Abstract
We argue that a key requirement on an information system that can implement an active conceptual model of learning from surprises is the ability to query data that is not query-able by content, especially data streams;we suggest that such data be queried by context. We propose an enhancement of entity-relationship modeling with active constructs in order to permit such streams to have context-based relationships with standard data. We propose a framework wherein the analysis of surprises and the subsequent monitoring of states that are ripe for such events are possible by the use of such contexts.
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Mazumdar, S. (2007). Accommodating Streams to Support Active Conceptual Modeling of Learning from Surprises. In: Chen, P.P., Wong, L.Y. (eds) Active Conceptual Modeling of Learning. ACM-L 2006. Lecture Notes in Computer Science, vol 4512. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77503-4_13
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DOI: https://doi.org/10.1007/978-3-540-77503-4_13
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