Knowledge construction from time series data using a collaborative exploration system

This paper deals with the exploration of biomedical multivariate time series to construct typical parameter evolution or scenarios. This task is known to be difficult: the temporal and multivariate nature of the data at hand and the context-sensitive aspect of data interpretation hamper the formulation of a priori knowledge about the kind of patterns that can be detected as well as their interrelations. This paper proposes a new way to tackle this problem based on a human–computer collaborative approach involving specific annotahree grounding principles, namely autonomy, adaptability and emergence, support the co-construction of successive abstraction levels for data interpretation. An agent-based design is proposed to support these principles. Preliminary results in a clinical context are presented to support our proposal. A comparison with two well-known time series exploration tools is furthermore performed. Ó 2007 Elsevier Inc. All rights reserved.

• Type: Journal
• Year: 2007
• Where: JBI
• Authors: Thomas Guyet, Catherine Garbay, Michel Dojat
• Keywords: Biomedical Multivariate Time, JBI 2004, JBI 2007, Time Series, Typical Parameter Evolution