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Big Data Management
. Ed(S): Garcia Marquez, Fausto Pedro; Lev, Benjamin
€ 180.98
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Description for Big Data Management
Hardback. Editor(s): Garcia Marquez, Fausto Pedro; Lev, Benjamin. Num Pages: 267 pages, 69 black & white illustrations, 38 colour illustrations, biography. BIC Classification: KJQ; UMB; UNA; UYZM. Category: (P) Professional & Vocational. Dimension: 235 x 155. Weight in Grams: 611.
This book focuses on the analytic principles of business practice and big data. Specifically, it provides an interface between the main disciplines of engineering/technology and the organizational and administrative aspects of management, serving as a complement to books in other disciplines such as economics, finance, marketing and risk analysis. The contributors present their areas of expertise, together with essential case studies that illustrate the successful application of engineering management theories in real-life examples.
Product Details
Format
Hardback
Publication date
2016
Publisher
Springer International Publishing AG Switzerland
Number of pages
267
Condition
New
Number of Pages
267
Place of Publication
Cham, Switzerland
ISBN
9783319454979
SKU
V9783319454979
Shipping Time
Usually ships in 5 to 9 working days
Ref
99-3
About . Ed(S): Garcia Marquez, Fausto Pedro; Lev, Benjamin
Dr. Fausto Pedro García Márquez completed his European Doctorate in Engineering at the University of Castilla-La Mancha (UCLM) in 2004. He received his Engineering degree from the University of Murcia, Spain in 1998, and his Technical Engineering degree at UCLM in 1995 and degree in Business Administration and Management at UCLM in 2006. He has also served as Technician in ... Read more
Reviews for Big Data Management
“This book is definitely timely with its ambitious goals to fulfill a big gap in the literature. … this book provides a valuable collection of perspectives that demonstrate the complexity and diversity of big data, and open avenues for future research. … This book will be particularly welcome by researchers who are interested in an interdisciplinary approach to big data ... Read more