Machine reasoning about anomalous sensor data
Matt Calder, Robert Morris, Francesco Peri
Last modified: 2008-09-13
Our group has developed a semantic data validation tool that is capable of observing incoming real-time sensor data and performing reasoning against a set of rules specific to the scientific domain to which the data belongs. Our software solution can produce a variety of different outcomes when a data anomaly or unexpected event is detected, ranging from simple flagging of data points, to data augmentation, to validation of proposed hypotheses that could explain the phenomenon. Hosted on the Jena Semantic Web Framework, the tool is completely domain-agnostic and is made domain-aware by reference to a Knowledge Base (KB) that describes the key resources of the system being observed. The KB comprises ontologies for the sensor packages and for the domain; historical data from the network; concepts designed to guide discovery of internet resources unavailable in the local KB but relevant to reasoning about the anomaly; and a set of rules that represent domain expert knowledge of constraints on data from different kinds of instruments as well as rules that relate types of ecosystem events to properties of the ecosystem. We describe our ontology, some rules describing coastal storm consequences, and how we relate our data to external resources. We describe in some detail how a specific actual event---an unusually high chlorophyll reading---can be deduced by machine reasoning to be consistent with a hypothesis of benthic diatom resuspension and inconsistent with a hypothesis of an algal bloom, both of which might otherwise have been potential explanations.