating and querying data from heterogeneous sources is a hot research topic in database research field. The emergence of the Semantic Web brings new paradigm shift of computing in data integration research where data is heterogeneous and distributed. To solve the problem this paper proposes a semantic-based approach. A Semantic-based Heterogeneous Relational Data Integration System, called SHRDIS, is presented. In this system, ontology is used as the mediated schema for the representation of the data source semantics. SPARQL queries over global schema are rewritten into local SQL queries that can be executed on heterogeneous relational databases. The architecture and implementation of SHRDIS is illustrated in detail. The experiment results show that the SHRDIS system has nice performance and scalability.

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Design of Multimedia Vocal Music Education Data Integration System Based on Adaptive Genetic Algorithm

Security and Communication Networks ◽

10.1155/2021/3897696 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Hao-Lan Zhang ◽

Peng-Jiang Yu

Keyword(s):

Genetic Algorithm ◽

Data Integration ◽

Music Education ◽

Vocal Music ◽

Adaptive Genetic Algorithm ◽

Integration System ◽

Application Performance ◽

Education Data ◽

Data Integration System ◽

Data Source

In order to solve the problems of low accuracy of data integration results, low integration efficiency, and easy confusion between different types of data in traditional methods, a multimedia vocal education data integration system based on adaptive genetic algorithm was designed. Specifically, the designed system is divided into three parts: data source management module, system administrator module, and database management module. The synchronized multimedia vocal education data are first processed by the synchronous multimedia vocal education data processing and then integrated by an adaptive genetic algorithm. The experimental results show that the longest data transmission time of the system is 2.3 s, which is much lower than that of the traditional method, and the accuracy of the integration result is higher, and the probability of data integration confusion is lower, which all indicate that the designed system has better application performance.

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Reasoning about Disclosure in Data Integration in the Presence of Source Constraints

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2019/215 ◽

2019 ◽

Author(s):

Michael Benedikt ◽

Pierre Bourhis ◽

Louis Jachiet ◽

Michaël Thomazo

Keyword(s):

Data Integration ◽

Upper Bounds ◽

Sensitive Information ◽

Lower And Upper Bounds ◽

Multiple Sources ◽

Integration System ◽

Global Schema ◽

Data Integration System ◽

Access Data

Data integration systems allow users to access data sitting in multiple sources by means of queries over a global schema, related to the sources via mappings. Datasources often contain sensitive information, and thus an analysis is needed to verify that a schema satisfies a privacy policy, given as a set of queries whose answers should not be accessible to users. Such an analysis should take into account not only knowledge that an attacker may have about the mappings, but also what they may know about the semantics of the sources.In this paper, we show that source constraints can have a dramatic impact on disclosure analysis. We study the problem of determining whether a given data integration system discloses a source query to an attacker in the presence of constraints, providing both lower and upper bounds on source-aware disclosure analysis.

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Implementation of Ontology Extraction Oriented to Non-Normalized Database

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.756-759.1489 ◽

2013 ◽

Vol 756-759 ◽

pp. 1489-1493

Author(s):

Shi Chao Cui ◽

Ying Li ◽

Yong Bin Wang

Keyword(s):

Normal Form ◽

Data Integration ◽

Relational Database ◽

Relational Databases ◽

Extraction Methods ◽

Ontology Learning ◽

Integration System ◽

Ontology Extraction ◽

Data Integration System ◽

System Interoperability

Ontology has be applied in many fields, such as data integration, system interoperability, etc. At present, there are many ontology extraction methods based on relational database, but they all assume that the database schema is at least in third normal form (3NF). A set of improved rules to extract ontology is presented in this paper. The ontology generated is described with the OWL language. Compared with other existing methods, this approach can be able to identify a variety of strategies about non-normalized design of relational databases and deal with them, while none of existing methods can identify all of them. The results show that our method provides a more accurate process of ontology learning from non-normalized relational database.

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