Accommodating streams to support active conceptual modeling of learning from surprises

Authors:
Subhasish Mazumdar
Affiliations:
Department of Computer Science, New Mexico Institute of Mining and Technology, Socorro, NM
Venue:
Active conceptual modeling of learning
Year:
2007

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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 where in the analysis of surprises and the subsequent monitoring of states that are ripe for such events are possible by the use of such contexts.