An Integrated Ontology-Based Approach for Patent Classification in Medical Engineering

Geisler, Sandra; Quix, Christoph; Hai, Rihan; Alekh, Sanchit

doi:10.1007/978-3-319-69751-2_5

Cited by 3 publications

(3 citation statements)

References 19 publications

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“…In addition, users can transform data in the data lake to their desired structure and format. The information retrieval requirements of external applications are supported via RESTful APIs [55].…”

Section: Heterogeneous Data Queryingmentioning

confidence: 99%

“…In essence, a data lake is a flexible, scalable data storage and management system, which ingests and stores raw data from heterogeneous sources in their original format, and provides maintenance, query processing and data analytics in an on-the-fly manner, with the help of rich metadata [116], [138], [142], [143]. Data lakes are proposed to store and manage data in many real-life use cases: Internet of things (IoT) and smart city [99], manufacturing [112], medicine [42], [55], [114], mobility service (e.g., Uber) [50], biology [23], smart grids [20], [103], air quality control [145], flights data [96], disease control, labor markets and products [13].…”

Section: Introductionmentioning

confidence: 99%

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Data Lakes: A Survey of Functions and Systems

Hai

Koutras

Quix

et al. 2023

IEEE Trans. Knowl. Data Eng.

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Data lakes are becoming increasingly prevalent for big data management and data analytics. In contrast to traditional 'schema-on-write' approaches such as data warehouses, data lakes are repositories storing raw data in its original formats and providing a common access interface. Despite the strong interest raised from both academia and industry, there is a large body of ambiguity regarding the definition, functions and available technologies for data lakes. A complete, coherent picture of data lake challenges and solutions is still missing. This survey reviews the development, architectures, and systems of data lakes. We provide a comprehensive overview of research questions for designing and building data lakes. We classify the existing approaches and systems based on their provided functions for data lakes, which makes this survey a useful technical reference for designing, implementing and deploying data lakes. We hope that the thorough comparison of existing solutions and the discussion of open research challenges in this survey will motivate the future development of data lake research and practice.

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Section: Heterogeneous Data Queryingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Data Lakes: A Survey of Functions and Systems

Hai

Koutras

Quix

et al. 2023

IEEE Trans. Knowl. Data Eng.

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Section: Query Heterogeneous Datamentioning

confidence: 99%

Data lake concept and systems: a survey

Hai¹,

Koutras²,

Quix³

et al. 2021

Preprint

Self Cite

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Although big data has been discussed for some years, it still has many research challenges, especially the variety of data. It poses a huge difficulty to efficiently integrate, access, and query the large volume of diverse data in information silos with the traditional 'schema-on-write' approaches such as data warehouses. Data lakes have been proposed as a solution to this problem. They are repositories storing raw data in its original formats and providing a common access interface. This survey reviews the development, definition, and architectures of data lakes. We provide a comprehensive overview of research questions for designing and building data lakes. We classify the existing data lake systems based on their provided functions, which makes this survey a useful technical reference for designing, implementing and applying data lakes. We hope that the thorough comparison of existing solutions and the discussion of open research challenges in this survey would motivate the future development of data lake research and practice.

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