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oapen-20.500.12657-597492022-11-29T03:32:15Z Engineering Agile Big-Data Systems Feeney, Kevin Davies, Jim Welch, James Computer programming / software engineering Data mining bic Book Industry Communication::U Computing & information technology::UM Computer programming / software development bic Book Industry Communication::U Computing & information technology::UN Databases::UNF Data mining 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. 2022-11-28T16:04:14Z 2022-11-28T16:04:14Z 2018 book ONIX_20221128_9781000795868_33 9781000795868 9781003338123 9788770220163 https://library.oapen.org/handle/20.500.12657/59749 eng application/pdf n/a 9781000795868.pdf Taylor & Francis River Publishers 10.1201/9781003338123 10.1201/9781003338123 7b3c7b10-5b1e-40b3-860e-c6dd5197f0bb 3983007a-5726-4f1e-b9df-3fbc771f2916 9781000795868 9781003338123 9788770220163 River Publishers 302 [...] European Commission European Union open access
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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.
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