Abstract
A proactive context-aware system automatically adapts its user interface to the user’s situational needs. This is achieved by continuously capturing the environmental properties, reasoning upon the context, and detecting situations where unsolicited adjustments are helpful or notifications informative. If the characteristics of those situations are well known in advance, their occurrence can be detected at runtime by the rule-based processing of raw sensor data. However, rule-based context reasoning methods determine the user’s situation mostly based on present sensor signals instead of considering the situation to be likewise the product of the past context. This article introduces a graph-based situation modeling formalism for the specification of system-relevant environmental circumstances as context flow graphs. A directed cyclic graph represents thereby the distinct contextual characteristics a user’s situation is made of and the temporal order in which these appear and disappear during the evolution of the situation. Complex situations for rule-based proactive context-aware systems can then be expressed at a high level of abstraction and without the need to understand the underlying sensor-related signal processing mechanisms. The technical feasibility is demonstrated by a prototypical distributed proactive context-aware middleware that, in addition, comes up with a web-based user interface for the interactive graphical and logical modeling of situations as context flow graphs.
Highlights
W ITH his seminal thoughts about a future full of ubiquitous computing devices and services [1], Mark Weiser contributed to the success of an application design paradigm that is today known as context-aware computing [2]
In a proactive context-aware system, this inference process can be set up either by hardcoding it, by allowing a user to customize it, or by letting the system learn the correlations between raw sensor signals and relevant high-level situations based on the usage history
Despite known challenges such as the detection of contradicting rules and the need to formalize the domain at design time [14], rule-based context reasoning became one of the major non-hard-wired contextaware computing techniques for ambient intelligence environments, with a multitude of approaches and applications introduced over the past decades [9]
Summary
W ITH his seminal thoughts about a future full of ubiquitous computing devices and services [1], Mark Weiser contributed to the success of an application design paradigm that is today known as context-aware computing [2]. Depending on the rule modeler’s expert level, the complexity of the application’s context and desired level of freedom, the context abstraction rules and their associated actions are defined through the use of sophisticated graphical or textual rule editors [13], with varying simplicity and power Despite known challenges such as the detection of contradicting rules and the need to formalize the domain at design time [14], rule-based context reasoning became one of the major non-hard-wired contextaware computing techniques for ambient intelligence environments, with a multitude of approaches and applications introduced over the past decades [9]. It’s intended purpose is to give domain experts a flexible and powerful way to encode domain-specific complex environmental circumstances - for the configuration of RPCAS’s - at a high level of abstraction and without the need to understand the underlying detection logic This approach to situation modeling is based on the fundamental assumption that situations may take time to evolve before they occur.
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