TY  - BOOK
AU  - Feeney,Kevin
TI  - Engineering agile big-data systems
T2  - River Publishers series in software engineering
SN  - 8770220158
AV  - QA76.9.S88 E54 2018
U1  - 004.21 23
PY  - 2018///]
CY  - Aalborg
PB  - River Publishers
KW  - System design
KW  - Big data
KW  - Agile software development
KW  - COMPUTERS
KW  - Computer Literacy
KW  - bisacsh
KW  - Computer Science
KW  - Data Processing
KW  - Hardware
KW  - General
KW  - Information Technology
KW  - Machine Theory
KW  - Reference
KW  - COMPUTERS / Programming / Software Development
KW  - COMPUTERS / Database Management / Data Mining
N1  - Front Cover; Half Title Page; RIVER PUBLISHERS SERIES IN SOFTWARE ENGINEERING; Title Page; Copyright Page; Contents; Preface; Acknowledgements; List of Contributors; List of Figures; List of Tables; List of Abbreviations; Chapter 1 -- Introduction; 1.1 State of the Art in Engineering Data-Intensive Systems; 1.1.1 The Challenge; 1.2 State of the Art in Semantics-Driven Software Engineering; 1.2.1 The Challenge; 1.3 State of the Art in Data Quality Engineering; 1.3.1 The Challenge; 1.4 About ALIGNED; 1.5 ALIGNED Partners; 1.5.1 Trinity College Dublin; 1.5.2 Oxford University -- Department of Computer Science1.5.3 Oxford University -- School of Anthropology and Museum Ethnography; 1.5.4 University of Leipzig -- Agile Knowledge Engineering and Semantic Web (AKSW); 1.5.5 Semantic Web Company; 1.5.6 Wolters Kluwer Germany; 1.5.7 Adam Mickiewicz University in Poznań; 1.5.8 Wolters Kluwer Poland; 1.6 Structure; Chapter 2 -- ALIGNED Use Cases -- Data and SoftwareEngineering Challenges; 2.1 Introduction; 2.2 The ALIGNED Use Cases; 2.2.1 Seshat: Global History Databank; 2.2.2 PoolParty Enterprise Application Demonstrator System; 2.2.3 DBpedia; 2.2.4 Jurion and Jurion IPG2.2.5 Health Data Management; 2.3 The ALIGNED Use Cases and Data Life Cycle. Major Challenges and Offered Solutions; 2.4 The ALIGNED Use Cases and Software Life Cycle. Major Challenges and Offered Solutions; 2.5 Conclusions; Chapter 3 -- Methodology; 3.1 Introduction; 3.2 Software and Data Engineering Life Cycles; 3.2.1 Software Engineering Life Cycle; 3.2.2 Data Engineering Life Cycle; 3.3 Software Development Processes; 3.3.1 Model-Driven Approaches; 3.3.2 Formal Techniques; 3.3.3 Test-Driven Development; 3.4 Integration Points and Harmonisation; 3.4.1 Integration Points3.4.2 Barriers to Harmonisation; 3.4.3 Methodology Requirements; 3.5 An ALIGNED Methodology; 3.5.1 A General Framework for Process Management; 3.5.2 An Iterative Methodology and Illustration; 3.6 Recommendations; 3.6.1 Sample Methodology; 3.7 Sample Synchronisation Point Activities; 3.7.1 Model Catalogue: Analysis and Search/Browse/Explore; 3.7.2 Model Catalogue: Design and Classify/Enrich; 3.7.3 Semantic Booster: Implementation and Store/Query; 3.7.4 Semantic Booster: Maintenance and Search/Browse/Explore; 3.8 Summary; 3.8.1 Related Work; 3.9 Conclusions; Chapter 4 -- ALIGNED MetaModel Overview4.1 Generic Metamodel; 4.1.1 Basic Approach; 4.1.2 Namespaces and URIs; 4.1.3 Expressivity of Vocabularies; 4.1.4 Reference Style for External Terms; 4.1.5 Links with W3C PROV; 4.2 ALIGNED Generic Metamodel; 4.2.1 Design Intent Ontology (DIO); 4.3 Software Engineering; 4.3.1 Software Life Cycle Ontology; 4.3.2 Software Implementation Process Ontology (SIP); 4.4 Data Engineering; 4.4.1 Data Life Cycle Ontology; 4.5 DBpedia DataID (DataID); 4.6 Unified Quality Reports; 4.6.1 Reasoning Violation Ontology (RVO) Overview; 4.6.2 W3C SHACL Reporting Vocabulary
N2  - 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
UR  - https://www.taylorfrancis.com/books/9781003338123
UR  - http://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf
ER  -