The Interaction Between Schema Matching and Record Matching in Data Integration

Gu, Binbin; Li, Zhixu; Zhang, Xiangliang; Liu, An; Liu, Guanfeng; Zheng, Kai; Zhao, Lei; Zhou, Xiaofang

doi:10.1109/tkde.2016.2611577

Cited by 19 publications

(7 citation statements)

References 30 publications

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“…It was therefore not easy to find an effective knowledge model and it was very difficult to manage and group the relationships between the data attributes. Existing integration and analytical frameworks like pharmacy data record matching framework [1], biodiversity data retrieval framework [4] and, data records with schema matching framework [7] are facing many challenges in analyzing this large-scale data due to the complexity of the distribution of data and the proliferation of multi-source data. To solve this problem, and integration and classification model based on the Probability Semantic Association (PSA) of the attribute and generation of knowledge pattern is proposed for a large data source.…”

Section: Problemmentioning

confidence: 99%

“…The mechanism of data integration and classification will provide a unified platform for collective associating multiple sources of data and also enhance the capability to exchange information between various information-sharing systems as in [7] the data integration with the interaction with schema and data records relation, and in [8] the perspective of data association for integration with machine learning studies are presented. Existing data integration systems are mainly extensions of traditional databases and are structured and data can be designed utilizing a few of the conventional data models [9].…”

Section: Introductionmentioning

confidence: 99%

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Integration and classification approach based on probabilistic semantic association for big data

VandanaKolisetty

Rajput

2021

Complex Intell. Syst.

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The process of integration through classification provides a unified representation of diverse data sources in Big data. The main challenges of big data analysis are due to the various granularities, irreconcilable data models, and multipart interdependencies between data content. Previously designed models were facing problems in integrating and analyzing big data due to highly complex and dynamic multi-source and heterogeneous information variation and also in processing and classifying the association among the attributes in a schema. In this paper, we propose an integration and classification approach through designing a Probabilistic Semantic Association (PSA) method to generate the feature pattern for the sources of big data. The PSA approach is trained to understand the data association and dependency pattern between the data class and incoming data to map the data objects accurately. It initially builds a data integration mechanism by transforming data into structured and learn to utilize the trained knowledge to classify the probabilistic association among the data and knowledge patterns. Later it builds a data analysis mechanism to analyze the mapped data through PSA to evaluate the integration efficiency. An experimental evaluation is performed over a real-time crime dataset generated from multiple locations having various events classes. The analysis of results confined that the utilization of knowledge patterns of accurate classification to enhance the integration of multiple source data is appropriate. The measure of precision, recall, fall-out rate, and F-measure approve the efficiency of the proposed PSA method. Even in comparison with the state-of-art classification method and with SC-LDA algorithm shows an improvisation in the prediction accuracy and enhance the data integration.

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Section: Problemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Integration and classification approach based on probabilistic semantic association for big data

VandanaKolisetty

Rajput

2021

Complex Intell. Syst.

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“…Schema Matching. Schema matching is the process of generating correspondences between the attributes of two database schemas, for the purpose of some data integration task [14,15]. An example is the often quoted coffee consumption data found in Google Fusion Tables [16,17], which is distributed among different tables that represent a specific region [18].…”

Section: Matching Of Data Modelsmentioning

confidence: 99%

Handling probabilistic integrity constraints in pay-as-you-go reconciliation of data models

Hùng

Weidlich

Tâm

et al. 2019

Information Systems

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Data models capture the structure and characteristic properties of data entities, e.g., in terms of a database schema or an ontology. They are the backbone of diverse applications, reaching from information integration, through peer-to-peer systems and electronic commerce to social networking. Many of these applications involve models of diverse data sources. Effective utilisation and evolution of data models, therefore, calls for matching techniques that generate correspondences between their elements. Various such matching tools have been developed in the past. Yet, their results are often incomplete or erroneous, and thus need to be reconciled, i.e., validated by an expert. This paper analyses the reconciliation process in the presence of large collections of data models, where the network induced by generated correspondences shall meet consistency expectations in terms of integrity constraints. We specifically focus on how to handle data models that show some internal structure and potentially differ in terms of their assumed level of abstraction. We argue that such a setting calls for a probabilistic model of integrity constraints, for which satisfaction is preferred, but not required. In this work, we present a model for probabilistic constraints that enables reasoning on the correctness of individual correspondences within a network of data models, in order to guide an expert in the validation process. To support pay-as-you-go reconciliation, we also show how to construct a set of high-quality correspondences, even if an expert validates only a subset of all generated correspondences. We demonstrate the efficiency of our techniques for real-world datasets comprising database schemas and ontologies from various application domains.

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“…On the other hand, there are studies aimed at higher accuracy by using instance‐based schema matching. However, algorithms based on attribute values are problematic in that even different attributes get paired if they have similar values.…”

Section: Challenges Of Transport Service Schema Matchingmentioning

confidence: 99%

A schema matching approach for integrated mobility service

Hirashima

Komoda

Fujiwara

2019

Elect Comm in Japan

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We propose a schema matching method of transport service data in different schema from multiple data sources. Nowadays, open data about transport services are increasing. However, there are some challenges to use them, because they have complicated data structure such as deeply nested objects or linked small objects. And their disparate data structures make data matching more difficult. So, we present a method to make match transport services data by exploiting the data structure, terminology, and domain knowledge. The proposed method gradually adds linked objects to matching target in matching process to find corresponding attributes. Generally, wider matching target space raises false positive rate. So, our proposed method calculates similarity score of attributes taking into account reference relationships between linked objects. We experimentally evaluate the method in real‐world transport service data and confirm that the method can improve the matching accuracy.

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The Interaction Between Schema Matching and Record Matching in Data Integration

Cited by 19 publications

References 30 publications

Integration and classification approach based on probabilistic semantic association for big data

Integration and classification approach based on probabilistic semantic association for big data

Handling probabilistic integrity constraints in pay-as-you-go reconciliation of data models

A schema matching approach for integrated mobility service

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