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Some Perspectives on Change Management: From Basic Primitive Tools to Context-Aware Applicative Components

Using knowledge about data organization allows for a finer tracking, more relevant analysis and more significant change reporting. Therefore, change management is called to evolve toward a stronger exploitation of structural semantics by algorithms. However, to reach its full potential, we claim that change management has to address an even more challenging issue: understanding and capturing the environment in which the changes take place. This will enable us to go beyond data-centric algorithms having narrow applicability towards context-sensitive algorithms having a much broader scope. To achieve this, we will need to define, model and exploit richer contextual semantics. A key dimension of this contextual semantics is time itself which, obviously, is consubstantial to the notion of change, but typically ignored by data-centric algorithms. Another central issue to be addressed is the design of suitable models encompassing structural as well as behavioral constraints.