An instance based schema matching between opaque database schemas

Munir, Sameer; Khan, Faraz; Riaz, Muhammad Atif

doi:10.1109/ice2t.2014.7006242

Cited by 4 publications

(17 citation statements)

References 8 publications

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“…In many cases, it is not easy to obtain information from the schema structure as either it is not available or the information is meaningless and could not be used for the matching purpose [5], [10], [17]. Thus, in such cases, instances are considered as the most efficient and reliable source of information to identify the correspondences between attributes and determine the similarities and corresponding attributes of schema based on exploiting the characters of available values/instances.…”

Section: B Instance Levelmentioning

confidence: 99%

“…Examples of external sources include WordNet/Thesauri, and dictionaries can be used for identifying the semantic relationships between schema attribute names or abbreviations such as synonymy and hyponyms in order to determine the similarities if it exists [5], [13].…”

Section: Auxiliary Levelmentioning

confidence: 99%

“…Linguistic level uses meta-data information such as the attribute's names or abbreviations and available textual descriptions to find the correspondences between schemas [5], [8], [13]. While, constraint level relies on the data types of the database attributes such as (string, numeric, and char), the ranges of instances, and different types of keys (primary, super, uniqueness) [13], [16].…”

Section: A Schema Levelsmentioning

confidence: 99%

“…Nevertheless, using schema matching approach is inappropriate when databases are developed separately and without unified standards [5]. Furthermore, it is impractical to employ the schema design information "schema attributes" to determine the correspondences attributes when different abbreviations of attribute names "column's names" is used to represent the same real world entities or objects [2]- [5].…”

mentioning

confidence: 99%

“…Furthermore, it is impractical to employ the schema design information "schema attributes" to determine the correspondences attributes when different abbreviations of attribute names "column's names" is used to represent the same real world entities or objects [2]- [5].…”

mentioning

confidence: 99%

See 4 more Smart Citations

A Survey of Schema Matching Research using Database Schemas and Instances

Alwan¹,

Nordin²,

Alzeber³

et al. 2017

ijacsa

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Abstract-Schema matching is considered as one of the essential phases of data integration in database systems. The main aim of the schema matching process is to identify the correlation between schema which helps later in the data integration process. The main issue concern of schema matching is how to support the merging decision by providing the correspondence between attributes through syntactic and semantic heterogeneous in data sources. There have been a lot of attempts in the literature toward utilizing database instances to detect the correspondence between attributes during schema matching process. Many approaches based on instances have been proposed aiming at improving the accuracy of the matching process. This paper set out a classification of schema matching research in database system exploiting database schema and instances. We survey and analyze the schema matching techniques applied in the literature by highlighting the strengths and the weaknesses of each technique. A deliberate discussion has been reported highlights on challenges and the current research trends of schema matching in database. We conclude this paper with some future work directions that help researchers to explore and investigate current issues and challenges related to schema matching in contemporary databases.

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Section: B Instance Levelmentioning

confidence: 99%

Section: Auxiliary Levelmentioning

confidence: 99%

Section: A Schema Levelsmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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A Survey of Schema Matching Research using Database Schemas and Instances

Alwan¹,

Nordin²,

Alzeber³

et al. 2017

ijacsa

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A Credit Conflict Detection Model Based on Decision Distance and Probability Matrix

Zhang

Sun

2022

Wireless Communications and Mobile Computing

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Considering the credit index calculation differences, semantic differences, false data, and other problems between platforms such as Internet finance, e-commerce, and health and elderly care, which lead to the credit deviation from the trusted range of credit subjects and the lack of related information of credit subjects, in this paper, we proposed a crossplatform service credit conflict detection model based on the decision distance to support the migration and application of crossplatform credit information transmission and integration. Firstly, we give a scoring table of influencing factors. Score is the probability of the impact of this factor on credit. Through this probability, the distance matrix between influencing factors is generated. Secondly, the similarity matrix is calculated from the distance matrix. Thirdly, the support vector is calculated through the similarity matrix. Fourth, the credit vector is calculated by the support vector. Finally, the credibility is calculated by the credit vector and probability.

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An Empirical Comparative Study of Instance-based Schema Matching

Alzeber

Alwan

Nordin

et al. 2018

IJEECS

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<span lang="EN-US">The main issue concern of schema matching is how to support the merging decision by providing matching between attributes of different schemas. There have been many works in the literature toward utilizing database instances to detect the correspondence between attributes. Most of these previous works aim at improving the match accuracy. We observed that no technique managed to provide an accurate matching for different types of data. In other words, some of the techniques treat numeric values as strings. Similarly, other techniques process textual instance, as numeric, and this negatively influences the process of discovering the match and compromising the matching result. Thus, a practical comparative study between syntactic and semantic techniques is needed. The study emphasizes on analyzing these techniques to determine the strengths and weaknesses of each technique. This paper aims at comparing two different instance-based matching techniques, namely: (i) regular expression and (ii) Google similarity to identify the match between attributes. Several analyses have been conducted on real and synthetic data sets to evaluate the performance of these techniques with respect to Precision (P), Recall (R) and F-Measure.</span>

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An instance based schema matching between opaque database schemas

Cited by 4 publications

References 8 publications

A Survey of Schema Matching Research using Database Schemas and Instances

A Survey of Schema Matching Research using Database Schemas and Instances

A Credit Conflict Detection Model Based on Decision Distance and Probability Matrix

An Empirical Comparative Study of Instance-based Schema Matching

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