Cognition, computing, and cooperation
Cognition, computing, and cooperation
A co-operative computer based on the principles of human co-operation
International Journal of Man-Machine Studies - Special issue on knowledge-based co-operation
A framework for knowledge-based temporal abstraction
Artificial Intelligence
Principles of mixed-initiative user interfaces
Proceedings of the SIGCHI conference on Human Factors in Computing Systems
Sketching a graph to query a time-series database
CHI '01 Extended Abstracts on Human Factors in Computing Systems
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Feature Selection Algorithms: A Survey and Experimental Evaluation
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
BinX: Dynamic Exploration of Time Series Datasets Across Aggregation Levels
INFOVIS '04 Proceedings of the IEEE Symposium on Information Visualization
CGIV '05 Proceedings of the International Conference on Computer Graphics, Imaging and Visualization
Temporal reasoning for decision support in medicine
Artificial Intelligence in Medicine
Artificial Intelligence in Medicine
Experiencing SAX: a novel symbolic representation of time series
Data Mining and Knowledge Discovery
VizTree: a tool for visually mining and monitoring massive time series databases
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Guest editorial: Knowledge-based data analysis and interpretation
Artificial Intelligence in Medicine
Representing unevenly-spaced time series data for visualization and interactive exploration
INTERACT'05 Proceedings of the 2005 IFIP TC13 international conference on Human-Computer Interaction
Learning rules from multisource data for cardiac monitoring
AIME'05 Proceedings of the 10th conference on Artificial Intelligence in Medicine
Language games for meaning negotiation between human and computer agents
ESAW'05 Proceedings of the 6th international conference on Engineering Societies in the Agents World
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Guest Editorial: Intelligent data analysis in biomedicine
Journal of Biomedical Informatics
Intelligent adaptive monitoring for cardiac surveillance
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
A data mining framework for time series estimation
Journal of Biomedical Informatics
Approximate variable-length time series motif discovery using grammar inference
Proceedings of the Tenth International Workshop on Multimedia Data Mining
Parallel exact time series motif discovery
Euro-Par'10 Proceedings of the 16th international Euro-Par conference on Parallel processing: Part II
A disk-aware algorithm for time series motif discovery
Data Mining and Knowledge Discovery
Learning actions in complex software systems
DaWaK'11 Proceedings of the 13th international conference on Data warehousing and knowledge discovery
HIS'12 Proceedings of the First international conference on Health Information Science
User-centered visual analysis using a hybrid reasoning architecture for intensive care units
Decision Support Systems
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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 annotations. Three 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.