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Table 1 Definitions for types of contextual attributes

From: Contextual anomaly detection framework for big sensor data




The records in the dataset include features which identify locational information for the record. For example, a sensor reading may have spatial attributes for the city, province, and country the sensor is located in; it could also include finer-grained information about the sensors location within a building, such as floor, room, and building number.


The records are related to other records as per some graph structure. The graph structure then defines a spatial neighbourhood whereby these relationships can be considered as contextual indicators.


The records can be considered as a sequence within one another. That is, there is meaning in defining a set of records that are positioned one after another. For example, this is extremely prevalent in time-series data whereby the records are timestamped and can thus be positioned relative to each other based on time readings.


The records can be clustered within profiles that may not have explicit temporal or spatial contextualities. This is common in anomaly detection systems where, for example, a company defines profiles for their users; should a new record violate the existing user profile, that record is declared anomalous.