Simitsis Alkis (14 results)

Condition: New.

Condition: New.

Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This open access 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.…

Hardcover. Condition: Brand New. 383 pages. 6.14x0.88x9.21 inches. In Stock.

Condition: new. Questo è un articolo print on demand.

Hardcover. Condition: new. Hardcover. This open access 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. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. …

Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This open access 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. 362 pp. Englisch.…

Condition: New. Print on Demand.

Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Up to date results Fast track conference proceedings State of the art researchThis book constitutes the refereed proceedings of workshops, held at the 30th International Conference on Conceptual Modeling, ER 2011, in Brussels, Belgi.…

Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt.

Condition: New. PRINT ON DEMAND.

Hardcover. Condition: new. Hardcover. This open access 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. …

Buch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This open access 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 Customer Service Center GmbH, Europaplatz 3,69115 Heidelberg, Germany, Heidelberg 388 pp. Englisch.…

Hardcover. Condition: new. Hardcover. This open access 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. It offers a comprehensive survey of the entire data management stack, from scalable and explainable data analytics to traceable data workflows. mso-ansi-language: EN-US;">The book is designed to be a valuable resource for both researchers and practitioners seeking to leverage data engineering for data science applications. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. …