Usage-Based Schema Matching

Elmeleegy, Hazem; Ouzzani, Mourad; Elmagarmid, Ahmed K.

doi:10.1109/icde.2008.4497410

Cited by 37 publications

(30 citation statements)

References 13 publications

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“…Moreover, there has been a trend to consider the logs query as an additional level of information for schema matching by a number of researchers and studies [6]. This new approach attempts to extract attributes usage of each targeted schema from the logs of queries concerning the schema relationships, and their features such as joins and with aggregate functions [6]. Besides, the click logs have been mainly utilized for keyword queries of an entity search engine in order to determine the terms of the search.…”

Section: A Schema Level Matchingmentioning

confidence: 99%

“…Two levels under schema level matching can be exploited to define the correspondence between attributes, which are element level and structure level. Moreover, there has been a trend to consider the logs query as an additional level of information for schema matching by a number of researchers and studies [6]. This new approach attempts to extract attributes usage of each targeted schema from the logs of queries concerning the schema relationships, and their features such as joins and with aggregate functions [6].…”

Section: A Schema Level Matchingmentioning

confidence: 99%

“…Besides, the need for intelligent decision supports tools that extract heterogeneous data to ensure the best decision for users. Identifying the correspondences (matches) between database schemas has been commonly referred to as a schema matching problem [6], [7], [9].…”

Section: Schema Information Levels IImentioning

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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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: A Schema Level Matchingmentioning

confidence: 99%

Section: A Schema Level Matchingmentioning

confidence: 99%

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A Survey of Schema Matching Research using Database Schemas and Instances

Alwan¹,

Nordin²,

Alzeber³

et al. 2017

ijacsa

View full text Add to dashboard Cite

show abstract

“…The methodology consists of two phases. In the first matching phase, a set of correspondences between source and target schema elements is solicited from the user, with possible aid from automated techniques that find similar attribute pairs [25,23,20,14,24,13,16,26,15]. During the second mapping phase, the set of matches yields an executable transformation from the source schema to the target schema.…”

Section: Match-drivenmentioning

confidence: 99%

“…Many systems utilize a combination of these two techniques, such as LSD [14], Cupid [24], COMA [13]. Usage-based methods improve matching quality by exploiting usage information, such as query logs [16] and search clicklogs [26].…”

Section: Related Workmentioning

confidence: 99%

Sample-driven schema mapping

Cafarella

Jagadish

2012

Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data

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End-users increasingly find the need to perform light-weight, customized schema mapping. State-of-the-art tools provide powerful functions to generate schema mappings, but they usually require an in-depth understanding of the semantics of multiple schemas and their correspondences, and are thus not suitable for users who are technically unsophisticated or when a large number of mappings must be performed.We propose a system for sample-driven schema mapping. It automatically constructs schema mappings, in real time, from userinput sample target instances. Because the user does not have to provide any explicit attribute-level match information, she is isolated from the possibly complex structure and semantics of both the source schemas and the mappings. In addition, the user never has to master any operations specific to schema mappings: she simply types data values into a spreadsheet-style interface. As a result, the user can construct mappings with a much lower cognitive burden.In this paper we present MWEAVER, a prototype sample-driven schema mapping system. It employs novel algorithms that enable the system to obtain desired mapping results while meeting interactive response performance requirements. We show the results of a user study that compares MWEAVER with two state-of-theart mapping tools across several mapping tasks, both real and synthetic. These suggest that the MWEAVER system enables users to perform practical mapping tasks in about 1/5th the time needed by the state-of-the-art tools.

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Service Composition for the Semantic Web

Medjahed

Bouguettaya

2011

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Usage-Based Schema Matching

Cited by 37 publications

References 13 publications

A Survey of Schema Matching Research using Database Schemas and Instances

A Survey of Schema Matching Research using Database Schemas and Instances

Sample-driven schema mapping

Service Composition for the Semantic Web

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