Engineering agile big-data systems

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, th...

Full description

Saved in:
Bibliographic Details
Other Authors Feeney, Kevin (Editor)
Format Electronic eBook
LanguageEnglish
Published Aalborg : River Publishers, [2018]
SeriesRiver Publishers series in software engineering.
Subjects
System design.
Big data.
Agile software development.
elektronické knihy
electronic books
Online AccessFull text
ISBN8770220158
9788770220156
1523139056
9781523139057
9781003338123
1003338127
9781000792546
1000792544
9781000795868
1000795861
8770220166
9788770220163
Physical Description1 online resource (436 pages)

Cover

Table of Contents:
  • 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

Similar Items