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Big Data

Release Date: 2020/11/01
ISBN: 9781786305558
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Manipulating and processing masses of digital data is never a purely technical activity. It requires an interpretative and exploratory outlook – already well known in the social sciences and the humanities – to convey intelligible results from data analysis algorithms and create new knowledge.

Big Data is based on an inquiry of several years within Proxem, a software publisher specializing in big data processing. The book examines how data scientists explore, interpret and visualize our digital traces to make sense of them, and to produce new knowledge. Grounded in epistemology and science and technology studies, Big Data offers a reflection on data in general, and on how they help us to better understand reality and decide on our daily actions.

Table of Contents

  1. Cover
  2. Title Page
  3. Copyright Page
  4. Introduction
  5. 1 From Trace to Web Data: An Ontology of the Digital Footprint
    1. 1.1. The epistemology of the cultural sciences
    2. 1.2. The footprint in evidential sciences
    3. 1.3. The log or activity history
    4. 1.4. The digital footprint as a web log
    5. 1.5. The intentionality of digital footprints
    6. 1.6. Data as theoretically-loaded footprints
  6. 2 Toward an Epistemic Continuity Anchored in the Cultural Sciences
    1. 2.1. Digital technology in the cultural sciences
    2. 2.2. Field and corpus: two modes of access to reality
    3. 2.3. Virtual methods, a reconstruction of access to the field
    4. 2.4. The challenges of the technical revolution of the text
    5. 2.5. From the web as an object to the web as a corpus
    6. 2.6. Conclusion
  7. 3 The Status of Computation in Data Sciences
    1. 3.1. Making data computable
    2. 3.2. The field of computability
    3. 3.3. Computational thinking
    4. 3.4. Computation in the natural sciences
    5. 3.5. From exploratory analysis to data mining
    6. 3.6. The institutional and theoretical melting pot of data science
    7. 3.7. The contribution of artificial intelligence
    8. 3.8. Conclusion
  8. 4 A Practical Big Data Use Case
    1. 4.1. Presentation of the case study
    2. 4.2. Customer experience and coding of feedback
    3. 4.3. From the representative approach to the “big data” project
    4. 4.4. Data preparation
    5. 4.5. Design of the coding plan
    6. 4.6. The constitution of linguistic resources
    7. 4.7. Constituting the coding plan
    8. 4.8. Visibility of the language activity
    9. 4.9. Storytelling and interpretation of the data
    10. 4.10. Conclusion
  9. 5 From Narratives to Systems: How to Shape and Share Data Analysis
    1. 5.1. Two epistemic configurations
    2. 5.2. The genesis of systems
    3. 5.3. Conclusion
  10. 6 The Art of Data Visualization
    1. 6.1. Graphic semiology
    2. 6.2. Data cartography
    3. 6.3. Representation as evidence
    4. 6.4. The visual language of design in system configuration
    5. 6.5. Materialization and interpretation of recommendations
  11. 7 Knowledge and Decision
    1. 7.1. Big data, a pragmatic epistemology?
    2. 7.2. Toward gradual validity of knowledge
    3. 7.3. Deciding, knowing and measuring
  12. Conclusion
  13. References
  14. Index
  15. Other titles from ISTE in Information Systems, Web and Pervasive Computing
  16. End User License Agreement
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