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An efficient management of multiversion data with branched evolution is crucial for many applications. It requires database designers aware of tradeoffs among index structures and policies. This paper defines a framework and an analysis method for understanding the behavior of different indexing policies. Given data and query characteristics the analysis allows determining the most suitable index structure. The analysis is validated by an experimental study.

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This article presents a generalized metric distance, called ∆-distance, between images represented by a tree structure resulting from a recursive image partition. This distance is used to perform content-based image retrieval queries in databases. ∆-distance allows to retrieve images globally similar to a query image. This distance takes into account the location of the image visual features. It can be performed using a multi-level filtering algorithm. Moreover, ∆-distance allows region-based queries. In this case, the resulting images contain quadrants similar to the quadrants selected by the user in the query image or contain quadrants similar to the entire query image. Because it is a generalized distance function, some particular cases of the ∆-distance appear in existing content-based image retrieval systems.

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