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Authors: Rasmus Rosenqvist Petersen 1 ; Adriana Lukas 2 and Uffe Kock Wiil 3

Affiliations: 1 NOBLACKBOX Cambridge, United Kingdom ; 2 London Quantified Self, United Kingdom ; 3 Parient@home, Denmark

ISBN: 978-989-758-114-4

Keyword(s): Quantified Self, Self Tracking, Self Hacking, Data Aggregator, Explorative Analysis, Computational Analysis, Hypertext.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Business Analytics ; Cardiovascular Technologies ; Computing and Telecommunications in Cardiology ; Data Communication Networking ; Data Engineering ; Decision Support Systems ; Decision Support Systems, Remote Data Analysis ; Enterprise Software Technologies ; Health Engineering and Technology Applications ; Intelligent Problem Solving ; Knowledge Engineering and Ontology Development ; Knowledge-Based Systems ; Performance Evaluation ; Software Engineering ; Software Project Management ; Symbolic Systems ; Telecommunications

Abstract: Quantified Self is a growing community of individuals seeking self-improvement through self-measurement. Initially, personal variables such as diet, exercise, sleep, and productivity are tracked. This data is then explored for correlations, to ultimately either change negative or confirm positive behavioural patterns. Tools and applications that can handle these tasks exist, but they mostly focus on specific domains such as diet and exercise. These targeted tools implement a black box approach to data ingestion and computational analysis, thereby reducing the level of trust in the information reported. We present QS Mapper, a novel tool, that allows users to create two-way mappings between their tracked data and the data model. It is demonstrated how drag and drop data ingestion, interactive explorative analysis, and customisation of computational analysis procures more individual insights when testing Quantified Self hypotheses.

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Paper citation in several formats:
Petersen, R.; Lukas, A. and Wiil, U. (2015). QS Mapper: A Transparent Data Aggregator for the Quantified Self - Freedom from Particularity Using Two-way Mappings.In Proceedings of the 10th International Conference on Software Engineering and Applications - Volume 1: ICSOFT-EA, (ICSOFT 2015) ISBN 978-989-758-114-4, pages 65-72. DOI: 10.5220/0005553800650072

@conference{icsoft-ea15,
author={Rasmus Rosenqvist Petersen. and Adriana Lukas. and Uffe Kock Wiil.},
title={QS Mapper: A Transparent Data Aggregator for the Quantified Self - Freedom from Particularity Using Two-way Mappings},
booktitle={Proceedings of the 10th International Conference on Software Engineering and Applications - Volume 1: ICSOFT-EA, (ICSOFT 2015)},
year={2015},
pages={65-72},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005553800650072},
isbn={978-989-758-114-4},
}

TY - CONF

JO - Proceedings of the 10th International Conference on Software Engineering and Applications - Volume 1: ICSOFT-EA, (ICSOFT 2015)
TI - QS Mapper: A Transparent Data Aggregator for the Quantified Self - Freedom from Particularity Using Two-way Mappings
SN - 978-989-758-114-4
AU - Petersen, R.
AU - Lukas, A.
AU - Wiil, U.
PY - 2015
SP - 65
EP - 72
DO - 10.5220/0005553800650072

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