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1818 results in Knowledge Management, Databases and Data Mining

Page 22 of 91

  • Chapter 1 - Foundations of Data Science

  • from Part I - Data Science
  • Alfred Z. Spector, Massachusetts Institute of Technology, Peter Norvig, Stanford University, California, Chris Wiggins, Columbia University, New York, Jeannette M. Wing, Columbia University, New York
  • Book:
    Data Science in Context
    Published online:
    29 September 2022
    Print publication:
    20 October 2022, pp 7-28
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  • Summary

    This chapter first defines data science, its primary objectives, and several related terms. It continues by describing the evolution of data science from the fields of statistics, operations research, and computing. The chapter concludes with historical notes on the emergence of data science and related topics.

Data Science in Context
  • Foundations, Challenges, Opportunities
  • Alfred Z. Spector, Peter Norvig, Chris Wiggins, Jeannette M. Wing
  • Published online:
    29 September 2022
    Print publication:
    20 October 2022
  • Data science is the foundation of our modern world. It underlies applications used by billions of people every day, providing new tools, forms of entertainment, economic growth, and potential solutions to difficult, complex problems. These opportunities come with significant societal consequences, raising fundamental questions about issues such as data quality, fairness, privacy, and causation. In this book, four leading experts convey the excitement and promise of data science and examine the major challenges in gaining its benefits and mitigating its harms. They offer frameworks for critically evaluating the ingredients and the ethical considerations needed to apply data science productively, illustrated by extensive application examples. The authors' far-ranging exploration of these complex issues will stimulate data science practitioners and students, as well as humanists, social scientists, scientists, and policy makers, to study and debate how data science can be used more effectively and more ethically to better our world.

The Flexibility Paradox
  • Why Flexible Working Leads to (Self-)Exploitation
  • Heejung Chung
  • Published by:
    Bristol University Press
    Published online:
    15 September 2022
    Print publication:
    04 March 2022
  • Throughout the COVID-19 pandemic, flexible working has become the norm for many workers. This volume offers an original examination of flexible working using data from 30 European countries and drawing on studies conducted in Australia, the US and India. Rather than providing a better work-life balance, the book reveals how flexible working can lead to exploitation, which manifests differently for women and men, such as more care responsibilities or increased working hours.

Data Analytics for Cybersecurity
  • Vandana P. Janeja
  • Published online:
    10 August 2022
    Print publication:
    21 July 2022
  • As the world becomes increasingly connected, it is also more exposed to a myriad of cyber threats. We need to use multiple types of tools and techniques to learn and understand the evolving threat landscape. Data is a common thread linking various types of devices and end users. Analyzing data across different segments of cybersecurity domains, particularly data generated during cyber-attacks, can help us understand threats better, prevent future cyber-attacks, and provide insights into the evolving cyber threat landscape. This book takes a data oriented approach to studying cyber threats, showing in depth how traditional methods such as anomaly detection can be extended using data analytics and also applies data analytics to non-traditional views of cybersecurity, such as multi domain analysis, time series and spatial data analysis, and human-centered cybersecurity.

  • 3 - Introduction to Data Mining

  • Vandana P. Janeja, University of Maryland, Baltimore County
  • Book:
    Data Analytics for Cybersecurity
    Published online:
    10 August 2022
    Print publication:
    21 July 2022, pp 29-59

  • Summary

    This chapter gets into the techniques of data analytics, focusing on the three pillars of data mining, namely clustering, classification, and association rule mining, and how each can be used for cybersecurity. This chapter can be seen as a crash course in data mining. It begins with an understanding of the overall knowledge discovery and data mining process models and follows the elements of the data life cycle. This chapter outlines foundational elements such as measures of similarity and measures of evaluation. It outlines the landscape of various algorithms in clustering, classification, and frequent and rare patterns.

Page 22 of 91