Engineering Agile Big-Data Systems
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€104.99
A01=James Welch
A01=Jim Davies
A01=Kevin Feeney
academic case studies
Age Group_Uncategorized
Age Group_Uncategorized
agile methodology for data-intensive systems
Author_James Welch
Author_Jim Davies
Author_Kevin Feeney
automated system customization
automatic-update
Business Processes
Category1=Non-Fiction
Category=UMZ
Category=UNF
Category=UY
COP=Denmark
Data Engineering
Data Engineering Processes
Data Engineers
Data Intensive Systems
Data Life Cycle
Data Model
data modeling
Delivery_Pre-order
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eq_computing
eq_isMigrated=2
eq_nobargain
eq_non-fiction
Eric Miller
Floating Point Operation
Health Research Data
IPG
Language_English
MDE
Model Catalogue
Owl Dl
Owl Ontology
PA=Temporarily unavailable
Pilot Study
policy-driven systems
Price_€50 to €100
PS=Active
RDF Format
RDF Triple
requirements engineering
SKOS Concept
softlaunch
Software Development Process
software lifecycle management
Source Dataset
SPARQL Queries
Triple Store
Unified Governance
Wolters Kluwer
Product details
- ISBN 9788770220163
- Weight: 960g
- Dimensions: 156 x 234mm
- Publication Date: 31 Jul 2018
- Publisher: River Publishers
- Publication City/Country: DK
- Product Form: Hardback
- Language: English
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To be effective, data-intensive systems require extensive ongoing customisation to reflect changing user requirements, organisational policies, and the structure and interpretation of the data they hold. Manual customisation is expensive, time-consuming, and error-prone. In large complex systems, the value of the data can be such that exhaustive testing is necessary before any new feature can be added to the existing design. In most cases, the precise details of requirements, policies and data will change during the lifetime of the system, forcing a choice between expensive modification and continued operation with an inefficient design.Engineering Agile Big-Data Systems outlines an approach to dealing with these problems in software and data engineering, describing a methodology for aligning these processes throughout product lifecycles. It discusses tools which can be used to achieve these goals, and, in a number of case studies, shows how the tools and methodology have been used to improve a variety of academic and business systems.
Kevin Feeney, Jim Davies, James Welch
