On Enriching User-Centered Data Integration Schemas in Service Lakes

Alili, Hiba; Belhajjame, Khalid; Grigori, Daniela; Drira, Rim; Ghezala, Henda Ben

doi:10.1007/978-3-319-59336-4_1

Cited by 7 publications

(4 citation statements)

References 7 publications

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“…Thereafter, it enriches the underlying schema with the determined missing concepts and associated attributes in order to enable the storage of new data when needed. This contribution has been published in the International Conference Business Information Systems (BIS 2017) [8].…”

Section: Discussionmentioning

confidence: 99%

Quality Based Data Integration for Enriching User Data Sources in Service Lakes

Alili

Belhajjame

Drira

et al. 2018

2018 IEEE International Conference on Web Services (ICWS)

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In the Big Data era, companies are moving away from traditional data-warehouse solutions whereby expensive and time-consuming ETL (Extract, Transform, Load) processes are used, towards data lakes in order to manage their increasingly growing data. Yet the stored knowledge in companies' databases, even though in the constructed data lakes, can never be complete and up-to-date, because of the continuous production of data. Local data sources often need to be augmented and enriched with information coming from external data sources. Unfortunately, the data enrichment process is one of the manual labors undertaken by experts who enrich data by adding information based on their expertise or select relevant data sources to complete missing information. Such work can be tedious, expensive and time-consuming, making it very promising for automation.

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Section: Discussionmentioning

confidence: 99%

Quality Based Data Integration for Enriching User Data Sources in Service Lakes

Alili

Belhajjame

Drira

et al. 2018

2018 IEEE International Conference on Web Services (ICWS)

Self Cite

View full text Add to dashboard Cite

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“…Next, we discuss how we believe our model contributes to help mitigating the challenges. [66] 1 ✓ Haller and Lenz (2020) [67] 1 ✓ Hai and Quix (2019) [68] 1 ✓…”

Section: Related Workmentioning

confidence: 99%

Untitled

2022

IJDKP

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When we work in a data lake, data integration is not easy, mainly because the data is usually stored in raw format. Manually performing data integration is a time-consuming task that requires the supervision of a specialist, which can make mistakes or not be able to see the optimal point for data integration among two or more datasets. This paper presents a model to perform heterogeneous in-memory data integration in a Hadoop-based data lake based on a top-k set similarity approach. Our main contribution is the process of ingesting, storing, processing, integrating, and visualizing the data integration points. The algorithm for data integration is based on the Overlap coefficient since it presented better results when compared with the set similarity metrics Jaccard, Sørensen-Dice, and the Tversky index. We tested our model applying it on eight bioinformatics-domain datasets. Our model presents better results when compared to an analysis of a specialist, and we expect our model can be reused for other domains of datasets.

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“…Prototype: 1 We implemented our proposal with Java 1.8 and the following components: the Genlink implementation from the Silk Framework 2.6.0 to generate link rules, Jena TDB 3.2.0 to work with RDF data, ARQ 3.2.0 to work with SPARQL queries, and Simmetrics 1.6.2, SecondString 2013-05-02, and JavaS-tringSimilarity 1.0.1 to compute string similarities.…”

Section: Computing Facilitymentioning

confidence: 99%

“…The feasibility of many emerging business relies on the availability and interoperability of suitable on-line datasets [1]. The Web of Data provides business with islands of data (availability) that can be transparently used as required by the business models (interoperability).…”

Section: Introductionmentioning

confidence: 99%

On Feeding Business Systems with Linked Resources from the Web of Data

Cimmino

Corchuelo

2018

Business Information Systems

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Business systems that are fed with data from the Web of Data require transparent interoperability. The Linked Data principles establish that different resources that represent the same real-world entities must be linked for such purpose. Link rules are paramount to transparent interoperability since they produce the links between resources. State-of-the-art link rules are learnt by genetic programming and build on comparing the values of the attributes of the resources. Unfortunately, this approach falls short in cases in which resources have similar values for their attributes, but represent different real-world entities. In this paper, we present a proposal that leverages a genetic programming that learns link rules and an ad-hoc filtering technique that boosts them to decide whether the links that they produce must be selected or not. Our analysis of the literature reveals that our approach is novel and our experimental analysis confirms that it helps improve the F1 score by increasing precision without a significant penalty on recall.Supported by the Spanish R&D programme (grants TIN2013-40848-R and TIN2016-75394-R). The computing facilities were provided by the Andalusian Scientific Computing Centre (CICA). We are grateful to Dr. Carlos R. Rivero and Dr. David Ruiz for earlier ideas that led to the results in this paper. We also thank Dr. Francisco Herrera for his hints on statistical analyses and sharing his software with us.

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On Enriching User-Centered Data Integration Schemas in Service Lakes

Cited by 7 publications

References 7 publications

Quality Based Data Integration for Enriching User Data Sources in Service Lakes

Quality Based Data Integration for Enriching User Data Sources in Service Lakes

Untitled

On Feeding Business Systems with Linked Resources from the Web of Data

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