The science of datalogy

  title={The science of datalogy},
  author={Peter Naur},
  journal={Commun. ACM},
  • P. Naur
  • Published 1 July 1966
  • Computer Science
  • Commun. ACM
COHOL OI' ;lLGOL " [forum. XN 9, 1 (Jan. 19(Xi), 31-353 cot~cerneti itself with those c:LsCs where it was desirable, if not, m;ltld:~tor?F, to evnminc all possible corrlt>irl:lt,ions of conditions. There are cases where only : b few of thcsc combinations arc me:kningful, :~nd :~ll of the rem:~inder are meaningless :~,nd should le:id to ; I single :~cf ion, which could be a diagnost ic. It, has occnrreci t, o me t,htlt the method of my article can easily be extctndetl to cover such C:LSCS. T O… 
Angelic and demonic visitation: school memories
This essay considers how computing education can transform these errors into shared learning journeys by refining the relationship between programmer and user by changing data representation in the refinement calculus.
Data Science
Discussion of “Experiences with big data: Accounts from a data scientist’s perspective”
Abstract Kulachi, Frumosu, Khan, Rensch and Sponner have initiated an important discussion about the implications of Big Data on production analytics within Industry 4.0. Throughout their discussion,
Artificial Intelligence: theoretical, formative and communicative challenges of dati cation
This document explores, based on the recognition and definition of the new digital paradigm, the following topics: first, the need to catalog new skills and abilities for emerging professions in
IBM Watson Studio: A Platform to Transform Data to Intelligence
Data Science (Tukey in Exploratory data analysis: Past, present, and future) is fast emerging as the inter-disciplinary field that converges in specialist professionals, domain expertise, data modelling expertise, statistical expertise and computer science.
The origins of business analytics and implications for the information systems field
  • N. Hassan
  • Computer Science
    Journal of Business Analytics
  • 2019
This essay examines the implications of the complex origins of data analytics and data science for the IS field, specifically on how those different discourses impact future research and practice.
The Big Data Agenda : Data Ethics and Critical Data Studies
This book highlights that the capacity for gathering, analysing, and utilising vast amounts of digital (user) data raises significant ethical issues. Annika Richterich provides a systematic
Data Science Challenges to Improve Quality Assurance of Internet of Things Applications
This paper presents data science challenges to improve the quality assurance of Internet of Things applications sub-divided into four categories (Defect prevention, Defect analysis, User incorporation and Organizational) derived from the six quality assurance requirement categories.
Towards Data Science
What differentiates data science from the established sciences, data technologies, and big data is discussed and the goal is to encourage data related researchers to transfer their focus towards this new science.
Describing data patterns: a general deconstruction of metadata standards
The study revealed five basic paradigms that deeply shape how data is structured and described in practice, which can help to better understand data and its actual forms, both for consumption and creation of data.