TY - GEN

T1 - Uniform Access to Multiform Data Lakes using Semantic Technologies

AU - Mami, Mohamed Nadjib

AU - Graux, Damien

AU - Scerri, Simon

AU - Jabeen, Hajira

AU - Auer, Soren

AU - Lehmann, Jens

N1 - Funding Information: This work is partly supported by the EU H2020 projects BETTER (GA 776280) and QualiChain (GA 822404); and by the ADAPT Centre for Digital Content Technology funded under the SFI Research Centres Programme (Grant 13/RC/2106) and co-funded under the European Regional Development Fund.

PY - 2019/12/2

Y1 - 2019/12/2

N2 - Increasing data volumes have extensively increased application possibilities. However, accessing this data in an ad hoc manner remains an unsolved problem due to the diversity of data management approaches, formats and storage frameworks, resulting in the need to effectively access and process distributed heterogeneous data at scale. For years, SemanticWeb techniques have addressed data integration challenges with practical knowledge representation models and ontology-based mappings. Leveraging these techniques, we provide a solution enabling uniform access to large, heterogeneous data sources, without enforcing centralization; thus realizing the vision of a Semantic Data Lake. In this paper, we define the core concepts underlying this vision and the architectural requirements that systems implementing it need to fulfill. Squerall, an example of such a system, is an extensible framework built on top of state-ofthe- A rt Big Data technologies. We focus on Squerall's distributed query execution techniques and strategies, empirically evaluating its performance throughout its various sub-phases.

AB - Increasing data volumes have extensively increased application possibilities. However, accessing this data in an ad hoc manner remains an unsolved problem due to the diversity of data management approaches, formats and storage frameworks, resulting in the need to effectively access and process distributed heterogeneous data at scale. For years, SemanticWeb techniques have addressed data integration challenges with practical knowledge representation models and ontology-based mappings. Leveraging these techniques, we provide a solution enabling uniform access to large, heterogeneous data sources, without enforcing centralization; thus realizing the vision of a Semantic Data Lake. In this paper, we define the core concepts underlying this vision and the architectural requirements that systems implementing it need to fulfill. Squerall, an example of such a system, is an extensible framework built on top of state-ofthe- A rt Big Data technologies. We focus on Squerall's distributed query execution techniques and strategies, empirically evaluating its performance throughout its various sub-phases.

KW - Big Data

KW - Data Variety

KW - NoSQL

KW - Semantic Data Lake

KW - SPARQL

UR - http://www.scopus.com/inward/record.url?scp=85117539584&partnerID=8YFLogxK

U2 - 10.1145/3366030.3366054

DO - 10.1145/3366030.3366054

M3 - Conference contribution

T3 - ACM International Conference Proceeding Series

BT - 21st International Conference on Information Integration and Web-Based Applications and Services, iiWAS 2019 - Proceedings

A2 - Indrawan-Santiago, Maria

A2 - Pardede, Eric

A2 - Salvadori, Ivan Luiz

A2 - Steinbauer, Matthias

A2 - Khalil, Ismail

A2 - Anderst-Kotsis, Gabriele

PB - Association for Computing Machinery (ACM)

T2 - 21st International Conference on Information Integration and Web-Based Applications and Services, iiWAS 2019

Y2 - 2 December 2019 through 4 December 2019

ER -