Data engineering for data science Gilles Dejaegere, Alberto Abelló, Kristian Torp, Alkis Simitsis, editors

Format:

Book

Contributor:

Dejaegere, Gilles, editor.

Abelló, Alberto (Computer scientist), editor.

Torp, Kristian (Of Aalborg Universitet), editor.

Simitsis, Alkis, editor.

Language:

English

Subjects (All):

Electronic data processing.

Physical Description:

1 online resource

Place of Publication:

Cham, Switzerland Springer [2026]

Summary:

"This book aims to synthesize and integrate the research challenges in data science and data engineering. It offers a comprehensive survey of the entire data management stack, from scalable and explainable data analytics to traceable data workflows. By providing a consistent framework, it facilitates a thorough understanding of the data science lifecycle, from basic definitions to state-of-the-art concepts and techniques. The book is divided into four parts, each focusing on a different aspect of the data management and science lifecycle: governance, storage and processing, preparation, and analysis. Each part is organized to provide a coherent conceptual framework and is divided into multiple chapters, each focusing on a specific topic but together offering a comprehensive overview of the state of the art and the key challenges in the respective areas. While the parts and chapters follow a logical sequence, each chapter is designed to be self-contained and can be read independently. Chapters include references for further reading and deeper exploration, and often also provide concrete examples or use cases to make the material more accessible. In addition, many chapters introduce a taxonomy to break down complex research areas into manageable components, highlighting the core directions and developments within each domain. The book is designed to be a valuable resource for both researchers and practitioners seeking to leverage data engineering for data science applications. For both seasoned experts or budding professionals, it provides the tools and knowledge needed to stay at the forefront of data-driven advancements"-- Springer Nature Link

Contents:

Text data integration / Md. Ataur Rahman, Dimitris Sacharidis, Oscar Romero, and Sergi Nadal

Exploring the landscape of data fusion / Yeasmin Ara Akter, Alberto Abelló, Petar Jovanovic, Tomer Sagi, and Katja Hose

Scalable and privacy-aware relational data synthesis / Antheas Kapenekakis, Daniele Dell’Aglio, Martin Bøgsted, Minos Garofalakis, and Katja Hose

Comprehensive approach to feature selection / Uchechukwu Fortune Njoku, Alberto Abelló, Besim Bilalli, and Gianluca Bontempi

Current systems for managing massive high frequency time series / Abduvoris Abduvakhobov, Søren Kejser Jensen, Christian Thomsen, and Esteban Zimányi

MLOps systems for developing ML pipelines / Antonios Kontaxakis, Dimitris Sacharidis, Alkis Simitsis, Alberto Abelló, and Sergi Nadal

Workload placement and scheduling on heterogeneous CPU-GPU architectures / Marcos N. L. Carvalho, Anna Queralt, Oscar Romero, and Alkis Simitsis

Privacy-preserving blockchain-based federated learning / Eros Fabrici, Besim Bilalli, Josep Lluís Berral García, and Minos Garofalakis

Example-based explainability in machine learning / Ikhtiyor Nematov, Dimitris Sacharidis, Katja Hose, and Tomer Sagi

Table search in data lakes : methods, indexing techniques, and research challenges / Ibraheem Taha, Matteo Lissandrini, Alkis Simitsis, Torben Bach Pedersen, and Yannis Ioannidis

Adversarial learning for fraud detection / Daniele Lunghi, Alkis Simitsis, and Gianluca Bontempi

Approximate and adaptive methods for inference queries / Christos C. Papadopoulos, Alkis Simitsis, and Torben Bach Pedersen

Analysis of unconstrained trajectories, the case of AIS / Song Wu, Kristian Torp, Mahmoud Sakr, and Esteban Zimányi

Network-constrained trajectory data for traffic analytics / Rodrigo Sasse David, Kristian Torp, Mahmoud Sakr, and Esteban Zimányi

Notes:

Includes bibliographical references

Online resource; title from PDF title page (Springer Nature Link, viewed June 2, 2026)

Other Format:

Print version Data engineering for data science

ISBN:

9783032187659

3032187656

OCLC:

1593376408

Access Restriction:

Some versions Open access versions available from some providers open access