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Quantified Self Ideology:  Personal Data becomes Big Data Slide 1 Quantified Self Ideology:  Personal Data becomes Big Data Slide 2 Quantified Self Ideology:  Personal Data becomes Big Data Slide 3 Quantified Self Ideology:  Personal Data becomes Big Data Slide 4 Quantified Self Ideology:  Personal Data becomes Big Data Slide 5 Quantified Self Ideology:  Personal Data becomes Big Data Slide 6 Quantified Self Ideology:  Personal Data becomes Big Data Slide 7 Quantified Self Ideology:  Personal Data becomes Big Data Slide 8 Quantified Self Ideology:  Personal Data becomes Big Data Slide 9 Quantified Self Ideology:  Personal Data becomes Big Data Slide 10 Quantified Self Ideology:  Personal Data becomes Big Data Slide 11 Quantified Self Ideology:  Personal Data becomes Big Data Slide 12 Quantified Self Ideology:  Personal Data becomes Big Data Slide 13 Quantified Self Ideology:  Personal Data becomes Big Data Slide 14 Quantified Self Ideology:  Personal Data becomes Big Data Slide 15 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Quantified Self Ideology: Personal Data becomes Big Data

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A key contemporary trend emerging in big data science is the quantified self: individuals engaged in the deliberate self-tracking of any kind of biological, physical, behavioral, or transactional information, as n=1 individuals or in groups. The quantified self is one dimension of the bigger trend to integrate and apply a variety of personal information streams including big health data (genome, transcriptome, environmentome, diseasome), quantified self data streams (biosensor, fitness, sleep, food, mood, heart rate, glucose tracking, etc.), traditional data streams (personal and family health history, prescription history) and IOT (Internet of things) activity data streams (smart home, smart car, environmental sensors, community data). This talk looks at how personal data and group data are becoming big data as individuals and communities share, collaborate, and work with large personalized data sets using novel discovery methods such as anomaly detection and exception reporting, longitudinal baseline analysis, episodic triggers, and hierarchical machine learning.

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A key contemporary trend emerging in big data science is the quantified self: individuals engaged in the deliberate self-tracking of any kind of biological, physical, behavioral, or transactional information, as n=1 individuals or in groups. The quantified self is one dimension of the bigger trend to integrate and apply a variety of personal information streams including big health data (genome, transcriptome, environmentome, diseasome), quantified self data streams (biosensor, fitness, sleep, food, mood, heart rate, glucose tracking, etc.), traditional data streams (personal and family health history, prescription history) and IOT (Internet of things) activity data streams (smart home, smart car, environmental sensors, community data). This talk looks at how personal data and group data are becoming big data as individuals and communities share, collaborate, and work with large personalized data sets using novel discovery methods such as anomaly detection and exception reporting, longitudinal baseline analysis, episodic triggers, and hierarchical machine learning.

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