In the field of e-science stream data processing is common place facilitating sensor networks, in particular for prediction and supporting decision making. However, sensor data may be erroneous, like e.g. due to measurement errors (outliers) or changes of the environment. While it can be foreseen that there will be outliers, there are a lot of environmental changes which are not foreseen by scientists and therefore are not considered in the data processing. However, these unforeseen semantic changes - represented as annotations - have to be propagated through the processing. Since the annotations represent an unforeseen, hence un-understandable, annotation, the propagation has to be independent of the annotation semantics. It nevertheless has to preserve the significance of the annotation on the data despite structural and temporal transformations. And should remain meaningful for a user at the end of the data processing. In this paper, we identify the relevant research questions.In particular, the propagation of annotations is based on structural, temporal, and significance contribution. While the consumption of the annotation by the user is focusing on clustering information to ease accessibility.
|Workshop||3rd Workshop on Ph.D. Students in Information and Knowledge Management, PIKM 2010|
|Period||30/10/10 → 30/10/10|
|Other||30 Oct 2010|
- stream data processing