James Welch – författare

Hugo was also an editor of the Yale Younger Poets series and a distinguished teacher and master of the personal essay. Now many of his essays have been assembled and arranged by Ripley Hugo, the poet's widow and a writer and teacher, and Lois and James Welch, writers and close friends of the poet. Together the essays constitute a compelling autobiographical narrative that takes Hugo from his lonely childhood through the war years and his working and creative life to an interview just before his death in 1982. William Matthews, also a friend of Hugo's, has written an introduction.

Sylvester Yellow Calf is a former reservation basketball star, a promising young lawyer, and a possible congressional candidate. But when a parolee ensnares him in a blackmail scheme, he'll have to decide just who he is, and what he wants.

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.

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.