Extracting entity-relationship diagram from a table-based legacy database

Yeh, Dowming; Li, Yuwen; Chu, William C.

doi:10.1016/j.jss.2007.07.005

Cited by 42 publications

(20 citation statements)

References 4 publications

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“…We apply reverse engineering techniques according to the specific type of data of the RS at hand. In literature, several techniques to extract a conceptual schema from a relational database [36] and from XML documents or forms [25] are described. In regard to unstructured data, the extraction of a conceptual schema is a complex task.…”

Section: In the Case Study The Results Of The Interviews Highlight Tmentioning

confidence: 99%

A Data Quality Methodology for Heterogeneous Data

Batini¹,

Barone²,

Cabitza³

et al. 2011

IJDMS

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Section: In the Case Study The Results Of The Interviews Highlight Tmentioning

confidence: 99%

A Data Quality Methodology for Heterogeneous Data

Batini¹,

Barone²,

Cabitza³

et al. 2011

IJDMS

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show abstract

“…Generally, relational databases can be classified into different application areas which can be described by DB names or mined by some reverse engineering approaches [15,16]. In a relational database, a relation (or a table) collected a type of entries (or records) with multiple fields (or columns).…”

Section: Data Schema Of Relational Databasementioning

confidence: 99%

An NDN-based Query Processing Framework for Networked Databases

Liao¹,

Teng²,

Zhang³

et al. 2017

Proceedings of the International Conference on Computer Networks and Communication Technology (CNCT 2016)

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Abstract. Nowadays, massive databases have accumulated and networked in the large-scale and distributed networks, however, in which extensive data are still hard to be directly shared to or efficiently exploited by the users as many challenges and technical designs of query processing are arising in the networks. To address these issues, a name-based query framework is proposed by utilizing Named Data Networking (NDN). Based on the principles of NDN, our query scheme transforms SQL query statements into name-based queries firstly, and then processes these queries for routing, local querying and answers integration in distributed networks. In the system, networked query algorithms process the complex queries and distributed answers with hierarchical names. The primary implementation solutions illustrated in the paper and finally the performance analysis demonstrated that the name-based query scheme is feasible and can fulfill general network design principles.

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“…Yet they have been proved not to scale properly for big loads of data, which is the most common scenario in DW systems, as pointed out, for example, in (C. Monash, 2008;Golfarelli and Rizzi, 2009). For this reason, this is still a research topic giving rise to new proposals, like (Sismanis, Brown, Haas and Reinwald, 2006;Yao and Hamilton, 2008;Yeh, Li and Chu, 2008). Furthermore, note that these approaches exclusively work over instances and they cannot easily tolerate erroneous data (that may generate fake IDs that do not really hold or overlook real ones).…”

Section: Related Workmentioning

confidence: 99%

Ontology driven search of compound IDs

Abelló

Romero

2011

Knowl Inf Syst

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Abstract. Object identification is a crucial step in most information systems. Nowadays, we have many different ways to identify entities such as surrogates, keys and object identifiers. However, not all of them guarantee the entity identity. Many works have been introduced in the literature for discovering meaningful identifiers (i.e., guaranteeing the entity identity according to the semantics of the universe of discourse), but all of them work at the logical or data level and they share some constraints inherent to the kind of approach. Addressing it at the logical level, we may miss some important data dependencies, while the cost to identify data dependencies purely at the data level may not be affordable. In this paper, we propose an approach for discovering meaningful identifiers driven by domain ontologies. In our approach, we guide the process at the conceptual level and we introduce a set of pruning rules for improving the performance by reducing the number of identifier hypotheses generated and to be verified with data. Finally, we also introduce a simulation over a case study to show the feasibility of our method.

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Extracting entity-relationship diagram from a table-based legacy database

Cited by 42 publications

References 4 publications

A Data Quality Methodology for Heterogeneous Data

A Data Quality Methodology for Heterogeneous Data

An NDN-based Query Processing Framework for Networked Databases

Ontology driven search of compound IDs

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