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...

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Bibliographic Details
Other Authors: Feeney, Kevin, (Editor)
Format: eBook
Language: English
Published: Aalborg : River Publishers, [2018]
Series: River Publishers series in software engineering.
Subjects:
System design.
Big data.
Agile software development.
elektronické knihy
electronic books
ISBN: 8770220158
9788770220156
1523139056
9781523139057
9781003338123
1003338127
9781000792546
1000792544
9781000795868
1000795861
8770220166
9788770220163
Physical Description: 1 online resource (436 pages)

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Table of contents

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001 kn-on1079008279
003 OCoLC
005 20240717213016.0
006 m o d
007 cr cn|||||||||
008 181215s2018 dk ob 001 0 eng d
040 |a EBLCP  |b eng  |e pn  |c EBLCP  |d YDX  |d MERUC  |d N$T  |d YDXIT  |d UPM  |d OCLCF  |d UKAHL  |d OCLCQ  |d OCLCO  |d K6U  |d IAI  |d OCLCO  |d OCLCQ  |d TYFRS  |d OCLCQ  |d SFB  |d OCLCQ  |d EBLCP  |d OCLCQ  |d TEFOD  |d OCLCQ  |d OCLCO  |d OCLCQ  |d OCLCO  |d OCLCQ  |d OCLCO  |d OCLCL  |d OCLCQ 
020 |a 8770220158 
020 |a 9788770220156  |q (electronic bk.) 
020 |a 1523139056 
020 |a 9781523139057 
020 |a 9781003338123  |q (electronic bk.) 
020 |a 1003338127  |q (electronic bk.) 
020 |a 9781000792546  |q (electronic bk. ;  |q EPUB) 
020 |a 1000792544  |q (electronic bk. ;  |q EPUB) 
020 |a 9781000795868  |q (electronic bk. ;  |q PDF) 
020 |a 1000795861  |q (electronic bk. ;  |q PDF) 
020 |z 8770220166 
020 |z 9788770220163 
024 7 |a 10.1201/9781003338123  |2 doi 
035 |a (OCoLC)1079008279  |z (OCoLC)1076240440  |z (OCoLC)1280209439  |z (OCoLC)1417771898 
245 0 0 |a Engineering agile big-data systems /  |c editors, Kevin Feeney [and seven others] 
264 1 |a Aalborg :  |b River Publishers,  |c [2018] 
300 |a 1 online resource (436 pages) 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
490 1 |a River Publishers series in software engineering 
504 |a Includes bibliographical references and index. 
505 0 |a 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 
505 8 |a 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 
505 8 |a 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 
505 8 |a 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 
505 8 |a 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 
506 |a Plný text je dostupný pouze z IP adres počítačů Univerzity Tomáše Bati ve Zlíně nebo vzdáleným přístupem pro zaměstnance a studenty 
520 |a 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. 
590 |a Knovel  |b Knovel (All titles) 
650 0 |a System design. 
650 0 |a Big data. 
650 0 |a Agile software development. 
655 7 |a elektronické knihy  |7 fd186907  |2 czenas 
655 9 |a electronic books  |2 eczenas 
700 1 |a Feeney, Kevin,  |e editor. 
776 0 8 |i Print version:  |a Feeney, Kevin.  |t Engineering Agile Big-Data Systems.  |d Aalborg : River Publishers, ©2018 
830 0 |a River Publishers series in software engineering. 
856 4 0 |u https://proxy.k.utb.cz/login?url=https://app.knovel.com/hotlink/toc/id:kpEABDS005/engineering-agile-big?kpromoter=marc  |y Full text 

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