Knowledge Management in the Development of Data-Intensive Systems

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Augmented Analytics
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B01=Bedir Tekinerdogan
B01=Bruce R. Maxim
B01=Ivan Mistrik
B01=Matthias Galster
Big Data
Big Data Applications
Big Data Systems
Business process data
Category1=Non-Fiction
Category=KC
Category=KJMK
Category=KJMV3
Category=KJMV5
Category=UMZ
Category=UN
Category=UY
Cloud Computing
Cloud Monitoring
Cloud Providers
cloud service integration
Confront developing
COP=United Kingdom
Data Intensive Systems
data lifecycle management
Data Set
Data-intensive software systems
Decision Forest
Deep Learning Algorithms
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domain-specific modeling
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Flexible environments
human-centric knowledge modeling in software
Knowledge Acquisition
Knowledge management
Language_English
Machine Learning
Machine Learning Algorithms
Natural Language Processing
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Price_€50 to €100
Product Line Architectures
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real-time analytics
Reference Architectures
Smart Home
softlaunch
Software
Software Artifacts
software engineering methods
Software Intensive System
Software Systems
system architecture variability
Technical Debt
Virtual Screening
Virtual Screening Workflow

Product details

  • ISBN 9781032015972
  • Weight: 453g
  • Dimensions: 178 x 254mm
  • Publication Date: 25 Sep 2023
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Paperback
  • Language: English
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Data-intensive systems are software applications that process and generate Big Data. Data-intensive systems support the use of large amounts of data strategically and efficiently to provide intelligence. For example, examining industrial sensor data or business process data can enhance production, guide proactive improvements of development processes, or optimize supply chain systems. Designing data-intensive software systems is difficult because distribution of knowledge across stakeholders creates a symmetry of ignorance, because a shared vision of the future requires the development of new knowledge that extends and synthesizes existing knowledge.

Knowledge Management in the Development of Data-Intensive Systems addresses new challenges arising from knowledge management in the development of data-intensive software systems. These challenges concern requirements, architectural design, detailed design, implementation and maintenance. The book covers the current state and future directions of knowledge management in development of data-intensive software systems. The book features both academic and industrial contributions which discuss the role software engineering can play for addressing challenges that confront developing, maintaining and evolving systems;data-intensive software systems of cloud and mobile services; and the scalability requirements they imply. The book features software engineering approaches that can efficiently deal with data-intensive systems as well as applications and use cases benefiting from data-intensive systems.

Providing a comprehensive reference on the notion of data-intensive systems from a technical and non-technical perspective, the book focuses uniquely on software engineering and knowledge management in the design and maintenance of data-intensive systems. The book covers constructing, deploying, and maintaining high quality software products and software engineering in and for dynamic and flexible environments. This book provides a holistic guide for those who need to understand the impact of variability on all aspects of the software life cycle. It leverages practical experience and evidence to look ahead at the challenges faced by organizations in a fast-moving world with increasingly fast-changing customer requirements and expectations.