image: Big Data, published quarterly online with open access options and in print, facilitates and supports the efforts of researchers, analysts, statisticians, business leaders, and policymakers to improve operations, profitability, and communications within their organizations. Spanning a broad array of disciplines focusing on novel big data technologies, policies, and innovations, the Journal brings together the community to address the challenges and discover new breakthroughs and trends living within this information. Complete tables of content and a sample issue may be viewed on the Big Data website. view more
Credit: ©Mary Ann Liebert, Inc., publishers
New Rochelle, February 19, 2016--The explosive interest in wearable personal tracking devices is generating huge amounts of so-called "quantified self" (QS) data, just waiting to be analyzed and used to improve human health. One solution for turning QS data into actionable information and insights that can guide users' decision-making is described in a new study published in Big Data, the highly innovative, peer-reviewed journal from Mary Ann Liebert, Inc., publishers. The article is available to download free on the Big Data website until March 31, 2016.
In "Mining the Quantified Self: Personal Knowledge Discovery as a Challenge for Data Science", Tom Fawcett, Silicon Valley Data Science, Mountain View, CA, provides an overview of the emerging QS movement and the opportunities and challenges it presents for the field of big data. He identifies several key trends: the increasing variety and aggregation of QS data being collected, and rising user expectations for more actionable insights, which will require more analytical capabilities.
The author presents his views on what data science can contribute to the QS movement, and proposes using data mining tools to search for patterns that may, for example, be able to identify mild food allergies and suggest experiments a user could perform to test these predictions. Patterns identified in the data may also reveal ways to improve users' exercise performance or diet.
"Tom Fawcett's paper highlights the benefits for individuals based on their own data without the usual risks around data privacy and misuse," says Big Data Editor-in-Chief Vasant Dhar, Professor at the Stern School of Business, New York University. "The paper demonstrates some compelling examples of the use of data for personal well being."
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About the Journal
Big Data, published quarterly online with open access options and in print, facilitates and supports the efforts of researchers, analysts, statisticians, business leaders, and policymakers to improve operations, profitability, and communications within their organizations. Spanning a broad array of disciplines focusing on novel big data technologies, policies, and innovations, the Journal brings together the community to address the challenges and discover new breakthroughs and trends living within this information. Complete tables of content and a sample issue may be viewed on the Big Data website.
About the Publisher
Mary Ann Liebert, Inc., publishers is a privately held, fully integrated media company known for establishing authoritative medical and biomedical peer-reviewed journals, including OMICS: A Journal of Integrative Biology, Journal of Computational Biology, New Space, and 3D Printing and Additive Manufacturing. Its biotechnology trade magazine, GEN (Genetic Engineering & Biotechnology News), was the first in its field and is today the industry's most widely read publication worldwide. A complete list of the firm's more than 80 journals, newsmagazines, and books is available on the Mary Ann Liebert, Inc., publishers website.
Journal
Big Data